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Transit Service Contracting Dec 1988
Click HERE for graphic. Transit Service Contracting Cream-Skimming or Deficit-Skimming? December 1988 Prepared by Robert Cervero Institute of Urban and Regional Development University of California, Berkeley Berkeley, California 94720 Prepared for University Research and Training Program Urban Mass Transportation Administration U.S. Department of Transportation Washington, D.C. 20590 Distributed in Cooperation with Technology Sharing Program U.S. Department of Transportation Washington, D.C. 20590 DOT-T-89-13 Executive Summary Transit Service Contracting,. Cream-Skimming or Deficit Skimming? Robert Cervero University of California, Berkeley December 1988 The overall fiscal decline in America's mass transit industry, some argue, is endemic of a public sector that lacks any incentive to cut costs, innovate, and respond to changing patterns of demand. More and more, transit authorities around the country are attempting to inject greater competition into their transit systems by contracting out bus routes to private firms. Recent experiences suggest that competitive contracting can not only contain runaway cost increases but capture new ridership markets as well. Standing in the way of any widespread acceptance of competitive contracting, however, is the commonly voiced argument that private companies will skim the cream -- generally considered to be rush hour markets -- away from public entities. This compels one to ask first whether there are many profitable public -transit services around the country from which to skim any cream? Or are private contractors skimmers of deficits who turn losses into profits? The aim of this research is to help resolve this controversy using a balance of empirical analysis and qualitative.case analyses. The cream-skimming argument, we believe, rests on several questionable assumptions about urban transit's costs and performance characteristics. First and foremost is the belief that there are significant numbers of publicly operated routes and services across the country that are generating a profit. A second tenable assumption is that America's public transit industry is a natural monopoly - that is, there are inherent economies of scale and consequently lower unit costs that can be realized only by allowing a single exclusive franchiser to exist. The final assumption is that public transit authorities will somehow lose control over their services and become vulnerable to predatory competitors who "steal them away". Our belief is that these assumptions are groundless. This study addresses the cream-skimming topic only with respect to fixed-route bus transit services, the bread-and-butter of the transit industry. Particular attention is given to studying the cost and performance characteristics of peak-hour, express services because many transit managers view them as their "cream", ostensibly because buses are generally full and workers are at their busiest during the rush period. Context or Transit Service Contracting in the U.S. Triggering the national interest in competitive contracting have been the dramatic declines in the transit industry's fiscal health over the past decade. The cost of operating the nation's public transit systems increased more than threefold between 1975 and 1985, even though ridership remained more or less constant over that period. During the first half of the 1980's, the fastest real dollar cost increases were for fringe benefits, materials and supplies, utilities, and insurance. Virtually every indicator of productivity has declined as well. Between 1975 and 1985, for instance, transit labor productivity dropped sharply from 13,618 vehicle miles of service per worker to 9,364. Competitive contracting has been touted by many advocates as the most promising way of reversing these declines. In theory, two levels of benefits should redound to public agencies i engaged in competitive contracting. The immediate benefits, called first-order benefits, should result from substituting lower cost privately operated services for comparable publicly operated ones. By spurring competition, contracting should also produce second- order benefits by prodding public entities to get their own fiscal houses in order so that they can win back services. As discussed in Chapter 2 of the report, a number of empirical studies have confirmed these benefits. Today, an estimated one-third of all public agencies which sponsor transit services are engaged in some form of transit service contracting. While the majority of service contracting is for demand-responsive transit, over 20 percent of fixed-route systems nationwide also contract for fixed-route services, p y peak-only express runs. Only five systems with fleets sizes exceeding 100 contract out all of their fixed-route services. There are only two precedents, moreover, where a pre-existing publicly operated fixed-route service has been converted over to private operation. Of the studies conducted to date on the cost and performance impacts of transit service contracting, most have found real cost savings m the range of 10 to 50 percent. Since few large transit properties actually engage in service contracting on a significant scale, the aggregate nationwide cost savings from contracting have fairly modest, well under 10 percent. Profitability of Public Transit Operations in the U.S. Since the cream-s g argument rests on the belief that there are profitable publicly operated bus routes in the U.S., a major component of this research involved investigating whether this is so. To date, little research has been conducted on this question. In this study, profitability is measured in terms of the degree to which farebox revenues exceed the direct, day-to-day operating expenses that are not sunk and are avoidable should services be contracted out. Direct operating expenses include labor, energy, materials costs for operating and maintaining vehicles as well as use-related depreciation for any vehicles used in providing services. All outlays for fixed facilities like maintenance garages as well as all administrative functions are ignored because such costs could not be instantaneously eliminated if a transit agency contracted out a specific set of services to a private vendor. For the empirical phase of the research, detailed 1987 cost and revenue data were compiled for 76 of the highest ridership peak-only, express routes and 76 of the highest ridership all-day local routes for 25 transit properties with over 100 vehicles. Cost models were developed for each property using vehicle hours, vehicle miles, and peak vehicles as input factors. A set of allocation rules were established based on recent research on causal factors related to the encumbrance of expenses and uniformly applied to all transit systems studied. In all, only ten transit routes were found to either cover or exceed their direct, day-to-day operating costs from farebox receipts. These profitable or break-even routes were operated by five of the 25 agencies -- WMATA (Washington, D.C. area), SEPTA (Philadelphia area), GRT (Richmond area), MTC (Minneapolis-St. Paul area), and Westchester County (NY), with the latter being a wholly private franchised operation. In all, these routes comprised less than one percent, or 0.0076, of total routes operated by the 25 case study sites. Thus even when the most conservative approach to estimating costs is adopted, fewer than one percent of the nation's fixed-route transit services are estimated to make a profit or break even. With few exceptions, profitable publicly operated routes shared common demand-side characteristics. Nearly all serve highly transit-dependent populations, as evidenced by the low median incomes and high shares of minority residents along the neighborhoods they Thisect. Population and employment densities are consistently high along profitable corridors, anywhere between 30 percent and 100 percent above the net densities of the principal city served by the transit system. Equally important, profitable routes usually connect the region's central business ii district, serving a number of employment clusters and major activity centers along the way. With the exception of two peak- express routes that were found to be profitable, average trip lengths tend to be quite short along profitable routes, generally in the range of 2 to 4 miles. Such high rates of seat turnover produce high revenue yields, especially under flat fare systems. Given that the poor make up a large share of the patrons on these routes, then, these pricing policies are inherently regressive. Finally, all successful routes average high load factors, generally well over 1.2 during the peak period and in the range of 0.70 to 0.90 during most of the mid-day. Overall, profitable routes have none of the characteristics of the kinds of services that would be most subject to contracting. No cases exist where transit managers have selectively contracted out routes with high load factors serving short-haul trips along dense corridors with transit-dependent households. Rather, competitive contracting of fixed-route transit services, as practiced to date, has been limited primarily to new, start-up services targeted at low-density suburban markets. Thus, the gap between the cream-skimming argument and the reality of contracting is fairly wide. The Scale Economies Argument Against Contracting A classical argument against privatization is that public transit properties enjoy economies of scale that can only be reaped by limiting the number of entrants into the urban mass transportation market. Research seems to discredit this argument. For the most part, economies in the bus transit sector can only be found when outputs are expressed in terms of patronage. When additional demand requires the start-up of new services or the expansion of existing ones, extra equipment has to be purchased, the work force has to be expanded, and additional mileage and hours- of service have to be logged. As a result, this and other research unequivocally show that unit costs of delivering bus services rise when vehicle-miles are increased, particularly in the case of large transit properties. In this study, for instance, it was found that for both peak and local services, the average cost per vehicle mile rises from $1.48 for transit properties with fewer than 250 vehicles to over twice as much -- $3.79 -- for transit systems with more than 500 vehicles. This tendency toward declining scale economies thus appears to hold even when patronage levels are controlled for (since the analysis is based on only high ridership routes). Besides paying higher penalties for split-shift and part- time work duties, bigger transit operators also appear to pay a penalty for the slower speeds and stop-and-go conditions they endure -- generally in the way of higher maintenance costs, fuel consumption rates, liability insurance rates, and driver wage scales. This and other research clearly point to the fact that the peak period is urban transit's nemesis, sustaining far greater deficit rates than the midday. Besides shedding expensive peak loads from public transit agencies, competitive contracting could provide many commuters with a higher quality service than that offered by conventional buses. While standard buses often sit idle during the midday and on weekends, the premium buses operated by private firms can be put to use as special charters to recreational sites during the midday. To the extent paratransit serves a peak- period supplements, these vehicles could also provide more specialized curb-to-curb services for elderly and disabled populations during the non-peak. Thus, load-shedding would not only help to reduce deficits, it would also likely enhance the quality of off-peak travel for many groups. Challenging Other Arguments Against Competitive Contracting Contracting is sometimes misconstrued to mean public involvement in the transit arena is somehow lost. To the contrary, public authorities remain the financial sponsors of contracted services. The actually delivery of services, however, is performed by the lowest bidder who meets acceptable performance standards. For six of the nation's major transit jurisdictions currently involved with contracting fixed-route services on a significant scale, all were found to be sponsors iii of the contracted services. They also designed all contracted routes, set headways and schedules, and determined what fares would be charged. In all cases, contracts could be rescinded for failure to comply with the terms and conditions of the contract. Overall, the oversight of all aspects of transit services rests with the public entity undertaking contracting. As long as this is so, every safeguard is in place to prevent cream-skimming. Very simply, public entities govern what is contracted and what isn't, allowing them to save their best performing services for in-house operation. For cream-skimming to occur, it is of course necessary that a pre-existing publicly operated fixed-route service be transferred to private operation. As practiced to date, nearly every fixed- route service that has been bid out was a new, supplemental service. Of the two cases where a already-existing public bus route was taken over by a private firm in both instances the routes were among the agencies' (Houston Metro and Norfolk's TRT) poorest performing routes. Clearly, then, as practiced to date, competitive contracting of pre-established bus services has involved deficit-skimming rather than cream-skimming. Other criticisms lodged against competitive contracting were also investigated in this research. Some contend that private operators will deliver lower quality of service than public operators. Explicit performance standards related to on-time arrivals, safety, maintenance, and the like have prevented this. In the case of Houston Metro, evidence suggests that general service quality has actually improved following private takeover of park-and-ride express routes. Others perceived service contracting as a threat to organized labor and a de facto violation of UMTA's Section 13(c) labor protection legislation. To date, however, there is no single case of a transit employee having been furloughed directly as a result of service contracting. Still others contend that as contracting becomes more widespread, the overheads and labor costs of private firm will eventually rise as their workers begin to unionize. In general, periodic re-bidding of contracts have provided an effective safety valve for controlling costs. Moreover, rather than private sector costs rising, in some cases the public sector's costs fell in order to become more competitive (which in the case of two properties meant eventually winning back services from private firms. Conclusions The three main findings of this research which render the cream-s g argument meaningless are: (1) There are very, very few profitable fixed-route bus services in the U.S. from which any "cream" could possibly be skimmed. Even when only the direct operating portions of total costs are considered, less than one percent of all fixed-route bus services currently operated by medium and large size transit agencies in the U.S. make a profit or break even. (2) There is little evidence of any significant economies of scale in the transit industry, particularly for large transit agencies, meaning there is no real economic justification protecting transit properties from competition. This and other research show consistently that unit costs of delivering bus services rise when vehicle miles increase. Thus, private, firms that assist in serving high-deficit peak loads should help reduce the scale of public operations to a more cost efficient level. (3) In all instances to date, public agencies control which routes private bidders are given an opportunity to take over, meaning that agencies have retained . their best performing routes for in-house operation. By remaining the funding sponsors, public authorities are in a position to hold back any routes they so choose from possible bidding. Experiences to date show that only the highest deficit, poorest performing routes are ever contracted. iv Overall, it is concluded that competitive contracting of fixed-route transit services, as practiced today and in the foreseeable future, actually results in deficit-skimming. Rather than ruthless predators, contractors are actually friends of the public transit sector. They take over the least productive routes and usually deliver a comparable or better quality of service at a lower deficit rate. v Preface Many people were generous with their time in helping us conduct this research. Our special thanks goes to the staffs of over thirty public transit agencies who kindly supplied cost and revenue data in support of our analysis. We owe a particular debt of gratitude to William Lyons of the Transportation Systems Center who provided us with unpublished Section 15 data and helped us wade through reams of statistical accounts. Subhash Mundle and Janet Krause of Mundle and Associates of Philadelphia kindly provided us background data on route cost allocation models. Sharon Jacobs of Houston Metro and Burton Sexton of. WMATA were very accommodating in helping us carry out the case study phases of the research. The names of others deserving of special recognition have surely been left out, but to all of those who helped us through the drudgery of compiling information, we extend our thanks. Robert Cervero was the Principal Investigator of the project and wrote the research report. John Greitzer provided research assistance on cost modeling in chapter three and the case studies presented in chapter five. All of the maps were prepared by Cherie Seamans. The views and opinions expressed in the report are solely the author's and do not necessarily reflect those of the U.S. Department of Transportation, Urban Mass Transportation Administration. Table of Contents Page Executive Summary Preface Table of Contents . . . . . . . . . . . . . . . . . . . . . . . vii List of Figures . . . . . . . . . . . . . . . . . . . . . . . . .ix List of Tables. . . . . . . . . . . . . . . . . . . . . . . . . . x Chapter One: The Cream-Skimming Debate. . . . . . . . . . . . . 1 1.1 Introduction. . . . . . . . . . . . . . . . . . . . . . 1 1.2 Research Focus. . . . . . . . . . . . . . . . . . . . . 3 1.3 Report Outline. . . . . . . . . . . . . . . . . . . . . 5 Chapter Two: The Context of Transit Service Contracting in the U.S.. . . . . . . . . . . . . . . . . . . 7 2.1 Fiscal and Performance Trends in America's Transit Industry . . . . . . . . . . . . . 7 2.2 Potential Benefits of Competitive Contracting of Transit Service 9 2.3 Scope of Transit Service Contracting in the U.S.. . . .14 Chapter Three: Profitability of Public Transit Operations in the U.S.. . . . . . . . . . . . . . . . . . .19 3.1 Introduction: Previous Work on the Profitability of Transit Operations. . . . . .19 3.2 Specifying Profitability. . . . . . . . . . . . . . . .20 3.3 Investigating the Incidence of Profitability: Methodology. . . . . . . . . . .23 3.4 Comparison of Route Characteristics Among Express and Local Services . . . . . . . . .30 3.5 Level of Profitability. . . . . . . . . . . . . . . . .31 3.6 Summary Comparison of Performance Among Local Versus Express Routes . . . . . .34 3.7 Summary . . . . . . . . . . . . . . . . . . . . . . . .34 Chapter Four: Probing the Scale Economies Argument Against Contracting. . . . . . . . .38 4.1 Past Research on Economies of Scale in the U.S. Transit Industry . . . . . . . . . . .38 4.2 Evidence of Scale Economies from Case Studies . . . . .39 4.3 Comparison of Average Cost and Deficit Rates Among Transit Properties . . . . . .41 4.4 Conclusion. . . . . . . . . . . . . . . . . . . . . . .44 Chapter Five: Characteristics of Profitable Bus Routes . . . . .46 5.1 Introduction. . . . . . . . . . . . . . . . . . . . . .46 5.2 Southeast Pennsylvania Transportation Authority (SEPTA) . . . . . . . . .47 5.3 Greater Richmond Transit Company. . . . . . . . . . . .52 5.4 Washington Metropolitan Area Transit Authority (WMATA). . . . . . . . . . . . .57 5.5 Minneapolis St. Paul Metropolitan Transit Commission (MTC) . . . . . . . . . . . . .62 5.6 Conclusion. . . . . . . . . . . . . . . . . . . . . . .63 vii Table of Contents (continued) Chapter Six: Challenging Other Arguments Against Competitive Contracting . . . . . . .66 6.1 Introduction. . . . . . . . . . . . . . . . . . . . . .66 6.2 Oversight of Competitive Contracts. . . . . . . . . . .66 6.3 Agency Control of Contracted Services: Example of Houston Metro . . . . . . . . . . . . .67 6.4 Experiences With Transferring a Public Operated Fixed-Route Service. . . . . . . .68 6.5 Concerns Over Deterioration of Service Quality . . . . . . . . . . . . . . . .69 6.6 Concerns Over Labor Protection. . . . . . . . . . . . .70 6.7 Concerns Over Eventual Contractor Cost Increases. . . .70 6.8 Conclusion. . . . . . . . . . . . . . . . . . . . . . .71 Chapter Seven: Research Conclusions: Competitive Contracting and Deficit-Skimming. . . . . . . . . . . . .73 7.1 Debunking the Cream-Skimming Myth. . . . . . . . . . . .73 7.2 Contracting and Deficit-Skimming. . . . . . . . . . . .73 References. . . . . . . . . . . . . . . . . . . . . . . . . . . .75 Appendix A Transit Agencies Examined in the Research, Listing by Headquarters City and Peak Fleet Size. . . . . . . . . . . . .81 Appendix B Variables Used in the Analysis . . . . . . . . . .83 Appendix C Three-Factor Cost Allocation Model, Calculated for Seattle Metro and Denver RTD .84 Appendix D Cost Allocation Models for Analysis. . . . . . . .87 List of Figures Page 2.1 Financial Trends in the U.S. Transit Industry, 1975-1985 . . 8 2.2 Breakdown of Operating Expenses for U.S. Transit Industry, 1980 and 1985 . . . . . . . . . . . . 8 2.3 First and Second Order Benefits of Competitive Contracting .10 3.1 Trade-off of Cost Recovery Rate and Total Operating Deficit.22 5.1 Locations of SEPTA's Three Profitable Bus Routes . . . . . .49 5.2 Percent White and Non-White Households for SEPTA Route Corridors . . . . . . . . . . . . . . .51 5.3 Locations of GRT's Four Profitable or Near-Profitable Bus Routes. . . . . . . . . . . . . . .53 5.4 Percent White and Non-White Households for GRT Route Corridors . . . . . . . . . . . . . . . .56 5.5 Locations of WMATA's Three Profitable Bus Routes . . . . .59 5.6 Percent White and Non-White Households for WMATA Route Corridors . . . . . . . . . . . . . . .61 5.7 Location of MTC's Route 21 . . . . . . . . . . . . . . . .63 List of Tables Page 3.1 Listing of 25 Case Sties, by Vehicle Fleet Size Class. . . .24 3.2 Typical Two-Variable Cost Allocation Model . . . . . . . . .27 3.3 Typical nm-Variable Cost Allocation Model. . . . . . . . . .28 3.4 Comparison of Average Daily Route Characteristics Among Peak-Only Express and AR-Day Local Routes . . . .31 3.5 Comparison of Average Daily Route Characteristics by Fleet Size . . . . . . . . . .32 3.6 Bus Routes With Cost Recovery Ratios Above 0.75. . . . . . .33 3.7 Comparison of Average Performance Characteristics Among Express versus Local Routes . . .35 4.1 Comparison of Financial Performance Indicators by Fleet Size40 4.2 Comparison of Peak Passenger Cost Factors. . . . . . . . . .42 4.3 Comparison of Peak Passenger Deficit Factors . . . . . . . .43 5.1 Listing of Case Sites by Vehicle Fleet Size Class. . . . . .48 5.2 Average Income Indicators for SEPTA's Three Profitable Routes and At-Large Service Area . . . . . .50 5.3 Residential and Employment Densities Along SEPTA's Three Profitable Routes and for Philadelphia. .50 5.4 Average Income Indicators for GRT's Two Profitable Local Routes and At-Large Service Area . . .54 5.5 Ridership and Corridor Household Characteristics of Four Profitable Routes and for GRT Service Area . . . .55 5.6 Residential and Employment Densities Along GRT's Two Profitable Local Routes and for the GRT Service Area55 5.7 Average Income Indicators for WMATA's Three Profitable Local Routes, Washington, D.C., and SMSA. . . . . . . .60 5.8 Residential and Employment Densities Along MTA's Three Profitable Local Routes and for Washington, D.C..60 Chapter One The Cream-Skimming Debate 1.1 Introduction A growing body of evidence suggests that private contracting of urban transit services can substantially reduce operating deficits and inject a healthy dose of competition into America's troubled transit industry. As federal funding assistance continues to shrink and pressure mounts to eliminate waste and inefficiencies in current service and pricing practices, many observers contend that private contracting offers the best hope for restoring the industry's fiscal health of decades earlier when the private sector's role in America's transit scene was far more prominent (Lave, 1985; Orski, et al., 1985; Teal, et al., 1986; Guskind, 1987; Cervero, 1988; Teal, 1988). A commonly voiced argument against private contracting and greater competition is that free-wheeling entrepreneurs will "skim the cream" from public transit operators by taking away the most lucrative services. Some critics contend that private firms, whether unwittingly or not, will engage in predatory competition that will ultimately undermine the nation's transit industry. If predators skim, away money-making services, the argument goes, public transit agencies will be left with the money losers, thus leading to even higher operating deficits. This cream-skimming argument has long been used by the transit industry to protect its position as a publicly sanctioned monopoly -- first, to squelch competition from jitneys in the early 1900's and later to keep a lid on the number of taxi permits issued during the depression years of the 1930's (Eckert and Hilton, 1972; Cervero, 1985). Protestations that free competition would lead to violent taxi wars and unscrupulous pricing practices were common. Over time, taxis, jitneys, and private buses have fallen under government control in the belief that their ascendancy would harm common-carrier public transit services and threaten public safety. Regulations which restrict the entry of private transit operators into the local market or which create an oligopoly by granting limited numbers of franchises have become firmly entrenched in most American cities. The underlying economic rationale for shielding public transit operators from competition is that they are natural monopolies -- that is, they enjoy economies of scale (where average costs decline as demand increases), as do water and electric utilities. Under such conditions, a sole operator can most efficiently and inexpensively provide services. If numerous competitors were allowed into the urban transportation market, pundits contend, they would seek to serve only the most lucrative routes and corridors, leaving the "dregs" to public transit agencies. However, a single transportation company would serve the public interest by operating both money-making and money-losing services, a practice commonly called cross-subsidization. To ensure high levels of transportation throughout an urban region, then, regulators argue that the public has an obligation to protect public transit carriers from excessive competition and ensure that they receive an adequate rate of return. What prompted a reversal of thinking about the role of the private sector in the urban transit field was the steady decline in the fiscal health of America's transit industry during the 1970's. Because of spiralling deficits, federal subsidies to urban transit jumped from $132 million in 1970 to over $3.2 billion in 1982, a 2,400 percent increase (American Public Transit Association, 1982). Skyrocketing deficits, some argued, were endemic of a public sector that lacked any incentive to contain costs, innovate, and respond to changing patterns of demand and cost. Research generally confirmed this. Studies showed that, in general, operating subsidies have led to higher labor costs, unproductive work practices, lax management, unjustifiably low fares, and the un- business like expansion of services into marginal, low-density markets (Bly, et al., 1980; 1 Pickrell, 1983; Cervero, 1983; Pucher, et al., 1983). Public subsidies, critics maintained, have shielded local transit authorities from the responsibility for bringing down deficits, and removed any incentive to change entrenched fare and service practices and to adapt to new markets. Partly in response to these charges, privatization has become the centerpiece of federal urban transit policy in the United States since 1980. In a marked departure from past policies, the leadership of the federal Urban Mass Transportation Administration has strongly advocated maximum private sector participation in the delivery and financing of public transportation facilities and services (Teal, 1986). A watershed in this regard was the issuance of the Federal Private Enterprise Participation Policy Statement in 1984 which holds that "when developing federally assisted mass transportation plans and programs, UMTA grantees should give timely and fair consideration to the comments on proposals of interested private enterprise entities in order to achieve maximum feasible private participation".1 This statement gave rise to a number of initiatives to enlist the private sector's involvement in the urban transit field, perhaps the most controversial of which has been the contracting out of fixed-route bus services. The threat of work assignments being transferred from the public to the private sector, in particular, has spurred vocal labor opposition to the contracting out of services. However, the intent of contracting is less one of eliminating public jobs than one of injecting competition in the transportation sector. Transferring transit services from a public authority to a single franchise firm, in and of itself, is likely to produce few tangible benefits. Indeed, the substitution of a private transit monopoly for a public one will neither lower costs or improve performance. As being promoted in the transit field, contracting's aim is not to necessarily return public services to private carriers but rather to enlist private organizations in the delivery and financing of transportation services and facilities. Without exception, control over service and fare practices remains in the public sector. Private sector involvement is substantially limited to the actual delivery of services. Thus, the overarching objective is to end the public sector's monopoly of urban transit services and replace it with a policy of competition. This dramatic turnabout in federal transit policy has brought with it a renewal of charges that privatization will hurt public operators and encourage cream-skimming. According to Kenneth Moore, head of the 165,000 member-strong United Transportation Union: We're very concerned that the private carriers will come in and want to bid on the most lucrative routes in the properties. They will leave the less desirable routes -- the ones that can't make money -- for the public entity to operate. And that's just going to harm the public, because it's going to cause deterioration of service (Ryan, 1987, p. 12). Representative William Lehman, chairman of the Transportation Subcommittee of the U.S. House of Representative's Committee on Appropriations, echoed these sentiments when he stated that privately operated transit services were desirable only "... as long as they do not drain off the best routes from the public transportation [operators] so that public transportation is just left with the more costly type of routes."2 Critics of privatization have also invoked the cream-skimming argument in contending that private vendors have little interest in public welfare and seek only to make profits: No contractor is interested in picking up the homeless from the streets, halting illegal immigration, or taking on the problem of drug abuse in the schools. Government always gets the [unprofitable] jobs of society (Goodsell, 1986, p. 20). It is against this backdrop of competing arguments as to whether competitive contracting of transit services will improve the fiscal position of public transit agencies or 2 "skim their cream" that this research has been conducted. The cream-skimming debate remains an obstacle to the widespread acceptance of competitive contracting of services in the transit field and thus deserves to be studied in some degree of depth. The intent of this report is to help resolve the debate by articulating the extent to which competitive contracting would generally be financially beneficial or harmful to America's transit industry. 1.2 Research Focus It is believed that the competitive contracting of transit services that remain under the control of public agencies would help to appreciably lower operating deficits. Thus, this research postulates that competitive contracting would lead to-deficit- skimming" rather than "cream-skimming". The cream-skimming argument, we believe, rests on several questionable assumptions about urban transit's cost and performance characteristics. First and foremost is the belief that there are significant numbers of publicly operated routes and services across the country that are generating a profit. We believe there are very few. A second tenable assumption is that public transit is a natural monopoly - that is, there are inherent economies of scale in the transit industry and consequently lower unit costs can be realized by allowing a single exclusive franchiser, typically a public authority, to operate free from any head-to-head competition. Our belief is that public transit should no longer be considered a natural monopoly in the pure sense of the word in large part because there is little evidence of scale economies. The final argument that deserves scrutiny is that transit relies on the principle of cross-subsidization to fulfill its public service mandate -- profitable routes help cover the losses sustained on money-losing routes to the benefit of the community at large. Again, the argument goes, allowing private service providers to enter the transit marketplace will siphon away the most lucrative services to the detriment of the larger public interest. This point, of course, hinges on the premise that privatization involves an open "free-for-all" where private firms can go after any route or service they choose. We contend that as practiced to date, all public agencies that have engaged in contracting have retained full control over their services and have judiciously bid out routes or services that would incur too large of a deficit if operated in house. This research probes the extent to which the assumptions upon which the cream-skimming argument is built are empirically supported. Combining literature reviews and case studies with cost and performance data compiled for a sample of U.S. transit properties, the analysis focuses on the degree to which the cream- skimming argument seems to be fact or fiction. Specifically, three main questions are addressed: are there hardly any profitable publicly operated transit routes in the U.S. from which to skim any cream? is there evidence of economies of scale to justify protecting public transit agencies from competition? and are there any cases where public agencies have given up control of profitable or near-profitable services to private firms? To the extent that each of these questions can be answered with a "no", it is felt that the cream-skimming objections to service contracting can be laid to rest. Besides the cream-skimming argument, a number of criticisms have been frequently aired against contracting. These include: (1) overall service quality will deteriorate; (2) the position of transit labor will worsen; and (3) private provider costs will eventually rise to near the level of public service costs. This research looks into these criticisms as well. It should be noted that this study addresses the cream- skimming topic only with respect to fixed-route bus transit services. In general, the debate has focused on fixed-route bus operations since they represent the bread-and-butter of the transit industry, accounting for well over 80 percent of all transit passengers served and vehicle miles operated in the U.S. (American Public Transit Association, 1987). While private companies operate dial-a-ride 3 (DAR) services throughout the country, cream-skimming charges are rarely leveled against these firm since it is widely accepted that DAR encumbers high deficits per passenger. Rail transit services were omitted from the analysis since their cost structures are fundamentally different from those of bus transit systems owing to their capital-intensiveness. Additionally, since rail transit is widely held to enjoy scale economies.(at least when outputs are measured in terms of ridership) and the start-up costs of building a system are so enormous, rail transit takes on many characteristics of a natural monopoly. The fact that no urban rail transit routes in the U.S. are competitively contracted out by public transit agencies suggest that the cream-skimming debate is a non-issue in this sector. Within the fixed-route bus transit sector, the cream-s g argument against private contracting is linked primarily to the bidding out of peak-hour, express services. There is a common belief among many transit managers and board members that the peak is their ft cream", mainly because buses are usually filled and workers are at their busiest at that time (Oram, 1979; Cervero, 1982b; Lave, 1985). While rush-hour buses often have standing-room only, midday and late-evening buses sometimes only carry a few riders at a time. Furthermore, the fares charged for commuter express services are usually much higher than local fares, often by as much as a factor of two. Thus, the combination of a higher average load factor and a higher average fare has understandably led many to view peak express runs as money-makers. A careful look at the cost of operating these services, however, begins to suggest otherwise. Peak-only express services typically make one to three stops in the morning to pick up riders, quite often at suburban park-and-ride lots. Buses then proceed in a "closed-door" (i.e., non-stop) mode to a central business district or major employment center. The evening outbound trip is simply the reverse. Peak express runs are characteristically so long that only one trip per vehicle is possible during the morning or evening hour. Because of restrictive work rules, scheduling complexities, and low off-peak demand, drivers working these shifts end up being paid up to four hours for a single hour or so of revenue service. Since riders travel only in a single direction, buses frequently make the return trip empty. When one accounts for the labor cost penalties, deadhead, and layover allowances associated with peak-only express runs, these services no longer appear so solvent. One study found that it cost 2.5 times as much to provide a passenger with express service than local service, giving rise to an average subsidy that is nearly four times as high (General Accounting Office, 1981). While publicly operated express services have historically been plagued by high deficits, another studied found a number of instances where private companies were operating peak express services at a profit (Urban Mobility Corporation, 1985). Because the cream-skimming debate centers largely around peak- express services, the empirical phases of the research concentrates on comparing cost recovery differences between the most heavily patronized peak-express versus all-day local services for a sample of transit properties across the U.S. If the bulk of transit services that have been competitively contracted out to date are peak-express and these services are shown to be money-losers and return smaller shares of their costs through the farebox than their all-day local counterparts, the cream-skimming argument becomes groundless. Thus, a comparative examination of the cost and performance characteristics of heavily patronized peak-express versus all-day local services forms the empirical core of this research. In addition to directly testing the validity of assumptions behind the cream-skimming argument, this research probes some of the operating and contextual characteristics of the few profitable publicly operated fixed-route bus transit routes in the U.S. Particular attention is given to factors shaping the demand for travel on profitable routes, namely levels of transit dependency and various land use attributes of the corridors served. It is hypothesized that 11 profitable" or "near-profitable" services operate along dense urban corridors dotted with major activity centers wherein vehicles are generally full and the average distance travel by passengers is relatively short. Qualitative case evaluations of "profitable" routes are presented 4 for services in Philadelphia (PA), Washington, D.C., Richmond (VA), and Minneapolis-St. Paul (MN). Experiences of the Houston Metro transit agency and five other public entities which are engaged in the competitive contracting of fixed-route bus services are presented as a separate case analysis. Houston Metro offers an excellent case setting for further probing some of the other criticisms lodged against the contracting of peak-express services since it has one of the largest (in terms of both service miles and ridership) and longest standing pro of privately contracted express bus services in the country. It is also one of only two American public transit agency to convert an already-existing fixed route service from public to private operation. In addition to assessing Houston's and the other agencies' experiences to date with competitive bidding of services, the case study also focuses on the contractual agreements struck between Houston Metro and private carriers to gauge the degree to which public agencies maintain control over service standards and prices. Finally, it should be mentioned that while privatization takes many forms, this research examined the cream-skimming controversy only with regard to competitive contracting of transit services. Besides contracting for the operation of bus runs, privatization of urban transit can also involve subcontracting of specialized services (e.g., maintenance), private buyout or takeover of a public service, franchising of new services, removal of taxi and jitney entry restrictions, and more. Since the cream-skimming issues centers around private operators taking over lucrative services that have historically been under a transit agency's jurisdiction, this study concentrates on the financial implications of contracting out specific bus routes and service types. 1.3 Report Outline The report is divided into six remaining chapters. Chapter Two sets a context for studying competitive contracting by describing its national scope, potential benefits, and various financial factors that prompted the federal government to actively promote privatization of the nation's mass transit sector. The third chapter investigates the incidence of profitability among U.S. transit properties with 75 or more revenue vehicles. After presenting different ways of measuring profitability, the chapter discusses the methods employed in deriving direct, day-today operating costs for specific high ridership routes that were studied. The chapter closes with a listing and assessment of the profitable bus routes that were identified and a summary comparison of fiscal performance among all-day versus peak-express routes. Chapter Four further probes the viability of the cream- skimming argument by investigating the degree to which economies of scale appear to exist within the U.S. transit industry. Evidence on scale economies for 25 case sites is presented based on an analysis of how unit costs vary as a function of fleet size. As a follow-up to the third chapter, Chapter Five presents the demand- side characteristics of profitable routes for four transit properties. Routes are described primarily in terms of the level of transit-dependency of their customer base, their average load factors, average length of trips, and surrounding population and employment densities. Chapter Six addresses other issues which appear to invalidate the cream-s argument. Particular emphasis is placed on defining the degree to which public agencies engaged in contracting retain control over route design, performance standards, and pricing. The few experiences to date with transferring a pre-existing publicly operated bus route to a private service-provider are examined to assess the financial implications of such a switch. Chapter Six also examines the soundness of other arguments lodged against service contracting, including concerns over possible deterioration of service quality and eventual contractor cost increases. Chapter Seven concludes the report with a summary of the research findings and an 5 overall appraisal of the extent to which competitive contracting involves cream-skimming versus deficit-skimming. Notes 1. Quoted in: "Contracting Cuts Costs," Metro, September/October, 1985, p. 86. 2. Hearings before Subcommittee on the Department of Transportation and Related Agencies of the Committee on Appropriations, U.S. House of Representatives, May 1, 1985, Part 7, pp. 645-646. Quoted in: Pickrell (1986). 6 Chapter Two The Context of Transit Service Contracting in the U.S. 2.1 Fiscal and Performance Trends in America's Transit Industry The clear impetus behind the privatization movement has been the dramatic financial and performance declines experienced by America's public transit industry over the past twenty year. This section briefly discusses these declines for the nation as a whole. Financial Trends The cost of operating the nation's public transit systems increased more than threefold between 1975 and 1985, even though ridership remained more or less constant over this period. Figure 2.1 shows that the total operating cost (excluding capital expenditures) exceeded $12 billion in 1985. In the same year, passenger fares generated around $4.6 billion, meaning about 39 percent of costs were recovered through the farebox (i.e., farebox recovery rate = .39). When other sources of in-house revenue (e.g., advertising, rental income) are combined with fares, the total deficit in 1985 amounted to $6.7 billion. In constant 1985 dollars, this represents nearly a doubling of the total operating deficit since 1980. To make up the $6.7 billion deficit in 1985, 86.5 percent of operating assistance came from state and local governments, with the remaining subsidy covered by the federal treasury. In keeping with UMTA's policy of reducing local dependency on federal operating assistance, the federal share of total operating subsidies fell from around 30 percent in 1980 to under 15 percent five years later. While costs have continually escalated over the past two decades, ridership on surface transit in the U.S. has remained fairly constant since 1965, hovering at around 6 billion passenger trips per year. Expressed in constant 1985 dollars, the average operating cost per trip mom than doubled from 81 cents in 1980 to $1.69 in 1985. Over the same period, the average revenue per trip grew by only 41 percent (in constant dollars). The breakdown of expenses for operating public transit services in the U.S. in both 1980 and 1985 are shown in Figure 2.2. Expressed in constant 1985 dollars, the figure shows that every expense item increased over this period, although fuel and lubricant costs remained more or less the same. During the first half of the 1980's, the fastest real dollar increases were for fringe benefits, materials and supplies, and the "other" category of expenses (e.g., utilities, liability costs, and rents). The share of expenses going for salaries and fuel declined by between 2 and 4 percentage points over the 1980-1985 period. The inflation-adjusted expenditures for labor (salaries and benefits) grew only slightly faster than the number of transit employees between 1980 and 1985 -- 44 percent versus 40 percent. In general, the labor compensation package cost about the same in both years. In 1985 dollars, transit workers averaged annual salaries and benefits worth $32,350 in 1980 and $33,350 in 1985. Annual compensation levels have been the highest in the nation's Northeast and Pacific Coast regions and in metropolitan areas with populations above one million (Urban Mass Transportation Administration, 1988). When expenses are indexed to vehicle mileage over time, it is apparent that each dollar invested in the transit sector is buying less service. Expressed in constant 1985 dollars, every 7 Click HERE for graphic. Click HERE for graphic. 8 mile of transit service devoted to carrying passengers cost $3.25 in 1975, $3.57 in 1980, and $4.92 in 1985. Thus, even when controlling for inflation, every dollar sunk into America's public transit industry today buys far fewer passengers and miles of service than a decade ago. Operating Performance Performance can also be gauged in terms of various non- financial indicators, such as vehicle miles of service per employee (a measure of labor productivity) and passengers per vehicle (a measure of vehicle productivity). In general, indicators which translate resource inputs (e.g., labor, vehicle, fuel) into service outputs (e.g., vehicle miles, vehicle hours) are referred to as efficiency measures (Fielding, et al., 1978). Indicators which express service utilization (e.g., revenue passengers, passenger- miles) as a function of service inputs (e.g., employees, vehicle miles, operating expenses) are typically called effectiveness measures. Between 1975 and 1985, transit labor productivity dropped sharply in the U.S. Over this period, the annual revenue vehicle miles of service per full-time-equivalent employee fell from 13,618 to 9,364. The sharpest declines in labor productivity have generally been recorded in the nation's largest cities (Urban Mass Transportation Administration, 1988). Of course, labor productivity is highly sensitive to such factors as work rules, personnel management practices, and levels of deadheading. While work rules are controllable through labor negotiations, factors such as the amount of deadheading are shaped in part by exogenous forces like the suburbanization of employment and residences. Between 1980 and 1985, the number of passenger boardings per vehicle mile of service has fallen by about 4 percent - from 3.76 to 3.64. This decline occurred in all parts of the country except the Northeast and in all size classes of cities except those above one million population (Urban Mass Transportation Administration, 1988). The higher level of service effectiveness in large Northeast cities like New York and Boston largely reflects their provision of higher capacity rail services as well as their high average residential and employment densities. Another effectiveness indicator is vehicle utilization. During the first half of the 1980's, vehicle utilization rates slipped by around 25 percent when expressed in terms of total passenger miles per vehicle. In 1980, each transit vehicle registered around 530,000 passenger miles; by 1985, this figure dipped to 424,000. Part of this decline can be explained by the slight decrease in average trip lengths made by transit -- from 4.65 miles in 1980 to 4.53 miles in 1985. Declining load factors and the accumulation of spare fleets of vehicles, however, likely account for most of the drop-off. 2.2 Potential Benefits of Competitive Contracting of Transit Services In large part because of the transit industry's declining fiscal health and operating performance, competitive contracting has been touted by many advocates as the most promising way of reversing these declines. In theory, two levels of benefit should redound to public agencies that competitively contract out transportation services. The immediate benefits, called first- order benefits, should result from substituting lower cost privately operated services for comparable publicly operated ones. By spurring competition, however, contracting should also produce second-order benefits. To the extent the public sector's input costs and work practices are out of line with those of the private sector, for example, wage and work rule concessions might be expected. The very survival instincts of public agencies and organized labor in some instances might exert downward pressures on expenses and prompt various productivity improvements. As the public agency becomes more competitive, even a stronger cost discipline is exerted on the private sector, further driving down expenses and inducing additional productivity gains. In principle, then, competitive contracting gives rise to efficiency mindfulness in both public and private sectors to 9 the benefit of society at large. Figure 2.3 presents a normative model of competitive contracting's likely first and second order benefits in the urban transit field (Cervero, 1986). The engine that propels the process is competition. Competition drives down costs when the contract is first awarded and continues to contain them as contracts are periodically rebid. To remain competitive, firms must hold the line on costs, maintain lean overheads, and tailor their capital acquisitions so as to assure a profit. They are also more inclined to aggressively pursue new customers and carve out market niches. Studies show, for instance, that firms engaged in contracting use luxury buses for special charter services during weekends and off- peak hours and as premium-quality commuter buses for the general public during rush hours; thus, contracting has resulted in firms efficiently using available bus capacity over nearly the entire week (Giuliano and Teal, 1985). With a public monopoly, there is no real incentive to be efficient. Especially when insulated by subsidies and dedicated assistance, public managers need not fret over declining productivity or rising deficits. Notes one observer: "even private monopolies ... are goaded to some extent by the profit motive, whereas in the public sector the prerogatives of supplying inputs tend to be more important and to bear heavily on the production process than either do the profit motive or the objectives of satisfying consumers" (Fitch, 1975, p. 403). Indeed, the success of a noncompetitive public enterprise is often gauged by the size of a manager's budget and work force rather than the net return on investment. COMPETITION First-Order Benefits: - Service cost savings - Increased productivity - Service flexibility Second-Order Benefits: - Cost containment - Work rule changes - Increased public sector productivity - Service innovation Figure 2.3. First and Second Order Benefits of Competitive Contracting 10 In principle, there is no reason why private firms should be able to operate at lower costs than government agencies or franchises. In fact, public transit agencies enjoy several advantages over their private sector counterparts, including lower borrowing costs (due to their tax exempt status) and high rates of capital subsidies. The reason costs are not lower, quite simply, is because a profit stimulus is absent, the very thing which spurs private operators to curb expenses, aggressively seek out customers, and innovate. It is important to stress that competitive contracting does not mean public abandonment of mass transit. The overarching goal of mass transit authorities should always be to move as many people as safely and efficiently as possible within available resources. While governments can support this goal through careful strategic planning and financial assistance of operations, this does not necessarily mean they should directly operate each bus run themselves. Government's role should be one of sponsorship - to see what should be done is in fact done. The question as to who should actually deliver the services can best be answered in the marketplace through competition. Expected First-Order Benefits Three chief first-order benefits are listed in Figure 2.3 that could be expected from transit service contracting: (1) cost savings; (2) increased productivity; and (3) greater flexibility. (1) Cost Savings: Most of the cost savings from competitive contracting stem from lower labor compensation as well as work rule conventions which are less favorable to private sector than public sector employees. Wage rates are sometimes 50 percent lower and employer-paid benefits are substantially less for private drivers than for transit agency drivers who perform similar duties. This is particularly so when private workers are non-unionized. Besides receiving lower wage rates, studies show that private transit employees also rarely receive split-shift or spreadtime pay premiums and generally accrue overtime only after twelve hours (Giuliano and Teal, 1985). There also tend to be fewer restrictions on hiring part-time help in the private sector, an important consideration given the extreme peaking of transit demand. Additionally, private drivers can frequently be assigned light maintenance and other tasks during off-peak hours, whereas public transit workers are almost universally precluded from such work by featherbedding labor clauses. Labor cost savings of 23 percent to 70 percent have been recorded for public transit services transferred to small bus companies using non-unionized drivers (Cox, 1983; Teal, 1985). Even private firms operating under exclusive franchises have been found to have lower average wage rates and fewer work rule penalties than their public sector counterparts (Pucher and Markstedt, 1983; Perry, 1984). The cost savings conferred by competitive contracting are by no means unique to public transit. Contracting is today used for a wide array of government services - sweeping streets, refuge disposal running golf courses, police protection, school transportation, among others. The city of Pasadena, California, for instance, contracts out 21 percent of its $130 million annual operating budget. Local officials discovered they could save around $250,000 per year, or 25 percent of expenses, by hiring janitorial services (Main, 1985). And currently, more than 40 percent of all school trips in the U.S. are provided by private operators -- accounting for more riders than are carried by public mass transit -- at an estimated savings in the hundreds of millions of dollars (Metro, 1985). (2) Higher productivity: By most measures, productivity - i.e., the efficiency in which resource inputs are translated into service outputs -- is comparatively high in the private sector. For instance, since many private firms use available fleets during the midday, evening, and weekends for special charter services, their levels of vehicle productivity are normally higher than those of public transit agencies -- i.e., more road miles of service per vehicle are logged each year. 11 Similarly, careful attention to scheduling in the private sector often reduces deadheading and results in more revenue miles of service per vehicle. Moreover, regular inspections and management oversight typically result in higher maintenance productivity. (3) Service flexibility: With contracting, transit agencies are able to experiment more freely with new services. If a service proves to be unproductive, it can easily be eliminated by discontinuing the contract without any major backlash among public employees. Such flexibility is particularly important in view of the rapid demographic, economic, and spatial changes that are demonstrably changing travel patterns, such as the increase in inter-suburban commuting (Rothenberg, 1986; Pisarski, 1987). Second-Order Benefits While less attention has been given to the ripple effects of competitive contracting, in part because contracting is still in its infancy, a number of second-order benefits can nevertheless be expected. Figure 2.3 lists four of them: (1) cost containment; (2) work rule changes; (3) increased public sector productivity; and (4) service innovation. (1) Cost containment: The primary ripple effect of competitive contracting should be the exertion of a market discipline on input costs (labor, supplies, rents) within the contracting public agency itself. Because of competition, public agencies will find it necessary to contain costs, forcing managers to hold the line on purchases and drive harder bargains at the wage negotiations table. There is certainly enough precedence to expect this based on experiences in other industries and common carriers of transportation. Stiff competition from abroad, for instance, pressured Detroit's automobile manufacturers to take "belt- tightening" measures to contain costs in the early 1980's. To remain competitive, most were able to win significant wage and fringe benefit concessions from labor. Similarly, deregulation of the nation's airline industry has increased competition to the point where a number of drastic cost-cutting steps have been taken by different carriers, including wage rollbacks and the elimination of automatic cost-of-living allowances. Because current employees stand to lose either real wages or their jobs, not surprisingly much of the resistance to competitive contracting has come from organized labor. Because of Section 13(c) of the Urban Mass Transportation Act, which protects transit workers from being banned by public policy actions, the ability of competitive contracting to exert a discipline on wage rates and size of transit agencies' payrolls remains uncertain. Notably, 13(c) has been used by labor as a powerful bargaining chip for preserving a public monopoly of all transit jobs. Accordingly, as long as the 13(c) requirement remains in its current form, any phasing-in of competitive contracting will likely be bound by the natural rate of employee attrition. The greatest resistance to contracting has generally been in large urban areas with politically potent labor unions, notably cities in the nation's northeast and midwest regions. Some critics also discount the ability of contracting to contain wages, arguing that private sector costs will also eventually increase (Vuchic, 1986). this could occur, for instance, in small areas where there is an absence of competition because only one private firm has chosen to bid on a contract. Moreover, as a private firm wins more contracts and expands its workforce, employees are more likely to organize into unions; consequently, the argument goes, their costs will begin to rise as they experience some of the scale diseconomies facing large public monopolies and the threat of worker strikes looms. At least five cases of this have already been documented (U.S. Department of Transportation, 1985). Another possible scenario is that some competitors will deliberately underbid to win the contract, only to come back to the agency for additional funds after services have been initiated under the threat that they will go bankrupt. Unforeseen administrative costs that were not included in the negotiated contract have forced private firms to renege on contracts in several instances and prompted others to scrimp on services (Richard, 1980). In general, the periodic rebidding of contracts should provide an effective safety 12 valve for preventing this. Moreover, experienced managers would most likely become leery of bargain-basement offers and could weed out unqualified bidders by writing certain performance standards into contracts. Notwithstanding these concerns, specific second-order cost impacts that could be expected from competitive contracting include: wage rollbacks; elimination or reductions in automatic COLAS; job reclassifications; creation of non-unionized work positions; establishment of new wage structures for those hired after a certain date; changes in holiday and sick pay; - and revision of benefit packages. The most notable example of a second-order benefit in the transit field is the case of the regional transit authority in the Norfolk, Virginia area (Tidewater Regional Transit) which was able to win back paratransit services previously contracted out to a private taxi company because labor agreed to major concessions (Cervero, 1986; Talley, 1986). With the private take-over of dial- a-ride services, public employees recognized that their own future livelihoods were at stake unless the agency became more efficiency minded. Workers agreed to created a new minibus operator position at a comparatively low starting salary, with no work rules, no pay for out-of-service time, and reduced benefits. Talley (1986) estimated that every 10 percent increase in the service contracted out by the local transit agency led to a 1.5 percent decrease in its in-house worker compensation package. Able to underbid the taxi service which won the agency's dial-a-ride services only five years earlier, today nearly all transit services operated in the Norfolk, Virginia area are publicly operated. Similarly, the public transit agency in Dallas was able to win back some services previously contracted out because of the ripple benefits of competition. Competitive contracting in San Diego has likewise spawned some wage concessions by the city's transit workers during the 1980's (Orski, et al., 1985). Lastly, contracting for municipal services has spurred internal cost savings in at least one other instance. In 1977, officials in Phoenix opened up areas of the city to refuge contracting. With time, as the city's public works department entered its own bid, it became more and more competitive. In 1984, the department actually won back a section of the city that had previously been contracted out (Main, 1985). (2) Work rule changes: Competitive contracting could also be expected to prod unions into accepting certain work rule concessions. These might include: repeals of contract restrictions on hiring part-time workers; bilateral agreements to increase the share of part-time employees; reduced spreadtime, split-shift, or overtime pay premiums; reductions in guaranteed or combination time pay provisions; and relaxation of straight-time requirements for a fixed percentage of peak period drivers. Changes in part-time employment practices could prove particularly remunerative to public transit agencies. Studies consistently show the marginal cost of peak services to be exorbitant, on the order of two to three times as high as marginal costs in the off-peak (Morlok, et al., 1971; Oram, 1979; Cherwony, et al., 1981). Peak-only express services are even higher, up to four times as costly as regular route services on a per mile basis (Urban Mobility Corporation, 1985). At least three precedents can be found wherein competitive contracting, or the threat of introducing it, gave rise to work rule concessions (Cervero, 1986). In an attempt to recapture dial- a-ride services bid out to a private taxi company, public transit workers in the Norfolk, Virginia area agreed to the hiring of part- time workers and the reduction of guaranteed pay from 8 hours to 7.5 hours. In San Diego, competitive contracting induced workers to eliminate automatic COLAS, increase part-time employment by nearly one-quarter, and lower spreadtime pay penalties. And in Portland, Oregon, Tri-Met drivers agreed to increase the share of part-time workers from 14 percent to 24 percent of the workforce in order to limit Tri-Met's contracting of demand-responsive services. (3) Increased public sector productivity: Competition could also be expected to induce productivity gains in the public sector. For example, transit agencies might be expected to improve their scheduling through practices such as route interlining in order to remain competitive, giving 13 rise to both vehicle and labor productivity gains. Lower rates of absenteeism that resulted from a competitive environment, moreover, would raise productivity by lowering the size of an agency's extra board. In the case of Fort Wayne, Indiana, for example, absenteeism rates dropped dramatically when the public agency began competitively contracting out replacement mm (Cox, 1988). (4) Service innovation: Competition would also likely spur assorted service and fare innovations on the public side. Regional transit agencies, for instance, might attempt to carve out new service niches for themselves by tailoring services to special markets. In the case of suburban settings, this might take the form of developing a timed-transfer networks using business parks and shopping mars for on-site transit centers and connection points. Or differentiated fare structures, such as midday discounts, might be introduced to encourage off-peak ridership. With the discipline of competition, public transit agencies could be expected to devise better ways of delivering and pricing its services to the benefit of everyone. 2.3 Scope of Transit Service Contracting in the U.S. Prevalence of Contracting Because of these expected benefits, competitive contracting of mass transit services is already widespread and is practiced in a number of different contexts. One of the earliest surveys on the magnitude of private contracting in the U.S. transit industry was conducted by the American Public Transit Association (APTA). A 1983 survey of APTA members revealed that: 48 percent contracted out elderly and handicapped services; 27 percent contracted out other revenue services; 41 percent contracted out body repairs; 35 percent contracted out machine overhauls; and 75 percent contracted out a range of administrative functions (Metro, 1985). The most comprehensive survey so far on contracting in the transit field was carried out in late-1985 by Teal, et al. (1986). From a survey of over 1,000 public agencies, the authors found that 35 percent contract for some or all of their services. While the majority of service contracting is for demand-responsive transit, an estimated 22 percent of fixed-route systems nationwide also contract for fixed-route services, primarily peak-only express services. Teal, et al. (1986) found that contracting is most prevalent among smaller systems -- 28 percent of operators with 50 or fewer vehicles contract, compared to only 9 percent of larger systems. When large transit agencies engage in service contracting, it is almost invariably for specialized demand-responsive vans. Accordingly, contracted services nationwide make up only 5 percent of transit operating expenditures and just 9 percent of total service miles. In general, the authors found that contracting is concentrated among municipal and county governments which sponsor relatively small transit operations. Transit agencies with access to federal subsidies are less likely to contract. California leads the nation in transit service contracting -- an estimated 200 of the nation's 375 different public transit agencies contract out some portion of their operations (Teal, 1986). Collectively, however, contracted services account for less than 4 percent of the state's total transit operating budget. Over 80 percent of California's contracts involve demand-responsive services, split evenly between those which are available to the general public and those reserved exclusively for elderly and disabled customers. The contracting of entire fixed-route operations were found to be most common in small areas whereas express service contracting tended to be concentrated around large cities. While far fewer single-purpose transit agencies in California contract than cities and counties, those that do represent over 40 percent of transit mileage operated in the state. Finally, another survey found that the dollar mount spent on transit service contracts in California is significant, totaling over $100 million in 1986 and averaging nearly one-quarter of total operating expenditures (System, 1986). 14 Of the nation's public transit agencies with more than 100 vehicles, only five -- Phoenix, Honolulu, Charlotte, Springfield (MA), and Westchester County (NY) -- are known to contract for all of their services, and the latter two contract with franchise operators (Teal, 1986). Westchester County's is probably the most ambitious program, with over 321 buses being operated by between five and eight private firms (Orski, 1985; Teal, 1985). Where substantial portions of a large transit agency's services are contracted, they tend to be limited to demand-responsive (DRT) services. In California, transit districts in Orange, Riverside, San Bernardino, and San Diego Counties let bids for all of their DRT services. The Orange County Transit District (OCTD) runs the nation's largest DRT system, with five private operators competing annually to operate 130 vehicles. By comparison, few transit agencies in the U.S. contract out any type of fixed-route services, and where such practices do exist they are usually limited to express services. The regional transit authorities in Houston and Dallas engage in the greatest amount of contracting of commuter bus services in the U.S., with each contracting out for the operation of over 75 buses each day. In California, the San Mateo County Transit Authority (SamTrans) has contracted for many years with Greyhound to operate over fifty commuter buses along trunk lines serving one of the main corridors to downtown San Francisco. Farther north in Marin County, Golden Gate Transit has contracted for commuter subscription bus service, involving 15 to 25 vehicles, since the early 1970's. Only two precedents can be found where a public transit agency shifted an existing service from agency operation to contract operation. In 1979, the Tidewater Regional Transit (TRT) agency in the Norfolk, Virginia area contracted out eight previously existing dial-a-ride runs, two fixed route transit services, and a ferry shuttle to private firms. As noted earlier, because TRT's workers conceded to giving up certain work rule privileges and a lower wage scale for drivers of mini-vans, these services returned back to public operation in the mid-1980's. Houston represents the other case where publicly operated runs were bid out to private vendors. Its experiences are discussed in detail in Chapter Six. Because of the dearth of public-to-private switches of fixed route operations, it is difficult to gauge the cost difference of public versus private operations of an identical service. As discussed next, most estimates are based on unit cost comparisons of operating comparable types of services, albeit it is rarely possible to control for actual differences in the operating environment. Cost and Performance Impacts Although largely anecdotal, evidence on the cost and performance impacts of transit service contracting has generally been encouraging, confirming many of the hypothesized benefits discussed earlier in this chapter. Of the studies conducted to date, most have found savings in the range of 10 to 50 percent based on methods ranging from simple comparisons of unit costs to the use of fairly advanced cost allocation models (McKnight and Paaswell, 1984; Teal, et al., 1984; Teal, et al., 1986). Studies show that cost savings depend crucially on the size of the transit agency in question. Giuliano and Teal (1987), for instance, estimated that transit systems of 250 or more systems could save 25 to 50 percent of their costs when contracting out all fixed-route bus services. With 20 percent of these agencies' total service contracted, cost savings would be far less, in the 5 to 10 percent range. The authors note, however, that since few large transit agencies in the U.S. contract out more than 5 percent of their services, there is little empirical evidence to substantiate the benefits that would accrue from meaningful-scale contracting. Demand-Responsive Contracting Most of the evidence on the cost savings of competitive contracting has been documented for demand-responsive (DRT) services. From the literature, the following benefits have been cited 15 for DRT contracting: (*) Following Orange County Transit District's contracting of DRT services, total annual costs of operating over 100 van- size vehicles declined 4 percent within one year (Teal, 1987). In San Bernardino County, since the local transit authority began contracting DRT services in 1980, DRT costs have increased at less than the rate of inflation whereas non-- contracted fixed-route services have risen at over twice the inflation rate (Cervero, 1986), (*) The City of Los Angeles, unhappy the regional transit district's costly downtown shuttle service, replaced it in 1985 with the DASH wide-body van service operated by Diversified Paratransit. The new privately contracted service cost the city $100,000 less per year than what the regional agency charged. Ridership on the downtown circulator increased 20 percent following the changeover to private operations and operating deficits fell 15 percent within one year of letting the new contract (U.S. Department of Transportation, 1986). (*) In Chicago, the Mayor's Office of Senior Citizens competitively bids out special transportation services to private providers at a cost savings of 50 percent to 67 percent over the cost of identical service provided by the Chicago Transit Authority (Johnson and Pikarsky, 1985). (*) Portland's Tri-Met saved an estimated $4.50 per trip from its own cost of about $10 per trip when it contracted out its curb-to-curb service for disabled persons (Bladikas and Berman, 1987). Regular Fixed-Route Contracting Some of the more significant findings of the cost savings of contracting out regular fixedroute services are presented below: (*) San Diego Transit (STD), a city-owned public corporation, competes with five private firms for all services outside of San Diego's city limits. Since 1981 when the bidding process began, SDT's operations have shrunk from 49 to 29 fixed routes. SDT managers maintain that because of competition, the agency's mean salary level has gone from the second to the twenty-seventh highest in the nation, a figure more in line with SDT's relative size and budget (Cervero, 1986). (*) Yolo County, in the Sacramento area, reduced its transit operating costs by 38 percent when it decided in 1982 to procure services from ATE Management and Service Company rather than Sacramento Regional Transit (Morlok, 1985). In 1986, ATE operated 14 buses over six routes at one-half the cost that the County previously paid Sacramento Transit. (*) Hammond, Indiana contracted all of its fixed-route transit services in late 1982. The cost savings were large enough -- 41 percent in the first year -- that the city decided to expand the system. The combination of fare changes and improved service also led to a 33 percent one-year increase in ridership (Orski, et al., 1985). (*) In 1986, Fort Wayne, Indiana's transit agency began contracting with a private company to supply drivers for some of its fixed routes. Within two years, the cost per mile declined 27 percent (in real dollars) and patronage increased by more than 40 percent (Cox, 1988). (*) During 1986-1987, Carson, California contracted its six route local transit system for under $25 per hour, nearly two thirds less than the estimated hourly cost that the Southern California Rapid Transit District (SCRTD) incurs in operating local fixed-route services (Cox, 1987). 16 (*) Fairfax County, Virginia is saving over 60 percent by competitively contracting its local bus system instead of obtaining service from the Washington Metropolitan Area Transit Authority ($2.00 per vehicle mile versus $3.37 per vehicle mile) (Cox, 1987). Express and Commuter Bus Contracting Since this research project addresses the cream-s g debate primarily with reference to peak-only express services, it is revealing to also look at the evidence on the cost savings and overall benefits related to contracting out these services. Some of the major findings to date are presented below. (*) Golden Gate Transit (GGT) saves an estimated 25 percent by procuring subscription bus services from private companies rather than operating them in-house, in large part because the $7 hourly wage rate (1985 dollars) earned by most contractor drivers is considerably less than what GGT pays its drivers (Teal, 1985; Cervero, 1986). (*) Since 1974, the San Mateo County Transit District (SamTrans) has contracted peakexpress bus services out to Greyhound. These contracted service represent about 22 percent of SamTrans's peak bus fleet and total operating expenditures. The 1986 contract rate was $2.75 per vehicle- mile of service, compared to a $3.05 per vehicle-mile cost encumbered by SamTrans itself (Cianfichi, 1986). (*) Los Angeles County competitively contracts for a peak express service to the Santa Clarita Valley. Costs are 57 percent less than publicly operated services, with subsidy savings of 73 percent (Orski, et al., 1985; Cox, 1987). (*) The most transit-dependent city in the U.S., New York, encourages private firms to operate express services. These firms not only provide more frequent and more comfortable services than city-operated ones, but they do so at a profit, without any government aid (Walder, 1985; Morlok and Viton, 1985). (*) Snohomish County, Washington, recently entered into a long-term contract with a private operator for a fifty-bus commuter service previously obtained from the Seattle regional transit authority. Although service levels have remained constant, the county was able to reduce its annual expenditures for these services by more than 50 percent (Teal, 1986; Ringo, 1988). (*) The Dallas Area Rapid Transit (DART) has estimated that the contracting for labor and maintenance from Trailways to operate commuter express services saves the agency around $10 million per year (Guskind, 1987). (*) Houston Metro is saving an estimated 34 percent by competitively contracting out its park-and-ride express services. The full cost of contracted park-and-ride service, including capital and administration, is $4.66 per revenue mile compared to $7.07 per revenue mile for internally produced service (in 1986 dollars). Over the long run, Metro estimates they will save $27 per revenue-hour contracted (Sheehan, 1986; Cox, 1987). (*) In the United Kingdom, competitive bidding of inter-city bus services was estimated to have reduced operating costs by an average of 20 percent within the first year of deregulation (Gomez-Ibanez and Meyer, 1987). It should be re-emphasized that making cost comparisons before and after contracting or between public and private agencies for comparable services can be a hazardous undertaking. Vehicle types and service quality very often vary. Costing and accounting procedures also tend to 17 differ among public and private entities. Moreover, while the vehicle purchases of public transit agencies are heavily subsidized, such is not usually the case for private this. In addition, the length of driver experiences and training are not always comparable between public and private firm. Certainly differences in levels of unionization also account for significant cost differences in many cases. Notwithstanding these caveats, the preponderance of evidence clearly suggests that even modest levels of competitive transit contracting have been associated with significant cost savings. To the matter of whether there appears to be any grounds to the "cream-skimming" argument against competitive contracting that might discredit the purported benefits described so far, we turn to the next chapter. Note 1. The statistics presented in this section are drawn mainly from the American Public Transit Association (1987) and prior editions of the Transit Fact Book. 18 Chapter Three Profitability of Public Transit Operations in the U.S. 3.1 Introduction: Previous Work on the Profitability of Transit Operations As discussed in the first chapter, the cream-skimming argument against competitive contracting of transit services rests on the belief that there are profitable bus routes in the U.S. While there has been little rigorous research on this question to date, the conventional wisdom is that there are very few bus operations which cover their fully allocated costs.1 Pickrell (1986) addressed this very question in an earlier analysis of the cream-skimming controversy. Pickrell compared revenues to costs for twelve different public transit operators across eight metropolitan areas during the period stretching between 1979 and 1984. The author concentrated on the profitability of services by examining whether revenues exceeded costs for a number different services types - express, in-town peak and off-peak, suburban peak and off-peak, cross-town peak and off-peak, radial peak and off-peak, and commuter rail. Pickrell (1986, p. 26) found that none of the categories of transit services produced farebox revenues sufficient to cover day-to-day operating expenses and thus concluded that "there is apparently very little if an "cream to skim" from current public transit operations". While Pickrell's work has raised serious doubts about the profitability of public transit operations in the U.S., it alone has not been able to resolve the cream-skimming debate for several reasons. One, Pickrell's sample size was fairly small, spanning transit operations in eight major cities -- three in California, four in the northeast, and one in the midwest. Since the author relied largely on cost models developed by other researchers to generate cost estimates, his analysis was restricted mainly to cases where there were published details on cost equations. Second, Pickrell studied profitability for types of services rather than individual routes. That is, he lumped together revenue and cost records for all routes under a particular service type and drew an inference about the profitability of each service type. While this provided valuable insights into the general cost recovery levels of types of services, it said little about the profitability of individual routes. For the most part, contracting of fixed-route services is done on a route-by-route basis, enabling transit managers to judiciously select which bus runs to contract out (Teal, et al., 1986). Rarely is an entire set of services contracted out carte blanche. In large, dense cities where the potential for breaking even or generating a profit might be the highest, the general tendency has been for transit agencies to competitively bid out individual routes on a periodic basis to multiple vendors. By looking at the profitability issue for individual bus routes for a larger number of transit properties in the U.S., it is felt that the analysis presented in this chapter provides a refinement to that presented by Pickrell. While there appears to be no published evidence to date of profitable public transit operations in the U.S., several studies have found private operators of conventional bus services who clear a profit (Morlok and Viton, 1980, 1985; Walder, 1985; Urban Mobility Corporation, 1985). Morlok and Viton (1985) found 19 express bus routes operated by private carriers in the New York metropolitan area which generated profits ranging from 15 percent to 24 percent in 1972. AR 19 routes operated over long distances with highly peaked traffic, the very services that public operators have been historically shown to incur their highest deficits (Oram, 1979; Cervero, 1982b). Based on an assumed range of fares and service levels, Morlok and Viton concluded that operators of conventional large buses, with the same costs as existing publicly owned operators, could operate a service profitably at corridor passenger volumes down to 2,000 passengers per hour during typical peak periods. A more recent study by the Urban Mobility Corporation (1985) further confirmed the 19 potential profitability of operating peak, express services. Using fully allocated costs, the authors of the report found that contrary to common belief, un-subsidized express commuter bus services were functioning in at least ten metropolitan areas in the U.S., including New York, Chicago, Los Angeles, Boston, and Norfolk. They estimated that if all peak services operated by seven of the jurisdictions under study were converted to private operations, over $27 million in public subsidies could be saved annually. In general, both of these above-cited studies concluded that self-sustaining commuter bus operations were feasible when the following conditions existed: 1) high demand levels, normally involving load factors of 80 percent or higher; 2) little short- term demand fluctuation; 3) long-haul runs, typically 15-20 miles in each direction with few intermediate stops; and 4) a high concentration of trip destinations, such as a central business district or a large suburban employment center. At least several studies conducted to date thus concluded that their are a number of privately operated peak-only, commuter bus services in the country that more than cover their fully allocated operating costs. Ironically, these peak-hour, express services are the very ones that public transit agencies are commonly felt to incur their highest deficits with (Oram, 1979; Morlok and Viton, 1982; Cervero, 1982b). The clear inference, then, is that the competitive contracting of peak, express services to private firms would likely result in the conversion of high-deficit services into profit generators in a number of instances. To the matter of whether there are many public transit routes in the U.S. from which it is conceivable that profits could be skimmed away, we turn to the remainder of this chapter. 3.2 Specifying Profitability Defining Revenues Versus Costs At its simplest level, profitability exists when a transit agency generates revenues sufficient to cover its direct operating costs. Normally, this means an agency receiving enough farebox, advertising, and other receipts to offset its outlays for labor compensation, fuel, materials, and other factor inputs as well as to cover the debt and depreciation it incurred for capital expenditures. Historically, U.S. transit properties have received federal and state grants that cover as much as 80 percent of the purchase cost of rolling stock and other capital acquisitions. To a large extent, then, capital purchases are considered to be one- time, sunk investments that are largely subsidized by it others" and thus are generally ignored by agencies when assessing the fiscal performance of services. Similarly, overhead expenses, such as for administration, are also usually perceived as sunk and unavoidable by transit agencies, in large part because union pressures and the self survival instincts of transit managers retard efforts to cut back administrative staffs and overhead expenses even when services are reduced, whether through private contracting or the elimination of high-deficit services. In recognition of these realities, this study examines profitability in terms of the degree to which farebox revenues exceed the direct, day-to-day operating expenses that are not sunk and are avoidable should services be contracted out. Adopting the convention used by Pickrell (1986), direct operating expenses include labor, energy, and materials costs for operating and maintaining vehicles as well as use-related depreciation and debt service of any vehicles used in providing services. All outlays for fixed facilities (e.g., repair garages, headquarters) as well as all expenses for administrative functions (e.g., accounting, planning, supervision) are excluded in this analysis. As Pickrell notes, this more conservative definition of costs most closely match the expenses that would instantaneously be eliminated if a transit agency contracted out a specific set of services to a private vendor. For example, if a transit agency contracted out the operation and maintenance of buses used on two specific routes, it would immediately save the labor, fuel, and supply expenses of running these buses, along with the ability to reduce its outlays for new vehicle purchases by reassigning buses used on these contracted routes to new services or deploying them as backups. The agency's count of administrative workers and its inventory of garages and other fixed facilities 20 would likely be unaffected by the private takeover of these services.2 In many ways, then, this approach to cost estimation is geared toward evaluating the "short-term" potential cost-savings impacts of competitive contracting. It should be emphasized that this approach to defining cost is generous in the sense that it is favorable to showing that some publicly operated services are profitable. To the extent the of other" cost categories were included in the analysis, then the likelihood of an agency generating operating revenues which exceed costs would be less. Revenue-Cost Ratios Versus Absolute Differences The business world commonly views profitability in terms of returns on investment -revenues received as a percent of all variable and appropriately amortized fixed cost inputs. Thus, a 20 percent return on investment for any given time period means that revenues earned exceed input costs by one-fifth. Transit managers use a similar index in gauging the financial solvency of their operations, usually expressed as an "operating ratio" -- operating revenues divided by operating costs (typically absent any capital debt service or depreciation). In 1985, the average operating ratio for America's transit industry as a whole was 0.446 -- nearly 45 percent of total operating costs were recovered from fares, advertising, rents, and other sources.3 Since this ratio indicates the share of operating expenses recovered from major revenue sources, the term "cost recovery rate" is also sometimes used. Since this latter term more closely captures the idea of recovering operating expenses, it is adopted in the remainder of this report. As used here, however, only passenger revenues are considered in order to reflect the share of direct, day-to-day operating costs recovered from the farebox. A point of confusion that perpetuates the cream-skimming controversy is that systemwide cost recovery rates may increase following private take-over of services. Even when this is the case, as long as contracted services fail to meet their cost, the relinquishment of these services would be financially beneficial to the public sector, lowering total deficits. Thus is a misconception related to using cost recovery rates as a barometer of fiscal health. Just because these rates fall does not necessarily mean that a transit property is financially worse off. On the contrary, to the extent an unsubsidized private firm takes over deficit services, both public and private sectors benefit. Accordingly, the more appropriate gauge of the fiscal implications of competitive contracting is the change in the total deficit level. To pursue this point a bit further, consider transit agency XYZ which operates 50 express routes and 50 local routes. To keep things simple, suppose each route costs $1,000 per day to operate, producing a daily total cost of $100,000 for XYZ to deliver its services. Let's further assume that all routes carry 700 riders per day. Express routes correct $1 from each passenger whereas local routes charge only 50 cents per ride. Under this scenario, we find that $52,500 is generated each day from the farebox, producing a cost recovery rate of 0.525 and a daily deficit of $47,500. Furthermore, there appears to be a differential recovery rate: express services return 70 percent of their costs through the farebox while local services recover only 35 percent. Given these numbers, one could well understand why transit managers might be hesitant to contract out their express services. The fact remains, however, as long as express services are sustaining losses, the changeover to unsubsidized private operations would still help curb deficits. Figure 3.1 suggests by approximately how much. The graph shows the trade-off between XYZ's cost recovery rate and total operating deficit under a number of scenarios involving the contracting out of express services. The numbers next to the line in the graph signify the percent of express services that are presumed to be contracted. Thus, if 30 percent of XYZ's express services were taken over by a private firm, its cost recovery rate would fall to under 0.50 and its total daily deficit would likewise drop to $44,500. If 90 percent of express services were contracted out, the graph shows that while the recovery rate would be about 0.38, daily deficits would plummet, to 21 Click HERE for graphic. $34,000. Therefore, the 90 percent contracting scenario would involve a total decline in XYZ's deficit from $47,500 to $34,000, a 28 percent decrease. Conceivably, XYZ could subsidize the private operator which takes over express routes at a rate of $13,499 for all 50 routes (or $269.98 per route) and still reduce its total daily deficit while ensuring both services are provided. It should be noted that under the above scenario, XYZ's changeover from 0 to 90 percent contracting of express runs would result in the agency's deficit per passenger rising from $0.64 to $0.81.4 Again, the inclination of XYZ's transit managers might be against widespread contracting of express services to preserve a lower deficit per rider figure. In reality, however, the apparent decline in XYZ's financial health is likely to be. more of a reflection of the misallocation of total costs among express versus local services. As discussed earlier in the report, numerous studies show that peak-only, express services tend to be far costlier on a unit basis than otherwise comparable all-day, local services because of the labor penalty costs associated with paying peakperiod workers guaranteed 8 hours pay or split shift bonuses (Cherwony and Mundle, 1978; Reilly, 1977; Cervero, 1982a). When such cost factors are properly allocated to peak-period services only, studies indicate that the daily costs of operating express services can increase by a factor of two or more. One study, for example, found that it generally cost 250 percent more to provide a passenger with express service than local service (General Accounting Office, 1981). In the above example, then, cost recovery rates could very well increase and both total deficits and deficit per passengers could decline if costs were reallocated to attribute more of XYZ's labor compensate n expenses to the express services. In short, as long as "press services operate in the red, there is no "cream" to skim- to the extent costs are properly attributed to express services, the cost recovery rates and overall financial picture of public transit agencies could be expected to improve as well. 22 3.3 Investigating the Incidence of Profitability: Methodology Selection of Case Sites Clearly, a prerequisite for making a profit on any transit service is high ridership. By and large, transit services with high levels of patronage are found in fairly large urban areas with enough density and well defined activity centers to create a significant customer base (Pushkarev and Zupan, 1977; Morlok and Viton, 1980). Accordingly, this study limited the case sites to all U.S. public transit properties with 1985 fleets of 75 or more motor buses based on Section 15 published statistics - 76 transit properties in all (Urban Mass Transportation Administration, 1986). Each of these agencies was queried as to whether they operate both peak-only express services and all-day local services. Those which did not provide both types of service were eliminated from further consideration.5 Since it was expected that public transit routes which cover relatively high shares of costs through the farebox were limited to heavily patronized ones and in order to keep the number of data cases manageable, data was sought only on the highest ridership routes for the remaining agencies. Through both written and telephone correspondence, the 76 agencies were asked to provide route-level cost and revenue data for their three highest-patronage peak-only express routes and their three highest-patronage all-day local routes. In all, 25 of the eligible agencies were found to have at least three peak-only express services and to disaggregate operating data on a route-by-route basis. About a dozen agencies which operate peak-only express runs consider these routes to be part of a larger family of routes that revert to local service during off-peak hours. Since none of these agencies maintained separate route statistics for the peak-hour portion of the service, they were also eliminated from further consideration. The 25 case study sites used in the analysis, grouped together in terms of vehicle size classes, are listed in Table 3.1. (See Appendix A for the complete name, fleet size, and headquarters location of each case study site.) In all. a data base made up of 76 peak-only express routes and 76 all-day local routes was created from the 25 U.S. transit properties used in this analysis. The data base consisted mainly of fiscal year 1987 data on average daily operating costs, farebox revenue, ridership, and service inputs (e.g., vehicle miles) for each route. The range of data submitted by the 25 transit properties studied in this analysis and variables that were created from the submitted data are presented in Appendix B. Estimating Route-Level Operating Costs Problems in Measuring Route-Level Costs While fairly accurate records are maintained on the daily revenue receipts collected from the farebox of a transit property's routes, comparable data unfortunately is rarely available on the cost end. In all cases, the route costs submitted by transit agencies were estimates, not actual measured costs. For a host of reasons, transit operators do not maintain records on the actual cost of operating individual routes. One major reason is that since bus drivers accrue different wage rates depending on their years of service, the cost of any one route would be largely dependent upon how driver tours were assigned and rotated. Logically, "average" labor costs should be associated with each route. Additionally, since buses are frequently interlined, or switched from one route to another during the day, assigning maintenance costs to particular routes can become problematic. Furthermore, administrative and supervisory costs cannot be easily apportioned among numerous routes for which a manager is responsible. Even fuel costs are difficult to assign to a particular route; while a route's daily mileage is usually known, varying traffic conditions and ages of vehicles effect the fuel efficiencies of different routes. To compile accurate cost data on each route, moreover, would require a highly elaborate accounting system which would likely be 23 Table 3.1 Listing of 25 Case Sites, by Vehicle Fleet Size Class Vehicle Fleet Size Class Transit Agency > 1,000 Washington Metropolitan Area Transit Authority (Washington D.C., Maryland, Virginia) Southeastern Pennsylvania Transportation Authority (Philadelphia region) 500 - 999 Metro Seattle (Seattle region) Metropolitan Transit Commission (Minneapolis-St. Paul region) Metropolitan Transit Authority of Harris County (Houston region) Denver Regional Transit District (Denver region) Bi-State Development Agency (St. Louis, Missouri, Illinois) 250 - 499 PACE (Chicago region) Santa Clara County Transportation Agency (Santa Clara County, Calif.) Orange County Transit District (Orange County, Calif.) Central Ohio Transit Authority (Columbus region) Utah Transit Authority (Salt Lake City region) Phoenix Transit (Phoenix region) < 250 Westchester County Department of Transportation (White Plains region) Golden Gate Transit Bus Division (San Francisco region) (continued) 24 (continued) Vehicle Fleet Size Class Transit Agency < 250 Southeastern Michigan Transportation Authority (Detroit region) Sacramento Regional Transit District (Sacramento region) Jacksonville Transportation Authority (Jacksonville region) Greater Richmond Transit Company (Richmond region) Tidewater Regional Transit System (Norfolk-Virginia Beach region) Hillsborough Area Regional Transit Authority (Tampa region) SunTran (Tucson region) Fort Worth Transportation Authority (Fort Worth region) Metro Tulsa Transit Authority (Tulsa region) Transit Authority of Northern Kentucky (Fort Wright region, Kentucky, Ohio) Notes: Vehicle fleet size includes motor buses only, as reported in UMTA (1986). Jurisdictions do not cross state lines except where noted. 25 prohibitively expensive and yield somewhat dubious results since cost factors such as driver wage rates vary among routes. For all of these reasons, route-level operating costs are more often than not estimated by breaking down a transit agency's annual operating costs, a practice normally known as "cost allocation". Cost Allocation Approaches Cost allocation methods vary widely throughout the nation's transit industry. To date, no universal set of guidelines has emerged which specifies precisely which costs should be considered. While UMTA has established guidelines for its annual Section 15 reporting program, they only govern the collection and maintenance of systemwide data. Typically, transit agencies estimate the cost of a specific route on an ad hoc basis whenever a service change is being contemplated. Route-level cost allocation methods range from simple unit cost estimates based on a single variable to fairly complex multivariable models (Miller and Rea, 1973; Levinson, 1978; Cervero, 1982a). Typically, expense items are segregated into subcategories such as labor, maintenance, and fuel. Subcategories are then stratified among several variables, such as vehicle hours or vehicles miles of service, which are considered causally linked to the encumbrance of expenses in each subcategory. A multivariable equation can then be derived by calculating a unit coefficient for each factor of production (e.g., by dividing the total cost of all subcategories by, say, vehicle miles). Single-variable cost estimates are of limited usefulness in comparing routes within a transit agency. Since a simple average cost is derived, these estimates can mask significant variations in the cost structures of different services, such as peak-only express and all-day local services. The most common approach is to estimate costs based on two variables. Table 3.2 presents a typical two factor model based on vehicle miles and vehicle hours as input measures. Since both miles and hours of service are determinants of average speeds, two variable equations can reflect differences in the operating environments of various routes. As shown in the hypothetical model presented in Table 3.3, the three factor equations typically add a route's count of peak vehicles as the third predictor of costs. This is done chiefly to account for such overhead costs as administration, insurance, and physical plant, all of which normally vary most closely with fleet size rather than with miles or hours of operation. The choice of a cost model can obviously significantly affect route cost estimates. In general, estimates are most sensitive to the choice of cost models when services are quite heterogenous; where a particular route is similar to an agency's "average" route, different cost models have been found to produce fairly similar results (Cherwony and Mundle, 1980). Besides the number of allocation variables that should enter into an equation, opinions often differ on which costs should be associated with which input factors. Almost without exception, drivers' wages and benefits are assigned to the vehicle hour factor. Mechanics' pay, fuel, lubrication, and tire expenses are almost always assigned to vehicle miles. Research, however, has shown that fuel and lubrication costs vary more as a function of the number of vehicles in a fleet than with vehicle miles traveled (Gephart, 1984; Purdy and Wiegmann, 1986). Finally, not all transit agencies include the same cost components in their models. Some properties include depreciation, debt service, and other costs of vehicle ownership in their models while others don't. And while most include administrative expenses and the cost of casualty and liability insurance in their accounts, a few exclude them under the belief that they are not expenses directly encountered in running buses in the literal sense. Notwithstanding these differences, the majority of transit properties typically use similar types of cost models with similar cost components and similar attribution rules. 26 Table 3.2 Typical Two-Variable Cost Allocation Model Annual costs associated with each variable Allocation Vehicle Vehicle Cost Subcategory Variable Hours Miles Vehicle operations Hours $10,000,000 Transportation admin. Hours 500,000 Vehicle maintenance Miles $4,000,000 Maintenance admin. Miles 600,000 Total annual costs: $10,500,000 $4,600,000 Total annual service inputs: 400,000 6,000,000 vehicle hours vehicle miles UNIT COSTS: $10,500,000 / 400,000 vehicle hours = $26.25 per vehicle hour $4,600,000 / 6,000,000 vehicle miles = SO.77 per vehicle mile COST EQUATION: ($26.25 x vehicle hours) + ($0.77 x vehicle miles) 27 Table 3.3 Typical Three-Variable Cost Allocation Model Annual costs associated with each variable Allocation Vehicle Vehicle Peak Cost Subcategory Variable Hours Miles Vehicles Vehicle operations Hours $40,000,000 Transport. admin. Hours 200,000 Scheduling Hours 100,000 Vehicle maintenance Miles $10,000,000 Maintenance admin. Miles 150,000 Accident repair Miles 50,000 Servicing vehicles Vehicles $2,000,000 Fare Collections Vehicles 100,000 Insurance Vehicles 1,000,000 Total Annual Costs: $40,300,000 $10,200,000 $3,100,000 Total Annual Service Inputs: 500,000 8,000,000 275 vehicle hours vehicle miles peak vehicles UNIT COSTS: $40,300,000/ 500,000 vehicle hours = $80.60 per vehicle hour $10,200,000/ 8,000,000 vehicle miles = $1.27 per vehicle mile $3,100,000/ 275 vehicles = $11,273 per vehicle (annual); then $11,273/ 365 service days = $30.88 per vehicle (daily) COST EQUATIONS: ($80.60 x daily vehicle hours) + ($1.27 x daily vehicle miles) + ($30.88 x peak vehicles) 28 Cost Models Employed As discussed in section 3.2, cost estimates were derived for this analysis to reflect the short-term cost impacts that public transit agencies might experience from contracting out their services. This meant the exclusion of administrative overhead costs and all capital expenditures except those directly related to usage, such as the depreciation on rolling stock. The inclusion of use-related depreciation reflects the costs that public transit agencies could save by foregoing new vehicle purchases or redeploying vehicles to other routes when services are contracted and where private operators provide their own vehicles. In order to maintain consistency in the estimation of costs, a uniform cost allocation model was developed for each of the 25 transit properties which were studied. The three input factors used in estimating costs for each of the 25 agencies were: (1) daily total (in-service and out-of-service) vehicle miles; daily total (in-service and out-of-service) vehicle hours; and daily number of vehicles operating during the peak period. The approach used is a variant of the cost allocation methodology developed by Mundle and Associates for an UMTA research project on private contracting carried out by the Urban Mobility Corporation (1985).6 Under this approach, the three input factors are assumed to be causally linked to the encumbrance of the following costs: (1) Vehicle Hours: transportation administration; revenue vehicle movement control; scheduling of transportation operations; and revenue vehicle operation (primarily drivers' wages and fringe benefits). (2) Vehicle Miles: vehicle maintenance administration; inspection and maintenance of vehicles; accident repairs to vehicles; vandalism repairs to vehicles; and maintenance of vehicle movement control systems. (3) Peak Vehicles: servicing revenue vehicles (chiefly fuel and lubrication); maintenance of fare collecting equipment; ticketing and fare collection; and insurance. All of these costs would likely be absorbed by private contractors which took over both the operation and maintenance of certain services and used their own vehicles. Examples of initial cost models that were derived using these cost allocation rules for two transit agencies included in the analysis -- Seattle Metro and Denver's Regional Transit District (RTD) - are presented in Appendix C. Computing a three-factor cost model for each agency was a data intensive process. A few agencies provided all of the required cost details based on their Level A submissions to UMTA's Section 15 "Uniform System of Accounts and Records and Reporting Systems". However, Level A submissions, which contain the maximum amount of detail requested by UMTA, are not required and thus are voluntarily provided. For those remaining transit properties which had less detailed cost data, more precise breakdowns were estimated from data tapes made available by the U.S.D.O.T.'s Transportation Systems Center.7 The final adjustments made to each agency's model were the exclusion of general overhead administrative costs and the inclusion of use-related depreciation expenses.8 Again, the omission of administrative and other overhead expenditures reflects the fact that these expenses would unlikely decline in proportion to any reduction in vehicle requirements that resulted from the contracting out of services, at least in the near term. Over the long run, such expenses n-Light fall, although probably no faster than the natural rate of employee attrition. The inclusion of use- related depreciation reflected the fact that buses normally depreciate directly in proportion to the amount of miles they log and these mileage-related expenses can be avoided through contracting. A use-related depreciation cost of 37.5 cents per mile was included in the vehicle mileage factor of each model based on findings from research by Pickrell (1986).9 This figure was based a straight-line depreciation calculation assuming a new bus costs $150,000 with a useful service life of 400,000 29 miles and adopting a social discount rate of 7 percent. When refining cost models, researchers sometimes also use a peak cost adjustment factor to more accurately reflect differences in the effective wage rates and productivity levels of bus drivers during peak versus off-peak periods (Cherwony and Mundle, 1978; Cervero, 1982a). This factor acknowledges that labor costs, although paid at a standard hourly rate, effectively vary by time- of day since peak work activities lead to more spread-time and over-time duties, which result in more pay hours per vehicle hour of operation. It also reflects the fact that higher levels of deadheading and work rule restrictions which limit the number and duration of split shifts raise hourly costs during the peak. Collectively, these factors have been shown to raise the cost of peak period operations by around 20 percent (Cherwony and Mundle, 1978; Cervero, 1982a). In order to take a more conservative approach, however, this study did not divide cost estimates into peak and off-peak components and thus did not apply a 20 percent peak inflation index. To do so would have worsened the financial performance of peak-only express services relative to all-day local operations. As mentioned previously, since these cost differentials are rarely directly acknowledged by transit officials when considering the potential financial impacts of contracting out peak services, we felt it would be most appropriate to exclude them in this analysis. Nonetheless, if it can be shown that peak-only express services are outperformed even when these cost adjustments are ignored, no one can challenge the analysis on the basis that the assumptions of the cost models were "stacked against" peak-only express services. The final cost models developed for each of the 25 case study transit properties used in this study are presented in Appendix D. In addition to presenting the three factor cost equations computed for each transit property, Appendix D also shows the average daily cost estimates derived for the three highest-patronized peak-only express routes and the three highest-patronized all-day local routes of each agency.10 3.4 Comparison of Route Characteristics Among Express and Local Services Before presenting the findings on the level of profitability among the transit routes studied, it is instructive to first compare the operating characteristics of these routes. Table 3.4 presents some of the operating features of the 76 express routes and 76 local routes in terms of the daily average: vehicle miles, vehicle hours, number of peak vehicles in operation, and travel speeds." Table 3.4 indicates that the all-day local routes that were studied average more than three times the daily vehicle miles as their peak-only counterparts and nearly six times the hours, although they use only twice the number of buses. As expected, average speeds of express routes are significantly greater than those of local routes. The slower average speeds of local routes reflect the more frequent stopping and closer spacing of stops along these routes in addition to the greater reliance of express services on limited-access freeways for the line-haul portions of the journey. When daily hours and miles are used as output measures, local services average a significantly higher rate of vehicle productivity. Based on the standard deviation statistics (in parentheses), there is far greater variation in all of these indicators (with the expectation of average speeds) for express than local services. Breaking these statistics down by fleet size is further revealing. Table 3.5 shows that larger transit systems tend to operate both local and express services at slower speeds, ostensibly because of the higher levels of congestion in which most big bus systems operate in. Additionally, the table shows that while the average number of miles, hours, and peak vehicles increase with fleet size among all-day local services, there really is little difference in these characteristics among small and mid- size operators of peak-only services. Only among properties with 500 or more vehicles does the scale of express services appear to be significantly higher. 30 Table 3.4 Comparison of Average Daily Route Characteristics Among Peak-Only Express and All-Day Local Routes Mean Mean Mean Mean Vehicle Vehicle Peak Speed Service Miles Hours Vehicles (m.p.h.) Peak-only express 573.25 27.66 7.59 20.02 (519..54) (23.28) (7.10) (5.32) All-day local 1,951.92 158.63 15.51 12.90 (1,147.81)(102.56) (10.56) (3.16) NOTES: Figures in parentheses are standard deviations. Differences between means for each variable are significant at the .01 level. For peak-only express, N = 76; for all-day local, N = 76. 3.5 Level of Profitability Merging the daily passenger revenue and estimated operating cost of each route studied yielded an index of profitability -- a cost recovery rate. As discussed earlier in this chapter, a recovery rate above one indicates a profit is being generated while a rate below one signifies that a deficit is being incurred. Table 3.6 lists the 24 high-patronage routes which were found to recover at least three-quarters of operating costs through the farebox. In all, only 10 of the 152 routes examined in this study, or 6.6 percent, were found to either make a profit or break even. These ten routes were split among five of the 25 case study transit agencies. Two of these agencies operated the largest bus systems studied: the Southeast Pennsylvania Transit Authority (SEPTA) serving the greater Philadelphia area, with three profitable all- day local routes; and the Washington Metropolitan Area Transportation Authority (WMATA), also with dime money-makers. Minneapolis's Metropolitan Transit Authority (MTA) also was found to operate a profitable route. While all three of these transit properties maintain relatively large operations, the other two systems registering a profitable service -- Greater Richmond Transit (GRT) and Westchester County (NY) Transit -- are comparatively small. GRT operates one money-making express service, another express service that breaks even, and three local services that are nearly profitable (with cost recovery rates exceeding 0.90). While Westchester County was found to operate a profitable local route, it is noteworthy that this route (along with all other Westchester County services) is operated by a private franchiser. 31 Table 3.5 Comparison of Average Daily Route Characteristics by Fleet Size Mean Mean Mean Fleet Vehicle Vehicle Peak Speed Size Route Miles Hours Vehicles (m.p.h.) Under 250 Peak 430.58 17.93 5.29 21.30 (519.66) (17.63) (5.29) (6.14) Local 1,318.59 90.03 8.76 14.23 (770.43) (4039) (3.89) (3.34) 250-499 Peak 412.48 22.07 5.14 18.44 (283.67) (13.67) (2.06) (4.34) Local 2,328.62 169.28 15.95 13.49 (1,266.11) (93.52) (7.57) (1.31) 500+ Peak 898.21 45.12 12.71 20.07 (544.74) (26.92) (9.19) (4.72) Local 2,600.62 259.05 26.00 10.17 (1,041.82) (96.40) (11.96) (2.35) Notes: Figures in parentheses are standard deviations. Differences between means within each variable and fleet size group are significant at the .01 level. For fleets under 250: N = 31 for peak, N = 34 for local. For fleets 250 - 499: N = 21 for peak, N = 21 for local. For fleets of 500 +: N = 24 for peak, N = 21 for local. 32 Table 3.6 Bus Routes With Cost Recovery Ratios Above 0.75 Deficit Cost2 (Profit) Transit1 Service Recovery Per Agency Route Name Ratio Rider WMATA Local Benning Road 2.21 $(0.44) SEPTA Local 33 1.65 (0-30) SEPTA Local 52 1.52 (0-26) SEPTA Local C 1.44 (0.23) WMATA Local Pennsylvania Avenue 1.37 (0-22) WMATA Local Georgia/ 7th Street 1.27 (0.17) Richmond Peak 26 (Parham Road) 1.27 (0.27) Westchester Local 20 1.15 (0.09) MTC Local 21 1.10 (0.06) Richmond Peak 67 (Chappenham) 1.00 (0.00) Richmond Local 37 (Chamberlayne) .98 0.01 Tidewater Local 20-25 .95 0.04 Richmond Local 34 (Highland Park).94 0.03 Richmond Local 6-53 Broad/Main .92 0.05 Houston Peak 212 Seton Lake .90 0.18 Houston Peak 205 Kingwood .88 0.30 Westchester local 1 .87 0.12 MTC Local 18 .86 0.11 MTC Local 5 .82 0.15 WMATA Peak Lincolnia/ North Fairfax .81 0.25 BI-STATE Local 3070 Grand .80 0.11 TANK Local 6 .78 0.18 WMATA Peak South Capitol Street .77 0.24 Westchester Local 40 .77 0.21 Note: 1 See Appendix A for identification of transit agencies. 2 Cost recovery ratio = passenger revenues divided by estimated operating cost. 33 By far, the most profitable route studied was WMATA's "Benning Road", an all-day, local service that covers more than twice its costs through the farebox at an estimated profit of 44 cents per passenger. The next most profitable route was SEPTA's "33", also an all-day local service which clears around 30 cents per passenger. Aside from Greater Richmond Transit, no other transit agency studied operated any money-making express routes. Richmond's express route "26" was found to make a profit of around 27 cents for every rider carried.12 Factors which might account for the profitability of these and other money-making routes listed in Table 3.6 are discussed in Chapter 5. In summary, very few of the public transit routes which were studied were found to clear a profit. Given that the 25 case study transit systems had a combined total of 1,310 fixed routes, then the 10 profitable routes comprised less than one percent, or .0076, of total routes in operation. It bears repeating that the cost models used were fairly conservative. If the administrative and total capital costs attributable to each route were included the analysis, then the number of profitable routes would have likely been far less. Moreover, peak-only express services would likely have shown even poorer financial performance had a 20 percent peak period cost penalty been attached. Thus even when the most conservative approach to estimating costs is adopted, fewer than one percent of the nation's fixed-route transit services are estimated to make a profit or break even. 3.6 Summary Comparison of Performance Among Local Versus Express Routes Since the cream-skimming argument reflects a hesitancy to allow private operators take over peak-express routes, it is insightful to see how these services fare relative to all-day local ones in terms of various performance indicators. Averaging among all 76 peak routes and all 76 local routes in the data base, Table 3.7 presents this comparison. Table 3.7 shows that all-day local services outperformed peak express-only services on virtually every measure.13 The highest- patronage local services, for instance, covered around 38 percent more of their costs through the farebox -- 0.58 versus 0.42. The differential would have doubtlessly been higher had cost penalties been incorporated into the costs of peak-only express services. Furthermore, the average cost per hour for peak-only routes was 45 percent higher than that of all-day local routes. Local routes, moreover, average around twice as many passengers per dollar of expenditure and only about one-half the subsidy per rider as peak- only routes. The only measure in which peak-only express routes outperformed all-day local ones is when average costs are expressed on a per-mile basis. On average, the total costs per mile are around 13 percent less for peak versus local services. This difference obviously reflects the slower average speeds of most local routes. With slower speeds, it ends up costing a transit agency more to pay a driver to cover ten miles on a surface street as opposed to ten miles on a freeway flier. Maintenance costs also tend to increase in the stop-and-go traffic conditions experienced by local routes. Regardless, it is noteworthy that the average costs per mile differ only by 13 percent even though the average speeds of express services were previously shown to be (in Table 3.4) over 55 percent as fast. 3.7 Summary After adopting a fairly conservative approach to estimating the costs of the highest-patronized routes of 25 transit agencies across the U.S., it was found that very few routes produce farebox revenues sufficient to cover even the direct, day-to-day operating expenses incurred by public agencies. It is estimated that less than one percent of all fixed-route services currently operated in the U.S. today either cover their direct costs through the farebox or return a profit. On the whole, there is very little potential "cream to skim" from public transit agencies under any competitive contracting scenario. 34 Table 3.7 Comparison of Average Performance Characteristics Among Express versus Local Routes Overall Financial Performance Deficit Cost Per Recovery Service Rider Ratio Peak $1.62 .42 Local $0.75 .58 Effectiveness Measures Riders Riders Revenue Riders Per Per Per Per Service Mile Dollar Mile Vehicle Peak 1.34 0.51 $1.09 87.73 Local 3.21 1.04 $1.83 381.73 Efficiency Measures Cost Cost Cost Miles Per Per Per Per Service Mile Hour Rider Vehicle Peak $2.58 $50.05 $2.49 78.54 Local $2.96 $36.72 $1.22 136.36 Notes: All figures are mean values. For peak-only express, N = 65; for all-day local, N = 76. 35 On average, the 152 highest-patronized routes analyzed in this study recovered only 50 percent of their direct daily operating costs. The cost recovery rates of peak-only express services tended to fall appreciably below those of all-day local services. On average, the highest-patronized express services incurred a deficit of $1.62 for every passenger carried. It seems ironic, then, that deficits are so high on the very services that public transit agencies seem most reluctant to give up under the banner of "cream-skimming". Equally ironic is the fact that peak-only express services are exactly the market niche that private firms have shown the most interest in assuming and which research has shown can be operated at a profit by private entrepreneurs. This finding clearly lends credence to the counter-argument that competitive contracting would, almost without exception, involve deficit-skimming rather than cream-skimming. To probe the argument even further with regards to whether or not scale economies exist, we turn to the next chapter. Notes 1. Fully allocated costs represent the total cost incurred in producing a specific product or delivering a specific service, including direct costs of labor, capital, and material resources and a portion of the shared cost of labor, capital, and materials used in the delivery of the range of services produced by an organization. Adopted as pan of UMTA's Private Enterprise Policy (Federal Register, Volume 45, No. 205), these costs have been defined by Price Waterhouse (1987). 2. In the long run, the potential cost savings of contracting carried out on a large scale could create increments of savings to the public sector that are "lumpy" and substantial enough to induce a scaling back of administrative functions and the overall physical plant. 3. Total operating revenues were $5.378 billion while total operating costs were $12.057 billion (American Public Transit Authority, 1987). 4. $0.64 = $47,500/(35,000 express + 35,000 local) passengers. $0.81 = $34,000/(7,000 express + 35,000 local) passengers. 5. No precise limits were set as to the number or spacing of stops for express as opposed to local services. Classifications of routes as either express or local were based simply on the designations given by each transit agency. 6. The primary variation was the attribution of some bus servicing costs (chiefly fuel and lubrication) to the peak vehicle rather the vehicle miles factor. 7. These disaggregate cost estimates were derived by breaking down Section 15 Level B, C, and R submissions according to pro- rated Level A cost subcategories among peer groups of U.S. transit properties. For instance, Level R reporting agencies (who furnish only the minimum required information) only provide data on total maintenance costs and not the vehicle servicing cost components. Servicing costs had to be estimated in order to assign them to the peak vehicle variable. Detailed data from Level A agencies with fleet sizes of 500 to 1,000 vehicles revealed that servicing costs comprised, on average, 14.8 percent of total maintenance expenses. Using this average figure, servicing costs were subtracted from the reported maintenance expenses of Level R reporting transit systems and assigned to each agency's peak vehicle variable. 8. Overhead costs related directly to vehicle maintenance were retained to reflect the conversion of vehicle maintenance functions to private firms under competitive contracting. 36 9. This figure was adjusted slightly to reflect 1987 dollars. While this adjustment accounted for vehicle depreciation, no adjustment was made for capital interest expenses on the grounds that vehicle purchases are heavily subsidized and thus these costs are effectively hidden from the ledgers of many transit agencies. Although depreciation related expenses can be lowered by foregoing or delaying new vehicle acquisitions when existing services are contracted, the interest payments on prior purchases are still incurred. 10. Cost models are presented in Appendix D in the rank-size order of the transit agencies listed in , with the exception of the first two examples. The cost models of these two cases Bi-State Development Corporation in the St. Louis area and the Westchester County (NY) Department of Transportation -- are shown in more detail to reveal exactly how final cost equations were derived. 11. Throughout the remainder of this report, both vehicle miles and vehicle hours are expressed as aggregate totals (both in- service and out-of-service). 12. Greater Richmond Transit registered a higher average cost recovery rate than any other agency studied, ostensibly because it is run by a private management firm with a cost reduction mandate. See the case studies in Chapter 5 for further discussions of the Greater Richmond Transit agency . 13. An "effectiveness" measure reveals how well resource inputs are translated into measures of service utilization (such as ridership and revenues). An "efficiency" indicator translates service inputs (e.g., costs, vehicles) into service outputs (e.g., miles, hours). See Fielding, et al. (1978) for further discussions on these indicators. 37 Chapter Four Probing the Scale Economies Argument Against Contracting 4.1 Past Research on Economies of Scale in the US. Transit industry As discussed in the first chapter, another argument sometimes voiced against competitive contracting is that transit properties enjoy economies of scale that can only be reaped by limiting the number of entrants into the urban mass transportation market. To the extent that public transit functions as a natural monopoly, its average or unit costs should decline with increasing volumes of output and accordingly a single entity should be able to most efficiently provide the service.1 Such has been the logic historically used to shield public transit agencies from direct head-to-head competition from private taxi and bus companies. While early research conducted on the existence of economies of scale in the transit industry was inconclusive, more recent work suggests there are generally declining returns on investment for larger transit systems. One of the first studies demonstrating scale economies in the transit industry was conducted by Wells, et al. (1972). Using national data from 1960-1969, the authors found tendencies toward transit scale economies by finding that the cost per mile declined as vehicle mileage increased for ten of eleven American transit systems studied. Lee and Steedman (1970) likewise revealed decreasing unit cost characteristics among larger British transit systems during the same approximate time period. Most subsequent studies, however, have revealed the opposite. Studies by Koshal (1972), Wabe and Coles (1975), and Fravel (1978) found larger bus systems to exhibit proportionally higher costs as fleet size increases, owing ostensibly to the greater surface street congestion and stronger union pressures on driver wages experienced by bigger systems. The existence or absence of scale economies depends, in part, on the output measure selected. When output is expressed in terra of passengers or any other measure of service utilization, studies generally show that unit costs fall as ridership increases. This is often more a reflection of declining service quality (in terms of level of crowding) than any inherent productivity gain. Accordingly, most recent studies have used service or capacity related measures such as vehicle miles or vehicle hours when investigating the existence of economies of scale. Using a cross- section of 87 U.S. bus properties, Williams (1979) estimated a short-run, variable cost function which produced a cost elasticity of 0.467 as a function of bus-miles (e.g., every ten percent increase in bus miles is associated with a 4.67 percent increase in variable costs). A similar cross-sectional study by Viton (1981) found cost elasticities in the range of 0.51 to 0.60 as a function of bus-miles for 54 U.S. transit properties. Using translog cost models for multiple indices of output and time series data, several more recent analyses have generally confirmed the tendency toward diseconomies of scale with respect to vehicle miles among larger transit properties (Berechman, 1982; Berechman and Giuliano, 1982; Talley and Anderson, 1986). To summarize, the general consensus among transportation analysts appears to be that there is little evidence of economies of scale in the U.S. transit industry (Oram, 1979; McGillivary, et al., 1980; Berechman, 1982). When output is measured with respect to revenue passengers, slight productivity gains can be found. However, when the output measure "vehicle miles" is used, a more intuitive measure in the sense that costs are more directly related to the production of miles, the industry is generally characterized by decreasing returns to scale, particularly for large systems. To the extent, then, operating costs increase at a faster rate than revenues as services are expanded, public transit agencies would benefit financially by load-shedding -- i.e., contracting out incremental services that incur relatively high deficits, such as peak-hour express runs. in some instances, this might involve the conversion of a deficit-producing public operation to a profit- 38 making private operation. Because many private firms are able to deploy drivers and vehicles for special charter services rather than letting resources sit idle during the midday, there's sufficient evidence that such load-shedding practices can lead to a win-win situation for both the public and private sector (Teal and Giuliano, 1985). 4.2 Evidence on Scale Economies from Case Sites To further probe the scale economy issue, some of the performance indicators presented in the prior chapter were compared among vehicle size groups in addition to express versus local services. Table 4.1 shows that the average cost recovery ratio (based on the highest-patronage services) seesaws for both peak-express and all- day local services: it falls from the smallest to the mid-size operators and then rebounds to reach its highest average level for the biggest properties.2 Because they enjoy significantly higher ridership levels, transit agencies with more than 500 vehicles ostensibly do the best job of covering the costs of their most heavily utilized services. Correspondingly, the table also reveals that the big systems average the lowest deficit per rider on their highest-patronized routes. Most relevant to the scale economies question are the changes in average unit costs as a function of fleet size for these high- ridership routes. For both peak and local services, the average cost per rider is substantially lower for big properties than smaller ones. This seems to confirm the findings of other researchers that scale economies exist when output is expressed in terms of patronage. (The average weekday ridership volumes for the high-patronage routes studied were: 2,415 for small fleet, 6 733 for medium-sized ones, and 13,254 for the largest ones). It must be cautioned that these figures are based on a cross-sectional comparison across transit properties rather than measuring changes in unit costs within an agency as it begins serving larger increments of passengers. Nonetheless, the clear inference appears to be that as more people ride a system, the cost per rider plummets since the marginal cost of carrying each additional rider is practically zero as long as excess capacity exists. That is, scale economies should exist when output is measured in terms of riders whether comparing unit costs across different sizes of transit operators at one point in time or within a single transit at different points in time. Also consistent with the findings of other researchers is the steady increase in average cost per mile as a function of fleet size. Table 4.1 shows that the tendency toward scale diseconomies when output is measured in terms of mileage holds for peak-express and all-day local services alike. It should be emphasized that this relationship holds when patronage levels are controlled for since the analysis is based on high-patronage routes. While the marginal costs of serving additional passengers on heavily used routes appear to drop as agencies get bigger, the extra cost of logging additional miles appears to increase. this is probably due in part to the affects of the higher density, more congested environs in which larger transit systems tend to operate. Big transit operations appear to pay a penalty for the slower speeds and stop-and-go conditions they endure -generally in the way of higher maintenance costs, fuel consumption rates, liability insurance rates, and driver wage scales. It is noteworthy that diseconomies of scale appear to be more severe among the highest patronized all-day services versus their peak-express counterparts. That is, the average cost per mile incurred on the busiest local routes of the very largest transit properties was about 30 percent higher than the average of mid-size operators. For peak-express services, the differential was only around 8 percent. While it appears that economies could be gained by shedding the loads of peakexpress services, the potential financial benefits from contracting out extra local services could be even greater in some instances.3 In summary, the analysis of this section appears to lend further credence to the potential benefits of load-shedding and casts further doubt about the credibility of the cream-skimming argument. Overall, high-demand, peak-hour services appear to be the disproportionately the highest 39 Table 4.1 Comparison of Financial Performance Indicators by Fleet Size Fleet Size Under Over 250 250-499 500 Indicator Buses Buses Buses F Prob. Peak-Express: Average cost recovery ratio .45 .27 .52 7.837 .001 Average cost/rider $2.97 $2.78 $2.17 1.701 .386 Average cost/mile $2.31 $2.74 $2.94 7.685 .001 Average deficit/rider $1.58 $2.23 $1.13 4.094 .021 No. of high-ridership routes 31 21 24 All-Day Local. Average cost recovery ratio .50 .46 .83 7.363 .001 Average cost/rider $1.56 $1.08 $0.83 7.901 .001 Average cost/mile $1.48 $2.91 $3.79 34.707 .000 Average deficit/rider $0.89 $0.67 $0.54 1.922 .154 No. of high-ridership routes 34 21 21 Notes: Cost recovery ratio = farebox revenue divided by direct, day-to-day operating costs. Cost/rider = operating costs divided by revenue-paying passengers (unlinked trips). Cost/mile = operating cost divided by total vehicle miles. Deficit/rider = (operating cost - passenger revenue) divided by revenue-paying passengers. 40 cost services and accordingly the biggest money-losers. Most transit agencies would profit by shedding some of their peak-period demand to private operators through contracting arrangements. This particularly holds when there are pressures to open up new services or expand existing ones. Rather than incurring high extra costs for logging new bus miles, the findings of this and other research suggest that more and more public transit agencies would be better off contracting additional services to the lowest bidder. 4.3 Comparison of Average Cost and Deficit Rates Among Transit Properties The prior chapter showed that the average cost per passenger was considerably higher for heavily-patronized express services than for heavily-patronized local ones. Even when the inflationary effects of restrictive work rules are not attributed to peak services, the analysis demonstrated that peak-only services tend to incur much higher deficits per rider than their local service counterparts. Clearly, this finding further suggests that there are greater diseconomies involved in serving high-demand peak-only services than high-demand local ones. This section probes this notion a bit further by comparing the average cost per passenger for the three highest ridership peak- only versus all-day local services of each transit property. Table 4.2 shows the peak passenger cost factor -- the ratio of average cost per peak-express rider to the average cost per local rider - for all 25 case study transit properties. For all except two agencies, Sacramento RTD and Utah Transit, the highest ridership express routes were more costly to serve on a per passenger basis than the highest ridership local ones. On average, it appears that most transit systems incur more than double the cost per rider on their busiest express routes than they do on their busiest local ones. The relatively poor performance of Sacramento RTD's local routes is mainly attributable to one high patronage service -- route 34 -- that is far more costly to operate than the other two local routes included in the analysis. Because route 34 operates in some low-density settings seven days a week, it logs nearly twice the daily vehicle hours yet only has a comparable patronage level as the other two local routes. Utah Transit's relative poor local route performance can likewise be attributed to one unusually expensive local route that serves suburban markets - route 24. Table 4.3 shows that the average deficit per peak-express versus all-day local rider also varies considerably across twenty of the case sites. (Excluded from the table are SEPTA, WMATA, Westchester County Transit, Minneapolis MTC, and Greater Richmond Transit, all of which were found to operate profitable routes and therefore would have registered negative deficits.) On average, patrons of the busiest peak-express routes produce deficits that are over twice as high as do passengers of the busiest all-day local routes. No clear pattern emerged as a function of fleet size, suggesting that this general relationship holds for small and large transit properties alike. The highest peak passenger deficit factor was calculated at 9.8 for Jacksonville Transit (also the transit property registering the highest peak passenger cost differential in Table 4.2). This nearly ten-fold differential can be largely attributable to the fact that Jacksonville Transit's dime highest patronage express routes carry only around one-twentieth as many daily passengers as the agency's three busiest local routes, while their total daily operating costs are not as varied. Other agencies that operate high-deficit peak-only services include Santa Clara County Transit, Orange County Transit, and Tidewater Transit. 41 Table 4.2 Comparison of Peak Passenger Cost Factors Average Cost (in dollars) Per Passenger for: Peak Passenger Transit Agency Peak Local Cost Factor Jacksonville 5.93 1.00 5.9 Santa Clara 6.90 1.56 4.4 SEPTA 2.12 0.49 4.3 Tidewater Transit 3.29 0.95 3.5 Bi-State Development 2.48 0.74 3.3 Orange County Transit 3.78 1.19 3.2 Seattle Metro 2.64 1.03 2.6 Houston Metro 2.29 0.92 2.5 HART 2.36 1.00 2.4 Minneapolis MTC 1.86 0.76 2.4 Phoenix Transit 2.25 0.95 2.4 Central Ohio 2.12 0.90 2.3 WMATA 1.21 0.52 2.3 Tucson Suntran 2.13 0.93 2.3 PACE 1.84 0.93 2.0 Fort Worth 3.01 1.51 2.0 Metro Tulsa 3.26 1.73 1.9 Denver RTD 2.49 1.34 1.9 Westchester County 1.36 0.80 1.7 Greater Richmond 0.87 0.55 1.6 Northern Kentucky 1.76 1.16 1.5 SEMTA 4.23 3.62 1.2 Golden Gate Transit 3.25 2.95 1.1 Sacramento RTD 1.91 1.93 1.0 Utah Transit 1.22 1.25 1.0 Total Average 2.66 1.23 2.2 Note: Peak Passenger Cost Factor = average cost per peak rider divided by the average cost per local rider. 42 Table 4.3 Comparison of Peak Passenger Deficit Factors Average Deficit (in dollars) Per Passenger for: Peak Passenger Transit Agency Peak Local Deficit Factor Jacksonville 3.63 0.37 9.8 Tidewater Transit 2.15 0.32 6.7 Bi-State Development 1.75 0.31 5.6 Santa Clara County 6.32 1.26 5.0 Orange County Transit 3.32 0.75 4.4 Seattle Metro 1.94 0.59 3.3 Phoenix Transit 1.48 0.48 3.1 PACE 1.39 0.49 2.8 Central Ohio 1.58 0.56 2.8 HART 1.46 0.54 2.7 Tucson Suntran 1.54 0.59 2.6 Fort Worth 2.44 1.04 2.3 Northern Kentucky 1.13 0.53 2.1 Metro Tulsa 2.41 1.28 1.9 Denver RTD 1.59 0.96 1.6 Houston Metro 0.71 0.59 1.2 Utah Transit 0.93 0.84 1.1 Sacramento RTD 1.11 1.00 1.1 SEMTA 2.62 2.61 1.0 Golden Gate Transit 0.96 1.63 0.6 Total Average 2.02 0.84 2.4 Note: Peak Passenger Deficit Factor = average deficit per peak rider divided by the average deficit per local rider. 43 4.4 Conclusion The analyses contained in this chapter, when coupled with the findings of other researchers, provide compelling evidence that declining economies of scale generally characterize the nation's transit industry. For the most part, economies can only be found when outputs are expressed in terms of patronage. As long as vehicle capacity is under-utilized, it is logical that transit properties can reap productivity benefits by attracting new customers. However, the marginal cost of attracting new customers when a system is at or near capacity could be substantially higher than the average cost estimates produced using aggregate analyses, such as in this study. When additional demand requires the start- up of new services or the expansion of existing ones, extra equipment has to be purchased, the work force has to be expanded, and additional mileage and hours of service have to be logged. As a result, this and other research unequivocally show that the unit costs of delivering bus services rise when vehicle-miles are increased, particularly in the case of large transit properties. The analysis contained in this chapter, it should be noted, differs from other research on scale economies in the transit industry on several grounds. For one, it was based on comparing unit costs variations among a subset of services for multiple transit agencies, specifically by contrasting costs among a handful of peak-express and all-day local routes. Additionally, the routes that were studied for each agency represented the three highest ridership bus lines for both types of services. Thus, the analysis offered insights into unit cost variations at the high end of the ridership spectrum. The general finding that peak-express services incur units costs that are roughly twice the units costs of all-day local services is consistent with evidence from other researchers, perhaps with the twist that the relationship holds even when the busiest services are examined. Overall, there appears to be little justification for treating urban mass transit as a natural monopoly. The absence of any inherent increasing returns to scale suggests there is little grounds for protecting public transit agencies from competition, particularly when the peak loads that these agencies risk losing are consistently found to be inordinately costly to serve. Shedding some of these costly loads to the private sector would appear to generate the first and second order benefits hypothesized in the second chapter: initially, overall deficits would fall to the extent private sector labor and capital inputs, whose costs are tempered by the forces of competition, fall below those of the public sectors; and eventually, by ridding itself of high deficit services and competing with others for peak period services, pressures should be exerted on the internal costs of the transit agency that begin to make them more competitive with private service-providers. All evidence points to the fact that the peak period continues to be urban transit's nemesis, in large part because costs increase faster than fares when conditions become more crowded and restrictive work rules often require time-and-half pay for spreading drivers' duties over the morning and evening peaks. Besides shedding expensive peak loads from public transit agencies, competitive contracting could provide many commuters with a higher quality service than that offered by conventional buses. Many private subscription buses that serve suburban commuter markets, for instance, offer riders padded, guaranteed seats and door-to- door service. The fact that most commuters are far more service sensitive than price sensitive indicates that more Americans might be willing to patronize transit ff the quality of the ride was improved (Mayworm, et al., 1980). While standard buses often sit idle during the midday and on weekends, the premium buses operated by private firms can be put to use as special charters to museums, sports events, theme parks, and other recreational sites. To the extent that the urban transportation market is deregulated to allow the entry of shared-ride taxis, jitneys, and private dial-a-vans, efficiency gains would be supplemented by other social benefits. While such paratransit modes could be used to handle some peak period commuter demands, they could also provide more specialized curb-to-curb services for elderly and handicapped populations. The expansion of paratransit options would clearly benefit most cities by offering a supplement to peak-hour transit operations while also providing an important off-peak service to disadvantaged persons (Oram, 1979; Teal, 1983; Cervero, 1985). 44 Notes 1. The four basic properties of any natural monopoly are: a large fixed capital investment; non-storable services; fluctuating demands with heavy peak loads; and inherent increasing returns to scale (Mooring, 1970; Kahn, 1971). 2. The statistical results presented in this section are based on an Analysis of Variance (ANOVA). F statistics and probability values signify the overall strength of association between the performance indicators shown and the fleet size variable. 3. It bears repeating that these inferences are made by comparing differences in unit costs across properties at one point in time rather than longitudinally within a particular transit agency. Since load-shedding normally occurs within a single agency, one must be cautious in inferring that the results of this analysis justify such practices. Still, to the extent that declining scale economies exist within a transit agency, the rationale behind load- shedding should nonetheless hold. 45 Chapter Five: Characteristics of Profitable Bus Routes 5.1 Introduction The prior two chapters found that there is very little "cream to skim" under the contracting out of public transit services. Peak-only express services, the very ones which public agencies seem to be most concerned about protecting and are thus the source of much of the cream-skimming debate, were generally found to be the most deficit-prone services. Overall, the busiest all-day local services were found to outperform the busiest peak-only express services by a factor of two to four, depending on the particular measure of performance. Further, of the ten bus routes that were found to be profitable, eight operated all day on surface streets. Of the two peak-only express routes that were profitable, both were operated by Greater Richmond Transit, all of whose routes performed well in the study. Given that local routes are more productive than express routes, and that so few routes of any kind appear to cover their costs through the farebox, this chapter investigates what factors might account for the profitability of these stellar routes. In contrast to the two previous chapters, emphasis is placed primarily on the demand side of the equation -- how socio-demographic factors, urban land uses, and the geographic distribution of residences and employment might be giving rise to high levels of demand and thus profitable services. vehicle costs certainly vary as a function of operating conditions, for the most part input costs are incurred at a similar rate within a transit agency (e.g., roughly the same cost per gallon of fuel or 10,000 miles of wheel tread wear). Since most of the agencies charge more or less a flat fare, generally distinguishing fares between express and local bus services and exacting a small peak surcharge, the effects of pricing structures on profitability are not directly considered. To a large extent, then, this analysis focuses on the influences of various exogenous factors that are outside a transit agency's control on levels of profitability -- namely, user demographics, spatial patterns of urban activities, and land use densities. As will be evident from the individual route-by-route case summaries presented in this chapter, virtually all eleven of the profitable or near-profitable routes that are discussed serve a population base with relatively high shares of minorities and low- income households when compared to the region at large in which they operate. To a large extent, profitable routes serve highly captive markets where automobile alternatives are limited. Further, these transit-dependent populations appear to ride buses for relatively short trips, producing high farebox returns per mile. By and large, profitable local routes traverse numerous activity centers such as retail plazas and schools as well as well- defined residential clusters and employment hubs. Such operating environments are the polar opposites of typical peak-only express routes which originate in residential neighborhoods and then close their doors until they reach the employment destinations. On all- day local routes serving short hop trips, seats turn over frequently, generating relatively high revenue yields. Equally important are the effects of land use intensities. Successful bus routes generally operate along corridors with high average population and employment densities. Most connect central business districts (CBDs) with densely populated neighborhoods and intensively developed jobs centers. While some exceptions to these rules were found, the chief demand-side prerequisites for a profitable service appear to be a large transit-dependent population, short-haul trip making between multiple activity centers, and generally high densities along the route's corridor. 46 The following sections describe some of these features for each of the profitable routes of the four transit agencies: Southeast Pennsylvania Transportation Authority (SEPTA); Greater Richmond Transit Company; Washington Metropolitan Area Transportation Authority (WMATA); and the Metropolitan Transit Comniission (MTC) serving the Twin Cities. As a prelude to the discussions which follow, Table 5.1 lists the eleven profitable or near-profitable routes and present summary performance statistics for each.1 5.2 Southeast Pennsylvania Transportation Authority (SEPTA) SEPTA is the regional public transit operator for Philadelphia and its Pennsylvanian suburbs. SEPTA is the fifth largest transit system in the country, with a fleet of about 1,100 motor coaches operating along 108 bus routes. Besides conventional motor bus services, SEPTA also operates streetcars, electric trolley buses, elevated and underground rail transit, and heavy rail commuter trains. The agency was formed in 1968 to assume the operations of several privately owned transit companies that were failing financially. In 1987, SEPTA carried around 200 annual unlinked passenger trips, serving a district with a population of approximately 4.7 million persons. SEPTA charges a basic local fare of $1.25 for bus and trolley, plus 30 cents for each suburban zone that is crossed.2 For the system as a whole, SEPTA recovered 47 percent of its 1987 fully allocated operating costs through passenger fares. Three of SEPTA's routes were found to generate a profit: Routes C, 33, and 52. Figure 5.1 shows the general location of these routes in the Philadelphia area, along with some of the major activity points along each route. Two of the routes, C and 52, connect low-income minority neighborhoods with downtown Philadelphia and other employment centers. The third route, 52, runs entirely within several low-income, mixed residential- commercial neighborhoods in predominantly black West Philadelphia. As with virtually all of the profitable routes found in this research, SEPTA's three money-making bus lines traverse neighborhoods with average household incomes well below the city's average and with relatively large minority concentrations (see Table 5.2 and Figure 5.2). AU three profitable routes, moreover, cover fairly long distances and average frequent headways (as short as 4 minutes during the peak and 10 minutes during the off-peak along trunk-line segments). Frequent boardings and alightings give rise to relatively high average passenger counts per vehicle mile - - 8.9 to 10.8, generally around 40 percent higher than SEPTA's systemwide average. As shown in Table 5.3, high densities also generally characterize the corridors along each of the routes. Transit-dependency, short-haul trip-making, and dense land uses thus appear to be the key factors behind the financial successes of these three SEPTA routes. Route C Route C is a long-distance north-south line, connecting the Cheltenham Mall at the city's northern boundary with the Philadelphia Naval Shipyard at the city's southern edge. From the north, Route C runs along Broad Street, Philadelphia's principal north-south arterial. It passes through largely black residential neighborhoods in North Philadelphia, through Chinatown, skirts Temple University, and then the western portion of downtown. The route continues through a number of commercial corridors and residential clusters in the southern half of the city, paralleling the Delaware River for much of its path. In addition to downtown and the Naval Shipyard, Route C also intersects several medical centers, schools, retail plazas, and SEPTA commuter rail stations. Veterans Stadium, the city's professional sports facility, is also served by Route C.3 Weekday services commence shortly after 5 a.m. each morning. Headways range from 5 to 15 minutes until around 5:30 p.m. when they change to around 25 to 30 minutes along the extreme northern section of the route (with primary stops retaining headways to 10 to 15 minutes). Service to the Naval Shipyard ceases at about 6 p.m.. Late-night "owl" services extend until nearly 3:00 a.m. at selected stops.4 47 Click HERE for graphic. 48 Click HERE for graphic. 49 Table 5.2 Average Income Indicators for SEPTA's Three Profitable Routes and At-Large Service Area 1980 Median Income per Household Capita Route C $14,082 $6,105 Route 33 13,455 6,387 Route 52 13,137 4,582 SMSA 21,192 7,458 Philadelphia City 16,258 6,053 Note: Bus route data are based on averages computed from all census tracts that are traversed by or which directly border each route. SMSA signifies the Standardized Metropolitan Statistical Area for greater Philadelphia. Sources: U.S. Bureau of the Census (1983) and Delaware Valley Regional Planning Commission (1988). Table 5.3 Residential and Employment Densities Along SEPTA's Three Profitable Routes and for Philadelphia Population Households Employment per acre per acre per acre Route C 39 16 38 Route 33 29 11 49 Route 52 45 15 5 Philadelphia City 21 8 10 Note: All data are for 1980 and are based on averages computed from all census tracts that are traversed by or which directly border each route. Densities are net of public lands, parks, open space, and restricted-use areas. SMSA signifies the Standardized Metropolitan Statistical Area for greater Philadelphia. Source: Delaware Valley Regional Planning Commission (1988). 50 Click HERE for graphic. Table 5.2 showed that the median 1980 household income for the census tracts served by Route C was about one-third less than that of the Philadelphia metropolitan area at-large. Average residential densities, moreover, are 86 percent higher than for the City of Philadelphia as a whole (Table 5.3). From Figure 5.2, it is seen that 55 percent of the immediately adjacent population served by Route C is non-white, compared to 42 percent of the city's population. High densities and high shares of transit-dependent residents have given rise to an average count of 8.82 passengers per vehicle mile on Route C, compared to around 6.4 for SEPTA as a whole. Route 33 This route, around 4 straightline miles from end-to-end, connects several residential neighborhoods in North Philadelphia with downtown. At the north end, Route 33 originates in the Nicetown residential area, heads south along both 19th and 22nd Streets, makes an abrupt turn to the east through the heart of downtown Philadelphia along Market Street, and terminates near the waterfront at Front Street on the city's eastern edge. Like Route C, Route 33 crosses a number of SEPTA bus routes and commuter rail stations along its stretch. Girard College is also directly served by the route. Route 33 operates nearly around-the-clock, beginning shortly after 4 a.m. and continuing until around 3 am. Both weekday and weekend headways range from 8 to 15 minutes until the evening. Buses run around one-half an hour apart during the late-night. 51 Tables 5.2 and 5.3 show the degree to which neighborhoods along the Route 33 corridor are more transit-dependent and denser than the region at-large: median household incomes are around 37 below the SMSA average, average residential densities are around 38 percent higher than those of the city as a whole, and average employment densities are nearly 5 times as high. Because of the relatively short-haul travel, moreover, Route 33 averages 10.7 passengers per vehicle mile, a rate that is two-third's higher than SEPTA's overall average. Finally, over 80 of the residents living adjacent to Route 33 are non-white, a far greater proportion than for the city or SMSA. Route 52 This route, around 4 lineal miles in length, runs in a northwest to southeast direction in a part of the city made up almost entirely of black households. The central part of the corridor is dotted by small retail shops, wholesaling, and some light industry. The southern terminus, at Woodland Avenue, serves a mixed-use neighborhood near the Schuylkill River. Route 52 connects several regional commuter rail and subway stations as well as other SEPTA bus routes. Route 52 operates around-the-clock seven days a week. As shown in Table 5.2, Route 52's immediate service area registers the lowest average incomes, highest share of minorities, and highest average population densities of any of the profitable route service areas studied. Population densities, in fact, are more than double Philadelphia's average. Per capita income along the corridor is only around 60 per cent of the regional average. Based on boarding-alighting surveys conducted by SEPTA, the average trip length of 2.2 miles along Route 52 is roughly one-half the system's average. In summary, then, a large population of captive users making short-hop trips along a fairly dense residential and mixed-use corridor have enabled Route 52 to cover 152 percent of its costs through the farebox. 5.3 Greater Richmond Transit Company Greater Richmond Transit (GRT) was established in 1973 to take over the operations of the Virginia Transit Company, a private firm which was facing bankruptcy at the time. GRT has continued a longstanding tradition of providing Richmond's residents urban mass transit services, dating back to 1888 when the world's first electric streetcar line began operations. Today, a private contractor, ATE Management and Service Company, manages a fleet of over 200 buses along 27 fixed routes for GRT, under the policy direction of the agency's board of directors. ATE Management has retained its contract with GRT since 1973 under a periodic rebidding process. In 1987, GRT carried slightly over 25 million unlinked passenger trips. GRT's local bus fare is 75 centers. Most express services cost $1.25 per trip. By national standards, GRT appears to be a fairly efficient operation. The systemwide cost recovery ratio is around 0.58, considerably higher than that of other peers. GRT staff attribute the system's fiscal health to a no-frills operation and continuity of management. Several agency officials worked previously for the Virginia Transit Company and have accumulated over 40 years of work experiences with managing mass transit in Richmond. Besides having a highly cost conscious management firm g the operations, the existence of a number of top GRT managers who previously worked in the private sector has evidently instilled an efficiency-minded work ethos within the agency. Two of GRT's peak-express routes were found to be profitable and two of its all-day local routes were found to be near- profitable (covering over 90 percent of their costs). The two moneymaking routes are: Route 26 (Parham Road Express) and Route 67 (Chippenham Express). The two all-day locals which nearly cover their costs are: Route 34 (Highland Park-Hull Street) and Route 37 (Chamberlayne). Figure 5.3 maps the location of these four routes while Tables 5.4 52 Click HERE for graphic. 53 Table 5.4 Average Income Indicators for GRT's Two Profitable Local Routes and At-Large Service Area 1980 Median Income per Household Capita Route 34 $15,404 $6,086 Route 37 14,141 6,429 SMSA 21,475 7,804 Richmond City 17,677 7,073 Note: Bus route data are based on averages computed from all census tracts that are traversed by or which directly border each route. SMSA signifies the Standardized Metropolitan Statistical Area for greater Richmond. Source: U.S. Bureau of the Census (1993) and unpublished survey data provided by the Greater Richmond Transit Company. through 5.6 and Figure 5.4 summarize some of the pertinent socio- demographic and land use characteristics of the corridors served by most of the routes. In general, GRT's most lucrative all day local routes serve largely non-white, low-income populations residing along corridors with high average densities. Some of the characteristics of all four profitable routes are detailed below. Route 26 (Parham Road Express) This peak-only commuter service originates at a park-and-ride lot in suburban Westbriar in Henrico County, northwest of Richmond, then operates non-stop as a "freeway flier" along Interstate 64, ending in downtown Richmond with stops near the Federal Building and numerous Virginia state government offices. Route 26 operates only on weekdays from 6:30 a.m. to 8:25 a.m. and from 4 p.m. to 5:45 p.m. During this period, buses depart every eight minutes in the morning and every seven minutes in the evening. In contrast to other profitable services, Route 26 clearly does not serve a captive, transit dependent population. Household incomes and auto-availability rates (Table 5.5) are relatively high. Women comprise 69 percent of the route's ridership. Route 26's success is clearly due less to the socio-demographic and land use make-up of the corridor and more to GRT's internal cost- efficiency and the route's connection of two hubs. For a one-way fare of $1.25, Route 26 offers a high-speed connection between several well-defined residential subdivisions and a number of geographically clustered government office centers. It averages load factors between 0.95 and 1.00, meaning there is little wasted capacity.5 While Route 26's farebox revenue receipts per vehicle mile are relatively low, the efficiency in which the service is offered has produced a comparably low operating cost per mile. 54 Table 5.5 Ridership and Corridor Household Characteristics of Four Profitable Routes and for GRT Service Area Percent of Percent of RidersHouseholds With Less Than Female Non-White 2 Workers 2 Cars Route 26 (Peak) 69 3 27 29 Route 67 (Peak) 76 49 59 65 Route 34 (Local) 75 98 50 66 Route 37 (Local) 65 85 60 70 GRT Service Area 54 36 34 58 Note: Household data are based on household characteristics of the riders surveyed from GRT's 1985 On-Board Ridership Survey. Source: 1985 On-Board Ridership Survey, unpublished results, Greater Richmond Transit Company. Table 5.6 Residential and Employment Densities Along GRT's Two Profitable Local Routes and for the GRT Service Area Population Households Employment per acre per acre per acre Route 34 8.1 3.4 5.5 Route 37 19.8 9.6 22.4 GRT Service Area 6.6 2.6 3.5 Note: All data are 1985 projections from the 1980 census and are based on averages computed from all census tracts that are traversed by or which directly border each route. The densities statistics shown are estimates adapted from gross density figures and are net of public lands, parks, open space, and restricted-use areas. Source: Richmond Regional Planning District Commission (1985). 55 Click HERE for graphic. Route 67 (Chippenham express) This peak-only commuter line operates slightly longer hours than does the Parham Road Express route, but less frequently, departing every 20 minutes in both the morning and evening periods. Morning service operates from 7 am. to 9:30 a.m. and evening servicing runs from 4 p.m. to 6 p.m. The route picks up passengers at the Chippenham Mall near the city's western boundary, follows a local arterial in a mainly closed-door, skip-stop manner, and connects to downtown Richmond, where it stops at a commercial center at Broad and Foushee Streets. Because it uses local streets over most of its distance, the fare for the Chippenham Express is 75 cents, just as for local routes. Like the Parham Road Express, the Chippenham line also draws ridership that is mostly female. In contrast, however, nearly one- half of the express run's ridership is non-white (see Table 5.5). About 65 percent of the line's riders, moreover, come from households with no cars or only one car, suggesting the corridor is made up of a higher percentage of transit-dependent customers than is the case for most peak-only express lines. While the Chippenham route is operated as cost efficiently as the Parham Road line, its relatively high transit-captive ridership base has produced high enough load factors during the three and one-half hour service period to allow the route to break even. 56 Route 34 This all-day local route connects several modest income neighborhoods in the northern and southern parts of Richmond to the city's center. Route 34 begins operating at 5:05 am. and continues until midnight. Headways range from 8 to 15 minutes throughout the day, and then increase to 20 minutes during the evening and half an hour during the late-night.6 In addition to the downtown area, Route 34 serves a regional hospital, several large-scale shopping malls, and a couple of commercial districts. Around 75 percent of Route 34's patrons are females and 98 percent are non-white, both of which are much higher than GRT's systemwide averages. Median family incomes along the Route 34 corridor are around 13 percent lower than the city's average and 28 percent lower than the SMSA's average. From a 1985 on-board ridership survey, it was found that nearly two-thirds of all passenger trips aboard Route 34 were bound for downtown Richmond. Despite the route's length and orientation to downtown, there still were a relatively large number of short- haul trips -- the average trip distance was just over three miles, about one-quarter shorter than GRT's average for non-express routes. Besides the prevalence of short distance trip-making, Route 34's financial success can also be attribute to its high average load factor -- around 71 passengers per hour, the highest of all of GRT's routes. Route 34 also has the highest proportion of "transit-oriented" riders per route area population -- 95 percent, in all. This index, measured by GRT planners from the ridership questionnaires, reflects the proportion of residents who regularly ride the bus and would have to forego a significant share of trips if bus service was not available. Overall, Route 34 clearly serves one of the most transit-dependent segments of Richmond's population. Route 37 This north-south route connects the Azalea Mall at its northern terminus with the heart of downtown Richmond at its southern end. Route 37 operates along Chamberlayne Avenue for much of its distance, passing numerous retail plazas and residential neighborhoods along the way. Weekday service begins at 5:45 a.m. and continues until around quarter after midnight. Buses operate 8 minutes apart during peak periods, 30 minutes apart at the mid-day, and on 50 minute intervals during the late-night period. Much of Route 37's clientele appears captive: median household incomes are 35 percent below the regional average, 85 percent of patrons are non-white, and 70 percent live in residences with one or no automobiles. Route 37 also traverses a relatively high- density corridor. According to Table 5.6, average population densities along the Route 37 corridor are three times higher than those of GRT's entire service area, while employment densities are over six times as high. 5.4 Washington Metropolitan Area Transit Authority (WMATA) WMATA, with a fleet of over 1,500 motor coaches, operates the fourth largest regional bus enterprise in the nation. Formed through a Congressionally approved interstate compact in 1966 to plan, finance, and operate public transit in and around the nation's capital, WMATA has emerged as a unique multi- jurisdictional agency. The District of Columbia, northern Virginia, and suburban Maryland are represented by two directors and two alternates on WMATA's Board. In a sense, each jurisdiction "buys" its transit services from WMATA, as much or as little as it is willing to subsidize. WMATA formally began operating public transit services when four different private bus companies were acquired in late 1973. Annual ridership has grown from 116 million to over 200 million since public acquisition and the expansion of services. Besides its 1,500-plus buses which 57 operate on over 400 basic bus routes, WMATA operates a rapid rail transit system, serving over 350,000 passengers per weekday on the 60 mile system.7 In 1987, WMATA met 48 percent of its bus operating costs through fares, a relatively high recovery rate by national standards. Currently, the local bus fare within the District is 75 cents, plus a zonal surcharge for crossing suburban districts or state boundaries. A 5 cents surcharge is also collected during peak hours.8 Three of WMATA's all-day local routes were found to generate a profit: Benning Road, Pennsylvania Avenue, and Georgia/7th Streets. In fact, Benning Road was found to be, by far, the most profitable route studied, returning over twice its direct, day-to-day operating costs through the farebox. As shown in Figure 5.5, all three of TA's profitable routes connect predominantly residential areas of the district to major government offices, commercial centers, and Metrorail stations along some of the District's principal arterials. Tables 5.7 and 5.8, along with Figure 5.6, summarize some of the major demographic and density characteristics of WMATA's three profitable routes in relation to the District as a whole. While the Benning Road and Georgia/7th Street lines clearly serve high shares of minority and low-income households, at least relative to the District at large and the metropolitan area as a whole, the Pennsylvania Avenue route is of a different character. WMATA's Pennsylvania route serves corridors with high densities, high average incomes, and high shares of non-whites. In general, the route caters to professionals and federal employees headed to government offices and other job centers, as well as other major activity centers (such as George Washington University) concentrated near or along Pennsylvania Avenue. In the case of all three profitable routes, peak period load factors exceed 1.5, while off-peak passengers account for between 70 percent and 90 percent of capacity, on average. Thus, high densities and a cluster of activities along the corridor have led to profitable operations in the case of the Pennsylvania Avenue route, whereas the high incidence of transit-dependency has ostensibly led to the financial success of the Benning Road and Georgia/7th Street lines. Characteristics of these three profitable all-day local routes are elaborated upon below. Benning Road WMATA provides extensive service on its Benning Road line, which consists of several sub-routes. The Benning Road X1 line runs east and west between the Potomac Park area in the District's northwest and the Minnesota Avenue Metrorail station in the northeast. For the most part, this line operates along Constitution Avenue, H Street, and Benning Road. It serves the Federal Triangle government complex and Amtrak's Union Station, among other key activity centers. The X3 branch of the Benning Road route runs on a northwest-southeast axis between the McLean Gardens district (along Wisconsin Avenue) and the Minnesota Avenue Metrorail station. The X3 route passes through Woodley Park and the predominantly minority Adams-Morgan neighborhoods. The X2, C4, and X5 branches of the Benning Road line connect between Lafayette Square near the White House to the Capitol Heights Metrorail station to the east. Overall, the Benning Road functions as a lifeline between low-income neighborhoods and various retail, entertainment, and employment centers as well as an important feeder connection to the Metrorail system. From Table 5.7, it is seen that median household incomes along the corridors served by Benning Road are 16 percent below the District's average and 34 percent below the SMSA's average. The corridor's share of minority households is comparatively high. Table 5.8 shows that both residential and employment densities markedly exceed the District's averages. On a census tract by census tract basis, the number of jobs are fairly evenly interspersed along the Benning Road corridor. Although average trip length data were not available for the Benning Road line, it is likely that a sizeable number of employed riders make short trips since zones with the most jobs also tend to be the ones with the most housing units. During interviews, several WMATA officials stated that the high boarding-alighting counts along 58 Click HERE for graphic. 59 Table 5.7 Average Income Indicators for WMATA's Three Profitable Local Routes, Washington, D.C., and SMSA 1980 Median Income per Household Capita Benning Road $18,765 $7,445 Pennsylvania Avenue 26,703 14,228 Georgia/7th Streets 20,170 8,469 SMSA 27,837 10,249 Washington, D.C. 21,982 8,960 Note: Bus route data are based on averages computed from all census tracts that are traversed by or which directly border each route. SMSA signifies the Standardized Metropolitan Statistical Area for the greater Washington, D.C. area. Source: U.S. Bureau of the Census (1983). Table 5.8 Residential and Employment Densities Along WMATA's Three Profitable Local Routes and for Washington, D.C. Population Households Employment per sq. mile per sq. mile per sq. mile Benning Road 15,489 6,371 18,663 Pennsylvania Avenue 11,396 11,795 23,733 Georgia/7th Street 13,790 5,941 13,672 District of Columbia 10,967 4,506 11,993 Note: All data are 1986 projections derived from the 1980 census and are based on averages computed from all census tracts that are traversed by or which directly border each route. Densities are estimates are based on usable space which excludes lakes, public parks, and other no- developable areas. Source: Washington Metropolitan Area Council of Governments (1988). 60 Click HERE for graphic. certain segments of the route likely reflected a high incidence of short-haul trip-making. With high turnover, WMATA is able to continually re-sell bus seats along the Benning Road corridor, apparently at a very high profit margin. Georgia/7th Streets This line connects the Silver Spring, Maryland Metrorail station north of the District with the L'Enfant Plaza Metrorail station near a cluster of federal offices. Formerly a streetcar route, the line has operated along Georgia and 7th Streets for over 50 years and has become a firmly entrenched service. Neighborhoods along the northern portion of the route are made up primarily of low-income households interspersed by fight retail. Service operates nearly around-the-clock seven days a week, with peak headways of eight minutes and off-peak headways of 14 minutes. As with the Benning Road line, the Georgia/7th Street line serves a population with appreciably lower average incomes and which reside at higher average densities than the typical District resident. Although average trip distances appear to be a little longer than those on the other two profitable WMATA routes, the Georgia/7th Street line also averages a high peak hour load factor of 1.4 and off-peak load factor of 0.75. 61 Pennsylvania Avenue WMATA's Pennsylvania Avenue line Thisects the heart of the District, traversing a historically significant corridor dotted by colonial townhouses, stately government buildings, midrise office towers, modem retail centers, and public plazas. Beginning near the Maryland border, the route runs through several upscale neighborhoods and commercial districts before turning east along Pennsylvania Avenue. The route then proceeds to the Naylor Gardens and Shipley Terrace neighborhoods in the southeastern part of the district, passing numerous employment clusters along the way, including the Federal Triangle complex, Capitol Mall, and the Lafayette Square area. In all, five Metrorail stations are directly served the route. In a way, the Pennsylvania Avenue line more closely resembles a special commuter ran than an all-purpose service since it connects established residential neighborhoods at both ends with major employment centers in the District's interior. For the most part, the residential neighborhoods which surround the Pennsylvania line shatter the stereotype developed so far that profitable routes only serve highly transit-dependent populations. On average, household incomes along the corridor are one-quarter higher than the regional average. The corridor's high median incomes, it should be noted, was inflated by four very affluent census tracts near the District's employment core. High on and off counts made by WMATA's corp of route checkers along Pennsylvania Avenue suggest that average trip lengths are generally short, probably in the 1.5 to 2 mile range. Overall, the lesson offer by the Pennsylvania Avenue line appears to be that a profit can be generated even along corridors made up of choice riders as long as numerous high-density activity centers are inter-connected and most trips are short in length. Restricted parking and the high cost of vehicle ownership in the District no doubt partly account for the financial success of this and other inner-city TA routes as well. 5.5 Minneapolis-St. Paul Metropolitan Transit Commission (MTC) MTC operates an active fleet of 955 motor buses on 129 fixed routes through Minneapolis, St. Paul, and surrounding suburbs. Created in 1967 to take over the operations of a fledgling private transit system, the agency's role has grown to the point where it provides services to more than a dozen municipalities within a 15 mile radius of downtown Minneapolis on a contract basis. MTC's monthly ridership presently stands at around 6 minion. In 1987, MTC recovered around one-third of its fully allocated operating costs through farebox receipts. MTC's basic bus fare is 60 cents, plus a 15 cents surcharge for peak hour travel. Zonal surcharges are also levied for intersuburban travel. The highest fare is $1.25 for peak hour express travel. One of MTC's all-day local routes, the 21 line, was found to cover its day-to-day operating costs through the farebox. Some of the pertinent features of this route are outlined below. Route 21 Route 21 runs in an east-west direction, connecting a minority neighborhood south of downtown Minneapolis, across the Mississippi River, to downtown St. Paul (see Figure 5.7). The route has a long ridership tradition. It began as a streetcar line in 1905 and has attracted a loyal following of customers ever since. Because of the Lake Street Bridge's structural deficiencies, Route 21's buses do not cross the Mississippi River; rather, passengers must transfer to a special shuttle to span the bridge.9 Despite this inconvenience, passenger loads remain quite high along the ten mile route. Like most profitable routes identified in this study, Route 21's high patronage levels and financial success stems in large part from the highly transit-dependent population it serves. A 1983 MTC survey showed that the route served riders with the lowest income profile of any other route: 43 percent of riders lived in households with annual incomes below $10,000 (Metropolitan Transit 62 Click HERE for graphic. Commission, 1983). The 1980 median household income of census tracts directly served by Route 21 or else contiguous to it, moreover, was found to be $16,801, 30 percent below the regional average. Additionally, approximately 21 percent of residents living in these nearby neighborhoods were non-whites, compared to 10 percent for the City of St. Paul and 13 percent for the City of Minneapolis. Finally, the on-board survey showed that Route 21's passengers average relatively short trips, despite the route's 10 mile end-to- end length. The mean trip distance on Route 21 is only 3.4, below MTC's average of 4.5 miles.10 This average, however, is inflated by a small number of passengers who travel over ten miles. Over one-third of Route 21's customers travel between 2 and 3 miles, and nearly 15 percent travel under one mile. As with other all-day local routes analyzed in this chapter, short-haul journeys, when coupled with high load factors, produce sufficient revenues to operate in the black. 5.6 Conclusion The analysis in this chapter focussed on characteristics of profitable routes that appear to effect the demand for transit travel. With few exceptions, profitable routes share a number of common characteristics: (1) They serve highly transit-dependent populations, as evidenced by the low median incomes and high shares of minority residents along the neighborhoods they Thisect. In general, median household incomes in neighborhoods abutting these profitable routes are between 20 percent and 40 percent lower than those of principal city served by the public transit system. (2) Population and employment densities are consistently high along profitable corridors, anywhere between 30 percent and 100 percent higher than the net densities of the principal city served by the public transit system. Equally important, profitable routes usually connect the region's central business district, serving a number of employment clusters and major activity centers along the way. 63 (3) With the exception of the two peak-express services, average trip -lengths tend to be quite short along profitable routes, generally in the range of 2 to 4 miles. Such high rates of seat turnover produce high revenue yields, especially under flat fare systems. (4) All successful routes average high load factors, generally well over 1.2 during the peak period and in the range of 0.70 to 0.90 during most of the mid-day. The two general exceptions to these findings were the Parham Road express line in Richmond and the Pennsylvania Avenue line in Washington, D.C. Neither route serves highly transit-dependent populations, although some low income households rely on the Pennsylvania Avenue line. In the case of the Parham Road express service, its financial success is likely due as much to the internal cost-efficiencies of the Greater Richmond Transit Company as to any one factor on the demand side. Regardless, the Parham Road express run enjoys consistently high load factors and like some of the other profitable routes, directly services a large government employment complex. It is noteworthy that some of these characteristics of profitable public transit services match those discussed by the Urban Mobility Corporation (1985) and Morlok and Viton (1985) for profitable private express bus services. Regardless of whether a public agency or private firm operates the service or whether buses operate on a frequent-stop basis on local streets or a limited-stop basis on freeways, high load factors and the interconnection of major activity nodes are key ingredients of any profitable bus service. The finding that profitable routes often consist of low- income, minority residents making short-haul trips raises an important policy question. Under the customary flat fare arrangement, those making short trips are to a large extent cross- subsidizing the journeys of those making longer ones (Cervero, 1982b). The incidence of this cross-subsidy is clearly regressive when the short distance users average fairly low income, as in the case of this study. To some extent, then, the high fare returns per mile of travel generated by the profitable routes discussed in this chapter are used to cover the low revenues (and high deficits) per mile of travel incurred by long-haul suburban routes, many of which are express operations. A fairer arrangement would be to introduce distance-based fares that would lower the cost of short trips and raise the cost of longer ones. While this would likely cause most of the money-making routes discussed in this chapter to fall below the break-even mark, at the same time the deficit level of suburban routes would fall. Thus, distance-based pricing could increase the overall cost recovery rate of a transit agency while reducing the regressivity of current flat fare arrangements (Cervero, 1981). In close, profitable routes have none of the characteristics of the kinds of services that would be most subject to contracting. No case exists where transit managers have contracted out routes with high load factors serving short-haul trips along dense corridors with transit-dependent households. Rather, competitive contracting of fixed-route transit services, as practiced to date, has been limited primarily to new, start-up services targeted at low-density suburban markets (Teal, et al., 1986). Thus, the gap between the contentions of the "cream-skimming" argument and the reality of contracting to date seems fairly wide. Very simply, the kinds of fixed-route services that might be conceivably contracted out to private firms would not encompass the markets which are served by the profitable routes discussed in this chapter. To the matter of what other arguments might be lodged against contracting under the aegis of "cream-skimming", we turn to chapter six. 64 Notes 1. The routes discussed in this chapter do not exactly correspond to those listed previously in the third chapter. First, Westchester County's profitable route is not discussed because the County's transit services are wholely privately operated. The focus of this report is on public transit operations and the threat that possible cream-s g could pose to them. Additionally, two near-profitable Greater Richmand Transit (GRI) routes are discussed since it makes sense to examine them in that two other profitable GRT routes are presented in the section and the two near-profitable ones are reasonably similar in character. 2. SEPTA's commuter rail fares range from $1.50 to $2.50 during the off-peak and $3.00 during the peak period. 3. A shorter version of Route C, interspersed with regular runs, omits the extreme northern and southern stops, concentrating just on the residential areas north and south of downtown and downtown itself. Because of this, these nearby neighborhoods enjoy shorter headways (five to ten n-dnutes for most of the day) than do the neighborhoods farther north and south. 4. Route C's weekend service is almost as frequent, operating at headways of about half an hour at its extremities and about 12 to 15 minutes at the primary stops in North Philadelphia and downtown. Owl service extends to 1:20 a.m. southbound and 2:35 a.rrl northbound. 5. A load factor represents the percent of passengers to per- cent of seats within a vehicle. A value of one signifies that every seat is filled and there are no standees in the bus. 6. Route 34's weekend service runs from 5:10 a.m- to 11:30 p.m. with headways of 15 to 25 minutes on Saturdays. Uniform 27 minute headways are maintained on Sundays and holidays. 7. WMATA's rail system will eventually extented to 101 one- way miles when the system is completed, slated sometime during the 1990s. 8. Peak surcharges are also collected on zonal fares. During rush hours, Metrobus fares range from 80 cents to $2.50, depending upon distance traveled. During the off-peak, they range from 75 cents to $1.60. Metrorail's fares range from 80 cents to $1.10 during the off-peak and 80 cents to $2.40 during the peak period. 9. The analysis for Route 21 in this report was based on cost data collected for 1986, the year prior to the initiation of the shuttle bus arrangement. Thus, no cost adjustments were made for the special operation of a connecting shuttle service. If the expenses related to operating this shuttle were included, it is unlikely that WC's Route 21 would still be able to cover its direct, day-to-day operating costs through fare payments. 10. Trip length estimates were derived from average travel fim data collected for riders and Route 21's average operating speed. 65 Chapter Six Challenging Other Arguments Against Competitive Contracting 6.1 Introduction Besides the fear of cream-skimming, a host of other arguments have been lodged against competitive contracting of transit services, including the belief that service quality will deteriorate, the position of transit workers will worsen, and private sector costs will eventually escalate as they begin experiencing some of the diseconomies of taking on a larger scale of transit operations. To address these questions, it makes obvious sense to look at the experiences of transit properties that have practiced contracting. This chapter briefly examines the experiences to date of some of the nation's transit agencies and government bodies actively involved in the competitive contracting of fixed-route bus transit services: Metropolitan Transit Authority of Harris County (Houston Metro); Dallas Area Regional Transit (DART); Westchester County (NY) Transit; Tidewater (VA) Regional Transit (TRT); San Diego County Transit; and Johnson County (KN) Transit. Because it has been letting competitive bids for express services for around a decade, particular emphasis is placed on the experiences of Houston Metro in the discussions which follow. These six agencies represent most of the population of public entities that contracted for fixed-route bus services in 1987 on a significant scale. Collectively, 18 private contractors or franchise companies presently offer services on 145 different routes among these six jurisdictions, with 51 of the contracted routes consisting of all-day local services. Before probing these additional arguments against contracting, one other issue directly related to the cream-skimming controversy is discussed in this chapter. This is the matter of the degree to which transit properties or political jurisdictions involved with contracting retain control over service and pricing practices. As discussed in the first chapter, lingering behind the cream-skimming argument is the notion that contracting would eventually evolve into a "free-for-all" where private firm could go after any route or service they so choose. Our contention is that, as practiced to date, contracting is a closely controlled activity, with public entities carefully screening the services they allow to be contracted and bidding out only a select number of high-deficit routes. The experiences of the six transit jurisdictions examined in this chapter allow this question to be addressed below. 6.2 Oversight of Competitive Contracts Contracting is sometimes misconstrued to mean that public involvement in the transit arena is somehow lost (Kolderie, 1986). This misconception has unfortunately fueled the cream-skimming controversy. Central to the idea of contracting is the separation of "sponsorship" from .,provision". Sponsorship relates to the promotion and financial support of a service, in particular a commitment to make u any deficits that might exist. Provision, on the other hand, involves the matter of who will actually deliver the service - that is, maintain and operate the buses. Under contracting, sponsorship remains with the public sector, signifying there is a continuing social commitment to maintaining and perhaps even enhancing public transit services in an area. The lowest bidder who meets certain minimum performance standards, however, becomes the provider of the service. To the user, it shouldn't matter whether public or private employees are delivering the service as long as buses are reasonably clean, safe, and on time. Very simply, contracting aims to substitute the lowest cost, competitively determined operation for higher cost, monopolized operations. 66 For the six transit jurisdictions studied that currently engage in contracting, all were found to be sponsors of the contracted services. That is, they paid private contractors a set fee for delivering a stipulated set of services, received all fare receipts, and covered any resulting deficits internally. They also designed all contracted routes, set headways and schedules, and determined what fares would be charged. In all cases, contracts could be rescinded for failure to comply with the terms and conditions of the contracts. Moreover, all contracts that were studied expressly prohibited private service-providers from changing fare levels or structures. While in four of the cases, private contractors were not allowed to change services in any way over the duration of the contract, Westchester County and Johnson County permit changes to be made as long as they were first approved by the public entity. In sum, then, the oversight of all aspects of transit services rests with the public entity undertaking contracting. As long as this is the case, every safeguard is in place to prevent cream-skimming. Very simply, public entities govern what is contracted and what isn't, allowing them to save their best performing services for in-house operation. 6.3 Agency Control of Contracted Services: Example of Houston Metro Background on Metro's Contracting Program The Houston Metro offers insights into the degree of agency control of contracts. Metro has been involved in competitively contracting of fixed-route services about as long as any transit property in the country. Since 1979, Metro has contracted with private firms for a substantial portion of its express services. Most -services connect suburban park-and-ride lots in northern Houston to downtown via High-Occupancy-Vehicle (HOV) lanes. Houston Metro's entry into the contracting business was prompted by the area's explosive growth in the late-1970's and early 1980's, fueled by the petroleum industry's boom. Metro had promised new commute services to a number of suburbs but did not have the equipment or staff to operate then-L Rather than hire new drivers, Metro instead embarked on a load-shedding program, using the available capacity of areawide charter bus companies as peak hour supplements. Initially, Metro granted contracts involving 120 buses on 12 express routes making 430 runs daily (Cervero, 1986). Five private charter firm operated these services, although these firms eventually consolidated into two companies. Since Metro needed to start up these new services quickly, it agreed to terms favorable to the contractors. After the contracts ended in 1984, Metro itself absorbed some of the express bus runs. Services involving only 75 buses operated by two charter companies - Texas Busline and Kerville Bus Company - - were contracted out. Several factors accounted for this retrenchment in contracting. One, the Houston area's economy experienced a sharp downturn in 1984 because of falling gasoline prices, resulting in ridership loses. Equally important, private operators' costs were comparably high to those of Houston Metro in 1984, ranging from $61 to $99 per vehicle hour. As private firms began securing contracts, drivers began unionizing and pushing for salaries comparable those of Metro's drivers. They also won guaranteed eight hour pay provisions, which made companies vulnerable to the cost impacts of peaking. Additionally, as part of each contract, vendors had to supply their own vehicles. Metro generally insisted on the operation of new, premium-quality coaches. Since firms were guaranteed only dime year contracts, they were forced to depreciate equipment over a short time span. Finally, in-house cost estimates made by Metro staff were found to overstate the cost advantages of contracting, resulting in the conversion of some routes to Metro. Currently, eight of the original 12 routes are contracted to GLK Contract Services, Inc., a consortium formed by Kerville Bus Lines and Greyhound. Metro advertised the routes as separate contracts, however GLK chose to bid on all eight routes together, with a winning bid of $71.82 per revenue vehicle hour, slightly below that of the outgoing contractor who had experienced unionization. This rate, however, is considerably below the nearly $100 fully allocated cost that 67 Metro incurs in operating an express bus over a single revenue hour. In all, GLK has agreed to operate 89 buses on the eight express routes for three years, with Metro holding an option to extend the contract for two more years. The contract is a turnkey package in which GLK not only operates the routes but provides maintenance at its own yards. In addition to the GLK contract, Metro has contracted out an all-day local circulator route in Clear Lake, a suburban community, since 1982 to a small private firm As discussed in the next section, this is one of the two examples nationwide where a previously publicly operated service was turned over to a private operator. Metro Oversight of Services For all of the contracts it has entered into to date, Houston Metro has retained complete control over all policy matters related to fares, route design, headways, and general service quality. Metro can change fares or route configurations at any time during a contract period; if such changes add to the contractor's operation cost, the company can notify Metro, whereupon the agency can set a new payment rate if one seems justified. The specific language of Metro's contract with GLK that pertains to public sector control is the following: The Contractor shall provide commuter bus service in a safe, courteous, and reliable manner and in accordance with the schedules provided by METRO's Scheduling Department. METRO representatives may from time to time ride in the Contractor's buses with or without prior notice to the Contractor to ensure compliance with the Contract. METRO representatives may also attend safety and training sessions conducted by the Contractor to monitor and participate in the programs. During the Contract period, METRO may elect to modify the [contracted] routes, schedules, or estimated revenue service hours. Modifications may include, but are not necessarily limited to, the following: extending, deleting or adding routes or portions of routes, and expanding or decreasing revenue hours.2 Where contractors use their own buses, they keep their company logos on the vehicles. Metro affiliation is indicated by a framed placard mounted on the rear of the bus and a route indicator sign and block number on the front of the bus. By retaining its own logo, GLK is free to use its buses for charter work outside of the peak period. In sum, then, as long as transit agencies engaged in contracting enter into a legally binding agreement such as Metro's, every safeguard should be in place to prevent cream-skimming. Very simply, agencies will be able to decide which routes to contract out and the pricing and service characteristics of the contracted services. 6.4 Experiences With Transferring a Publicly Operated Fixed-Route Service For cream-skimming to occur, it is of course necessary that a pre-existing publicly operated fixed-route service be converted over to a privately operated one. As practiced to date, nearly every fixed-route service that has been bid out was a new, start-up service. In most instances, the restriction of competitive contracting to new bus routes has been a concession to organized labor to prevent any litigation over possible violation of UMTA's Section 13(c) labor protection legislation. Clearly, then, the dearth of cases involving the contracting out of pre-existing services 68 means there really are no possibilities for cream-skimming, particularly in light of the fact that new routes usually suffer low ridership counts and high deficits. Over the last several decades, there appears to be only two examples where a pre-existing publicly operated transit service was competitively bid out to a private contractor.3 The first agency to convert unproductive publicly operated bus routes to private operation was the Tidewater Regional,Transit (TRI) in the Norfolk- Virginia Beach area (Cervero, 1986). In the early 1980's, TRT transferred several dial-a-ride operations and two fixed-route bus lines to a taxi company to operate as eight dial-a-ride modules involving 13 vans. As a consequence, TRT reduced its deficit per passenger by 64 percent below their previous levels between 1979 and 1981 (Teal, 1985; Cervero, 1986). Because of the ripple effects, TRT was able to get its own fiscal house in order and win back nearly all of the previously contracted operations. Presently, TRT only contracts out one fixed route service in Portsmouth which serves around 250 passengers per day. The contractor's direct operating cost is around $18 per revenue hour for the two buses the company operates. By comparison, the two runs operated by TRT for a nearly identical Portsmouth bus route is almost twice as high -- $35 per revenue hour (Talley, 1986). The other example of a public-to-private conversion of a pre- existing bus line is Houston's Metro contracting out of the Clear Lake Shuttle. The company awarded the Clear Lake Shuttle contract, Sierra Stagecoaches, Inc., operates at roughly one-half the hourly rate of Houston Metro's own costs for local circulator services -- $20 per revenue hour versus $45. It is noteworthy that when the contract was let, the Clear Lake Shuttle was the poorest performing route in the Metro system based on its cost recovery ratio, which was 6.4 percent. Because the Clear Lake Shuttle continues to be plagued by low ridership and high deficits, Metro plans to discontinue the service sometime in 1989. It is clear, then, that as practiced to date, competitive contracting of pre-existing bus services has involved deficit- skimming rather than cream-skimming. In fact, services that have been converted from public to private operations have been deficit riddled. As long as public transit agencies retain control over what gets contracted and at what price, there should be little occasion for any cream-skimming to occur. 6.5 Concerns Over Deterioration of Service Quality Critics of competitive contracting often contend that private operators will deliver lower quality service than public operators. There is little evidence to support this. Without exception, transit agencies write requirements into all contracts to ensure that service quality will remain comparable to services provided by public operators. In the case of all six transit properties that were studied, contracts contained specific legal language that held contractors responsible for meeting a stipulated number of explicit performance standards in terms of on-time arrivals, safety, maintenance, and the like. AR contracts also contained specific penalties that would be levied for non-compliance as well as provisions for terminating the agreement in the event service quality deteriorated. In the case of Houston Metro, for instance, the contract requires that each contractor achieve the following: 98 percent on- time operations (defined as no more than five minutes behind scheduled arrival time); an average of at least 25,000 miles between road service calls; a maximum of 2.2 accidents per 100,000 miles; and a variety of equipment specification standards prohibiting such deficiencies as tom seats, cracked mirrors or windshields, and inadequate air conditioning systems. Failure to meet any of these standards results in loss of the contractual payment for all affected trips, plus punitive damages, such as $40 per trips for each incidence of a bus leaving any stop before its scheduled departure time.4 Houston Metro has found that contractors usually meet the vast majority of its performance 69 standards. In a typical month, the agency docks a contractor around $600 for violations, a minuscule amount considering the present contractor bills Metro for around $500,000 worth of service each month. Nonetheless, a corp of checkers monitors contracted services on a regular basis to ensure Metro's express service quality remains high. The general perception of Metro management is that, ff anything, service quality has improved on most express routes following contracting. For instance, the prior contractor, National Transit, averaged a 99 percent on-time performance rate and over 35,000 miles between road calls, both well above Metro's own in-house standards. Problems have generally only been encountered when contractors occasionally slack off a month or so before the termination of a contract that will not be renewed. By and large, one would expect private entrepreneurs to be more responsive to the service preferences of transit consumers than a public monopoly. Particularly in the case of peak-hour commuter markets, charter bus companies are accustomed to providing discriminating, often affluent, customers with punctual services, clean and comfortable surroundings, and a guaranteed seat. Houston Metro's experiences generally seem to confirm this. 6.6 Concerns Over Labor Protection Competitive contracting has been perceived as a threat to organized labor. Some critics contend that competitive contracting violates the provisions of Section 13(c) of the federal Urban Mass Transportation Act which expressly protects transit workers from being harmed or displaced by actions of any public agency receiving federal funding. While Section 13(c) itself does not constrain the ability of transit agencies to engage in service contracting as long as workers are not displaced, it nonetheless has created a climate that discourages any kind of contracting that might conceivably worsen the position of public employees (Teal, 1986). To date, there is no single case of a transit employee having lost his or her job directly because of service contracting. This is due in large part to the fact that the overwhelming majority of contracts let to date have involved the operation of new, supplemental services. Thus, transit agencies that have contracted out services to date have been in a status quo rather than a retracted posture. Of the cases where previously existing services have been contracted out to private firms, discussed in section 6.4 above, no employees were let go as a consequence. With both TRT and Houston Metro, agency drivers of the contracted routes were reassigned to other tour duties following private take-over. In sum, the contention that public sector employees will be harmed by contracting is unfounded. Over the long run, of course, transit agencies might be able to reduce the size of their labor force at the natural attrition rate. Such a phased policy of expanded service contracting would not run afoul of either the letter or intent of Section 13(c). With time, then, service contracting would likely involve a shift in the composition of the nation's transit work force from public to private sector employment. This shift, however, would only occur as new employees are hired rather than through any forced displacement of public sector workers into the private sector. 6.7 Concerns Over Eventual Contractor Cost Increases Some observers have argued that as competitive contracting gains momentum, the costs of the private sector will rise to the levels found in the public sector. As private firms grow and take on new markets, the argument goes, their overheads and labor costs will eventually rise as their workers begin to unionize and they begin to experience some of the scale diseconomies associated with public transit operations. While this has occurred in a few instances, in most cases the effects of competition have been felt more on the public sector than private sector side. That is, experiences show that the 70 public sector's unit cost generally falls to the level of the private sector's rather than the private sector's unit cost rising to that of the transit agency. For example, when TRT (in the Norfolk, Virginia area) substituted private dial-a-ride services for a previous publicly operated fixed-route service, labor became alarmed (Cervero, 1986). This move marked an unprecedented entry of efficiency-minded entrepreneurs into what had historically been TRT's sole province. Believing that their own livelihoods might be at stake, driver's immediately filed a suit alleging infringement of their Section 13(c) protectionist rights. Since no public employees were fired and there were demonstrable cost savings to the agency from contracting, the courts ruled in TRT's favor. Seeing the writing on the wall, TRT's labor representatives agreed to several major concessions. One, guaranteed pay was reduced from 8 to 7.5 hours. Second, a minibus operator position was created at a starting salary of $4 per hour, with no work rule restrictions and reduced benefits. Since minibus drivers receive no out-of- service compensation, TRT's average ratio of in-service hours to pay hours rose from 0.87 in 1983 to 0.93 in 1984 (Talley, 1986). As a result, TRT's minibus paratransit division was able to competitively win back dial-a-ride services from private taxi companies in 1985. Where the unit costs of private contractors begin to rise because of unionization pressures, periodic rebidding of contracts would seem, in most cases, to provide an effective safety valve for controlling costs. In the case of Houston Metro, for instance, the two original contractors became unionized after receiving Metro's express services contract. When the routes were rebid, these companies were underbid by newer, non-unionized firms. Moreover, Metro has been able to win back some of its originally contracted services by lowering some its in-house expenses so as to become competitive. Among the transit agencies engaged in fixed-route contracting that were studied, all issued requests for new bids on a periodic basis, usually every two or three years. Such rebidding enables competitive contracting to continue holding costs in check. Of course, for the forces of competition to be felt, there must be a sufficient number of firms willing to submit bids. Among the six public entities engaged in fixed-route contracting that were studied, between 3 and 8 private firms bid on virtually every contract that was auctioned off. In roughly half of the cases, the existing contractor came in with the lowest bid. Overall, as long as a highly competitive environment is maintained, contracting should prevent any significant cost escalation within either the public or private sector from occurring. 6.8 Conclusion In this chapter, some of the "other" arguments lodged against competitive contracting under the banner of cream-skimming were probed. It was found that public agencies engaged in service contracting maintain such tight reigns over what private entities do that there are few opportunities for the public sector be hurt, whether through cream-skimming or some other predatory practice. By remaining the funding sponsors of all transit services, agencies can ensure that none of their best performing routes go out to bid. Experiences show that only the highest deficit, lowest ridership routes are contracted. Such controls also allow public entities to closely scrutinize the performance of private contractors. When safety or service quality are compromised, agencies can exact penalties against contractors or terminate the agreement. In addition, for cream-skimming to occur, a pre-existing public transit service must be converted to private operation. There have only been a few instances where this has actually occurred. As practiced to date, contracting has almost exclusively involved the bidding out of new, supplemental services. In the few cases where a public-to-private conversion of fixed-route service took place, services were among the poorest performing ones within the agency and no public sector employees were furloughed as a result. By and large, service quality seems to have remained unchanged or improved slightly following contracting. Explicit performance standards written into every fixed-route service 71 contract have guaranteed this. Through periodic rebidding, moreover, transit agencies have managed to maintain a competitive enough environment to ensure that the unit costs of delivering service remain under control as the scope of contracting enlarges. In sum, there appears to be no "other" justifiable grounds for dismissing competitive contracting under the guise of cream- skimming. Notes 1. DART, which contracts services to Trailways Commuter Transit, Inc., accounts for 52 of the routes. Westchester County, which operates as a franchise, contracts out services for 38 routes. The remaining four public entities bid out no more than 20 fixed-route services. 2. Metropolitan Transit Authority. Invitation for Bids for Commuter Bus Service. Metro Invitation for Bids No. 88H032B. Exhibit "A", Scope of Service, Computer Bus Service, March 17, 1988, p. 2. 3. This statement is based on an extensive literature review and telephone contacts with transit agencies around the country known to contract out services. 4. A host of other penalties are exact for non-compliance. Late departures: If a bus departs from a stop between five and ten minutes late, the penalty is $30 per trip. If the departure is 10 to 15 minutes late, the penalty is $50 per trip. Incomplete trips: If a bus departs more than 15 minutes late, or fails to complete the trip, the contractor does not receive the contractual payment for that trip. Missed trips: If the contractor cannot meet a pullout due to shortage of buses, it forgoes the contractual payment for that trip and pays an additional $100 fine. However, if the contractor provides Metro with at least 90 minutes advance notice of a missed pullout, the additional penalty is waived, and the contractor loses only the contractual payment for the trip. Deficient buses: If any bus on any trip fails to comply with all vehicle standards set forth by Metro, a penalty of $50 per violation is assessed. Such violations could include dirty buses, inadequate air conditioning, cracked mirrors or windows, tom seats, or inoperative lights. Fleet shortage: If the contractor fails to maintain the total fleet required by the contract, including not only the scheduled vehicles but a 10 percent spare vehicle requirement, a penalty of $200 per violation is assessed. In the GLK contract, the company must maintain a fleet of 89 route buses plus ten percent of that amount as spares, for a total of 99 buses. 72 Chapter Seven Research Conclusions: Competitive Contracting and Deficit-Skimming 7.1 Debunking the Cream-Skimming Myth The cream-skimming argument against competitive contracting rests on a number of assumptions that are unsupported. The absence of significant numbers of profitable routes and general scale economies in the transit industry, coupled with strict public sector control of all aspects of service delivery and pricing, make cream-skimming a virtual impossibility. The three main findings of this research which render the cream-skimming argument meaningless are summarized below: (1) There are very, very few profitable fixed-route bus services in the U.S. from which any "cream" could possibly be skimmed. Even when only, operating portions of total costs are considered, nearly all bus routes in the U.S. are plagued by deficits. In fact, less than one percent of all fixed-route bus services currently operated by medium and large size transit agencies in the U.S. cover or exceed their direct, day-to-day operating expenses through passenger fares. (2) There is little evidence of any significant economies Of scale in the transit industry, particular agencies, meaning there is no real economic justification for protecting transit properties from competition. This and other research show unequivocally that the unit cost of delivering bus services rise when vehicle miles are increased. The absence of any inherent increasing returns to scale means there is no real grounds for creating a public monopoly, particularly given that the peak loads agencies risk losing are inordinately costly to serve. (3) In all instances to date, public agencies control which routes private bidders are given an opportunity to take over, meaning that, without exception, agencies have retained their best performing routes for in-house operation. By remaining the funding sponsors of all transit services, public authorities are in a position to hold back any routes they so choose from possible bidding. Experiences to date show that only the highest deficit, poorest performing routes are ever contracted. Moreover, only new services with high start-up costs and unproven track records end up being contracted. There have only been two instances where pre- existing bus routes were contracted out for private operation, and in both cases the routes were among the agencies' poorest performing. Further, public agencies maintain control over all aspects of service delivery and pricing when contracts are let and impose strict penalties for non-compliance. Thus, public entities not only determine what gets contracted out, but the nature and price of the services that the traveling public receives. To the extent any of these conditions exists, there is virtually no chance that cream-skimming could occur. To date, these conditions hold for every conceivable situation where transit service contracting might take place. There is no compelling reason not to believe this will also be the case in the future. 7.2 Contracting and Deficit-Skimming Overall, this research has found that competitive contracting of fixed-route transit services, as practiced today as well as the foreseeable future, actually results in deficit-skimming. Rather 73 than ruthless predators, contractors are actually friends of the public transit sector. They take over the least productive routes and usually deliver a comparable quality of service at a lower deficit rate - and without causing any public employees to be furloughed or disrupting the continued operations on non-contracted bus services. This research also found that deficits per rider tend to be the highest on the very services that the private sector has shown the greatest interest in providing -- peak-only, express routes. This is mainly because firms can provide service-conscientious riders a high quality door-to-door ride on premium coaches during the peak and deploy the vehicles for special charter operations to recreational sites and the like during the midday, late-evening, and weekends. Thus, charter companies benefit by making full use of their buses and drivers throughout the week while the public sector gains by shedding high-deficit routes to lower-cost vendors. In general, competitive contracting of peak period services should result in a win-win situation for both the public and private sectors. In closing, regulatory constraints in the urban transportation markets need to be relaxed. To the extent more charter buses, shared-ride taxis, jitneys, and private dial-a-vans are allowed to compete for peak hour customers, the riding public should materially benefit. Besides shedding expensive peak loads from public transit agencies, opening up the market to more private firms would provide more commuters with a higher quality service than that offered by conventional bus transit. Since paratransit modes would begin to provide services which are more competitive with those of the private automobile, overall vehicle occupancy levels within a region would eventually increase, yielding such social benefits as reduced congestion, cleaner air, and fuel savings. Growth in the paratransit sector would also mean that more specialized curb-to-curb services would be available for disabled persons and older Americans, the latter being one of the fastest growing age groups in the nation. In summary, increased competitive contracting of bus services would not only aid public transit systems by reducing their deficits, it would probably also lead to the expansion of paratransit services in this nation, benefitting the commuter, the elderly, the disabled, and the public at large. 74 References American Public Transit Association. 1982. Transit Fact Book. Washington: American Public Transit Association. American Public Transit Association. 1987. Transit Fact Book. Washington: American Public Transit Association. Berechman, J. 1982. Analysis of Costs, Economies of Scale and Factor Demand in Bus Transport. Irvine: Institute of Transportation Studies, University of California, Report UCI-ITS- SP-82-2. Berechman, J. and G. Giuliano. 1982. 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Economic Characteristics of the Urban Public Transportation Industry. Washington: Institute of the Defense. Williams, M. 1979. Firm Size and Operating Cost in Urban Bus Transportation. Journal of Industrial Economics 28, 2: 209-218. 80 Appendix A Transit Agencies Examined in the Research, Listed by Headquarters City and Peak Fleet Size Peak Agency Fleet Washington Metropolitan Area Transit Authority (WMATA) 1,378 Washington, D.C. Southeastern Pennsylvania Transportation Authority (SEPTA) 1,112 Philadelphia, PA Metro Seattle 920 Seattle, WA Metropolitan Transit Commission (MTC) 826 Minneapolis, MN Metropolitan Transit Authority of Harris County 659 Houston, TX Denver Regional Transit District (RTD 635 Denver, CO Bi-State Development Corporation 634 St. Louis, MO PACE 473 Arlington Heights, IL Santa Clara County Transportation Agency 402 San Jose, CA Orange County Transit District 325 Garden Grove, CA Central Ohio Transit Authority 287 Columbus, OH Utah Transit Authority 275 Salt Lake City, UT Phoenix Transit 274 Phoenix, AZ Westchester County Department of Transportation 250 White Plains, NY 81 Appendix A (continued) Peak Agency Fleet Golden Gate Transit Bus Division 210 San RafaeL CA Southeastern Michigan Transportation Authority (SEMTA) 203 Detroit, MI Sacramento Regional Transit District (RTD) 184 Sacramento, CA Jacksonville Transportation Authority 151 Jacksonville, FL Greater Richmond Transit Company 150 Richmond, VA Tidewater Regional Transit System 147 Norfolk, VA Hillsborough Area Regional Transit Authority (HART) 135 Tampa, FL SunTran 120 Tucson, AZ Fort Worth Transportation Authority 102 Forth Worth, TX Metro Tulsa Transit Authority 84 Tulsa, OK Transit Authority of Northern Kentucky (TANK) 80 Fort Wright, KY Peak Fleet Size is the number of motorbuses, including articulated motorbuses, available for peak service. 82 Appendix B Variables Used in the Analysis Variables Submitted by Transit Agencies. Ridership (revenue-paying) Operating Cost Farebox Revenue Total Vehicle Miles Total Vehicle Hours Peak Vehicles in Operation Variables Calculated from Submitted-Data: Cost Recovery Ratio Cost per Vehicle Mile Cost per Vehicle Hour Cost per Rider Deficit per Rider Vehicle Miles per Dollar of Expenditure Riders per Dollar of Expenditure Riders per Vehicle Hour Riders per Vehicle Mile Revenue per Vehicle Mile 83 Appendix C Three-Factor Cost Allocation Model, Calculated for Seattle Metro and Denver RTD Example: Seattle Metro Variable and Dollar Amount Allocated: Vehicle Vehicle Peak UMTA Cost Function Hours Miles Vehicles Transportation Administration $4,653,420 Revenue Vehicle Movement Control $2,189,916 Scheduling Transp. Operations $1,968,456 Revenue Vehicle Operation $51,159,691 Vehicle Maintenance Administration $3,153,753 Servicing Revenue Vehicles $3,760,328 Inspection and Maintenance of Revenue Vehicles $12,860,003 Accident Repairs to Vehicles $355,229 Vandalism Repairs to Vehicles $72,581 Maintenance of Vehicle Movement Control Systems $101,580 Maintenance of Fare Collecting Equipment $87,253 Ticketing Fare Collection $502,056 Insurance $75,809 Total Allocated Costs: $59,971,483 $16,523,146 $4,425,446 Divide by 1985 Totals: 2,068,700 31,245,900 920 vehicle hours vehicle miles vehicles Unit Costs: $28.99 $0.53 $4810.27 per vehicle hour per vehicle mileper vehicle 84 Appendix C (continued) Seattle Metro (continued) Allocate annual $4810-27 per-vehicle cost among 250 weekdays for peak routes, and among 365 days for all-day local routes operating seven days a week. $4810.27 / 250 = $18.86 per vehicle for peak-only routes $4810.27 / 365 = $13.18 per vehicle for all-day local routes Final Model: Daily Peak Route Cost = $28.99 x daily route vehicle hours + $ 0.53 x daily route vehicle miles + $18.86 x vehicles used on route Daily Local Route Cost = $28.99 x daily route vehicle hours + $ 0.53 x daily route vehicle miles + $13.18 x vehicles used on route Computed Route Costs: Daily Daily Daily Vehicle Vehicle Peak Daily Route Type Hours Miles Vehicles Cost 41 Peak 75.0 1,374 12 $3,129 177 Peak 78.0 1,956 10 $3,486 301 Peak 40.0 805 8 $1,737 7 Local 390.0 3,568 31 $13,606 10-12 Local 166.0 1,028 12 $5,515 3-4 Local 207.0 1,372 16 $6,939 85 Appendix C (continued) Example Denver RTD Final Model: Daily Peak Route Cost = $25.81 x daily route vehicle hours + S 0.76 x daily route vehicle miles + $27.15 x vehicles used on route Daily Local Route Cost = $25.81 x daily route vehicle hours + $ 0.76 x daily route vehicle miles + $18.97 x vehicles used on route Computed Route Costs: Daily Daily Daily Vehicle Vehicle Peak Daily Route Type Hours Miles Vehicles cost 90x Peak 41.1 925 9 $2,008 120X Peak 13.7 349 3 $700 59X Peak 18.9 409 4 907 15 Local 220.4 2,358 13 $7,727 30 Local 194.9 2,650 12 $7,272 0 Local 198.0 2,435 13 $7,208 86 Appendix D Cost Allocation Models for Analysis Example: Bi-State Development Corp. Variable and Dollar Amount Allocated: Vehicle Vehicle Peak UMTA Cost Function HOUR Miles Vehicles Revenue Vehicle Movement Control $148,949 Scheduling Transp. Operations $326,444 Revenue Vehicle Operation $41,623,890 Revenue Vehicle Maintenance $16,729,170 (Minus Administration) Servicing Revenue Vehicles $1,867,469 Casualty and Liability Insurance $2,319,578 Total Allocated Costs: $42,099,283 $16,729,170 $4,187,047 Divide by 1985 Totals: 1,781,700 4,652,100 634 vehicle hours vehicle miles vehicles Unit Costs: $23.63 $0.68 $6,604.17 per vehicle hour + $0.37* per vehicle per vehicle mile Allocate annual $6,604.17 per-vehicle cost among 250 annual service days for peak routes, and among 365 service days for all- day local routes operating seven days a week. $6,604.17 / 250 = $26.42 per vehicle for peak-only routes $6,604.17 / 365 = $18.09 per vehicle for all-day local routes * use-related depreciation cost per mile, added to all transit systems in the database. 87 Appendix D (continued) Bi-State Development Corp. (continued) Final Model: Daily Peak Route Cost = $23.63 x daily route vehicle hours + $ 1.05 x daily route vehicle miles + $26.42 x vehicles used on route Daily Local Route Cost = $23.63 x daily route vehicle hours + $1.05 x daily route vehicle miles + $18.09 x vehicles used on route Computed Route Costs: Daily Daily Daily Vehicle Vehicle Peak Daily Route Type Hours Miles Vehicles Cost 4560 Belleville Peak 30.7 623 12 $1,697 4504 O'Fallon Peak 313 654 9 1,664 4530 Pontoon Peak 28.4 461 8 1,366 3070 Grand Local 178.1 1,655 17 6,254 3095 Kings Local 210.2 2,427 18 7,841 3560 Belleville Peak 167.4 2,301 20 6,733 88 Appendix D (continued) Example. Westchester County (NY) Department of Transportation Variable and Dollar Amount Allocated: Vehicle Vehicle Peak UMTA Cost Function Hours Mile Vehicles Vehicle Operations $18,849,396 minus 6% (Administration) - $1,130,964 Vehicle Operations $17,718,432 Vehicle Maintenance $6,985,364 minus 14.84% (Servicing) -$1,036,628 minus 14.30% (Administration) - $ 850,669 _____________ Vehicle Maintenance $5,098,067 Servicing Revenue Vehicles $1,036,628 Casualty and Liability $1,367,505 __________________________________________ Total Allocated Costs: $17,718,432 $5,098,067 $2,404,133 Divide by 1985 Totals: 765,000 11,006,800 250 vehicle hours vehicle miles vehicles Unit Costs: $23.16 $0.46 $9,616.53 per vehicle hour + 0.37* per vehicle per vehicle mile Allocate total $9,616 annual cost per vehicle over 250 weekdays for peak-only express routes, or 365 days for all-day local routes operating seven days a week. $9,616.53/ 250 = $38.47 per vehicle for peak-only routes $9,616.53/ 365 = $26.35 per vehicle for all-day local routes * use-related deprecation, added to all systems in database. 89 Appendix D (continued) Westchester County (continued) Final Model: Daily Peak Route Cost = $23.16 x daily vehicle hours + $ 0.83 x daily vehicle miles + $38.47 x vehicles used on route Daily Local Route Cost = $23.16 x daily vehicle hours + $ 0.83 x daily vehicle miles + $26.35 x vehicles used on route Computed Route Costs: Daily Daily Daily Vehicle Vehicle Peak Daily Route Type Hours Miles Vehicles Cost 41 Peak 20.1 303 6 $948 3 Peak 30.4 644 9 $1,585 62 Peak 15.2 272 7 $847 20 Local 189.9 2,357 15 $6,750 1 Local 115.5 1,621 19 $4,521 40 Local 118.7 1,580 14 $4,429 90 Appendix D (continued) Washington Metropolitan Ama Transit Authority (WMATA) Final Model: Daily Peak Route Cost = $28.10 x daily route vehicle hours + $ 1.08 x daily route vehicle miles + $34.16 x vehicles used on route Daily Route Cost = $28.10 x daily route vehicle hours + $ 1.08 x daily route vehicle miles + $23.47 x vehicles used on route Computed Route Costs; Daily Daily Daily Vehicle Vehicle Peak Daily Route Type Hours miles Vehicles cost Lincoln-N.Fairfax Peak 93.8 1,602 22 $5,120 South Capitol Peak 71.6 958 14 $3,526 11th St. Bridge Peak 79.4 988 15 $3,812 Pennsylvania Ave. Local 314.8 3,109 54 $13,471 Benning Road Local 183.0 1,739 29 $7,701 Georgia-7th Local 255.1 2,236 45 $10,639 91 Appendix D (continued) Southeastern Pennsylvania Transportation Authority Final Model: Daily Peak Route Cost = $19.86 x daily route vehicle hours + $ 1.12 x daily route vehicle miles + $21.03 x vehicles used on route Daily Local Route Cost = $19.86 x daily route vehicle hours + $ 1.12 x daily route vehicle miles + $14.41 x vehicles used on route Computed Route Costs: Daily Daily Daily Vehicle Vehicle Peak Daily Route Type Hours Miles Vehicles Cost 44G Peak 33.0 458 5 $1,273 4 Peak 5.6 78 2 $241 126 Peak 5.0 79 1 $209 C Local 526.1 4,431 46 $16,074 52 Local 297.8 2,175 26 $8,725 33 Local 294.5 1,855 24 $8,272 92 Appendix D (continued) Seattle Metro Final Model: Daily Peak Route Cost = $26.74 x daily route vehicle hours + $ 0.94 x daily route vehicle miles + $25.55 x vehicles used on route Daily Local Route Cost = $26.74 x daily route vehicle hours + $ 0.94 x daily route vehicle miles + $17.50 x vehicles used on route Computed Route Costs: Daily Daily Daily Vehicle Vehicle Peak Daily Route Type Hours Miles Vehicles Cost 41 Peak 75.0 1,374 12 $3,604 177 Peak 78.0 1,956 10 $4,180 301 Peak 40.0 805 8 $2,031 7 Local 390.0 3,568 31 $14,325 10-12 Local 166.0 1,028 12 $5,615 3-4 Local 207.0 1,372 16 $7,105 93 Appendix D (continued) Minneapolis Metropolitan Transit Commission Final Model: Daily Peak Route Cost = $25.09 x daily route vehicle hours + $ 1.00 x daily route vehicle miles + $15.06 x vehicles used on route Daily Local Route Cost = $25.09 x daily route vehicle hours + $ 1.00 x daily route vehicle miles + $1031 x vehicles used on route Computed Route Costs: Daily Daily Daily Vehicle Vehicle Peak Daily Route Type Hours Miles Vehicles Cost 26 West River Peak 75.0 1,564 15 $3,672 47 Express Peak 88.3 1,402 11 $3,781 35B Express Peak 32.5 552 8 $1,488 5 Local 4343 5,544 36 $16,812 18 Local 336.5 3,881 36 $12,695 21 Local 261.8 2,598 23 $9,404 94 Appendix D (continued) Houston metropolitan Transit Authority Final Model: Daily Peak Route Cost = $22.97 x daily route vehicle hours + $ 0.97 x daily route vehicle miles + $30.63 x vehicles used on route Daily Local Route Cost = $22.97 x daily route vehicle hours + S 0.97 x daily route vehicle miles + $20.98 x vehicles used on route Computed Route Costs: Daily Daily Daily Vehicle Vehicle Peak Daily Route Type Hours Miles Vehicles cost 212 Seton Peak 45.0 1,224 30 $3,140 204 Spring Peak 61.9 1,822 36 $4,292 205 Kingwood Peak 56.3 1,460 30 $3,628 82 Westheimer Local 226.1 2,618 29 $8,341 25 Richmond Local 195.5 2,299 19 $7,119 52 Scott Local 182.6 2,335 27 $7,026 201 North Shepherd Peak 39.1 913 21 $2,427 261 West Loop Peak 27.0 630 14 $1,660 59 SW Freeway Peak 12.6 270 6 $735 95 Appendix D (continued) Denver RTD Final Model: Daily Peak Route Cost = $25.06 x daily route vehicle hours + $ 1.20 x daily route vehicle miles + $27.00 x vehicles used on route Daily Local Route Cost = $25.06 x daily route vehicle hours + $ 1.20 x daily route vehicle miles + $18.49 x vehicles used on route Computed Route Costs: Daily Daily Daily Vehicle Vehicle Peak Daily Route Type Hours Miles Vehicles Cost 90x Peak 41.1 925 9 $2,383 120X Peak 13.7 349 3 $843 59X Peak 18.9 409 4 $1,072 15 East Colfax Local 220.4 2,358 13 $8,593 30 South Fed Local 194.9 2,650 12 $8,286 0 South Broadway Local 198.0 2,435 13 $8,124 96 Appendix D (continued) PACE Final Model: Daily Peak Route Cost = $22.40 x daily route vehicle hours + S 1.13 x daily route vehicle miles + $30.61 x vehicles used on route Daily Local Route Cost = $22.40 x daily route vehicle hours + $ 1.13 x daily route vehicle miles + $20.96 x vehicles used on route Computed Route Costs: Daily Daily Daily Vehicle Vehicle Peak Daily Route Type Hours Miles Vehicles cost 223 Elk/River Peak 32.6 287 5 $1,208 325 25th AvenuePeak 22.0 302 3 $926 312 Ogden Peak 7.1 66 1 $264 290/1 Touhy Local 109.7 1,467 11 $4,345 270 Milwaukee Local 96.9 1,044 8 $3,518 307 Harlem Local 107.2 1,475 11 $4,299 97 Appendix D (continued) Santa Clara County (C4) Transit Final Model: Daily Peak Route Cost = $30.17 x daily route vehicle hours + $ 137 x daily route vehicle miles + $45.20 x vehicles used on route Daily Local Route Cost = $30.17 x daily route vehicle hours + S 137 x daily route vehicle miles + $30.96 x vehicles used on route Computed Route Costs: Daily Daily Daily Vehicle Vehicle Peak Daily Route Type Hours Miles Vehicles Cost 142 Peak 101.4 1,945 11 $6,221 101 Peak 52.1 1,046 8 $3,366 104 Peak 37.5 881 5 $2,564 22 Local 27.8 601 5 $1,817 70 Local 430.4 5,375 30 $21,278 64 Local 168.2 2,728 11 $9,152 98 Appendix D (continued) Orange County (CA) Transit District Final Model: Daily Peak Route Cost = $29.05 x daily route vehicle hours + $ 0.90 x daily route vehicle miles + $34.93 x vehicles used on route Daily Local Route Cost = $29.05 x daily route vehicle hours + $ 0.90 x daily route vehicle miles + $23.92 x vehicles used on route Computed Route Costs: Daily Daily Daily Vehicle Vehicle Peak Daily Route Type Hours Miles Vehicles Cost 59 Peak 48.4 863 5 $2,357 36 Peak 19.0 330 4 $989 78 Peak 26.7 623 2 $1,406 43 Local 261.3 3,677 25 $11,498 57 Local 212.1 3,227 20 $9,544 60 Local 154.5 2,132 12 $6,694 99 Appendix D (continued) Central Ohio Transit Authority Final Model: Daily Peak Route Cost = $25.78 x daily route vehicle hours + $ 0.70 x daily route vehicle miles + $21.97 x vehicles used on route Daily Local Route Cost = $25.78 x daily route vehicle hours + $ 0.70 x daily route vehicle miles + $15.05 x vehicles used on route Computed Route Costs: Daily Daily Daily Vehicle Vehicle Peak Daily Route Type Hours Miles Vehicles Cost 31 Worthington Peak 13.7 285 8 $728 45 Reynoldsburg Peak 34.7 678 8 $1,545 46 Eastland Peak 24.6 440 5 $1,052 2 Main/North High Local 315.0 4,262 36 $11,646 1 Cleveland Local 238.1 3,232 22 $8,732 10 Broad Street Local 215.3 3,117 23 $8,078 100 Appendix D (continued) Utah Transit Authority Final Model: Daily Peak Route Cost = $17.00 x daily route vehicle hours + $ 0.73 x daily route vehicle miles + $20.99 x vehicles used on route Daily Local Route Cost = $17.00 x daily route vehicle hours + $ 0.73 x daily route vehicle miles + $14.38 x vehicles used on route Computed Route Costs: Daily Daily Daily Vehicle Vehicle Peak Daily Route Type Hours Miles Vehicles Cost 55 Peak 14.4 295 6 $586 46 Peak 4.6 76 4 $218 63 Peak 6.5 77 4 $251 24 Local 75.4 986 12 $2,174 18 Local 20.8 214 7 $610 22 Local 38.1 411 9 $1,077 101 Appendix D (continued) Phoenix Transit Final Model: Daily Peak Route Cost = $22.79 x daily route vehicle hours + $ 0.87 x daily route vehicle miles + $24.70 x vehicles used on route y Daily Local Route Cost = $22.79 x daily route vehicle hours + $ 0.87 x daily route vehicle miles + $16.92 x vehicles used on route Computed Route Costs: Daily Daily Daily Vehicle Vehicle Peak Daily Route Type Hours Miles Vehicles Cost 521 Peak 10.9 194 5 $541 500 Peak 10.1 197 3 $476 520 Peak 11.0 204 5 $552 29 Local 170.5 2,314 13 $6,119 41 Local 152.3 2,287 12 $5,664 17 Local 161.1 2,266 11 $5,829 102 Appendix D (continued) Golden Gate Transit Final Model: Daily Peak Route Cost = $40.54 x daily route vehicle hours + $ 0.78 x daily route vehicle miles + $45.14 x vehicles used on route Daily LoW Route Cost = $40.54 x daily route vehicle hours + $ 0.78 x daily route vehicle miles + $30.92 x vehicles used on route Computed Route Costs: Daily Daily Daily Vehicle Vehicle Peak Daily Route Type Hours Miles Vehicles Cost 4 Peak 39.2 1,255 20 $3,471 54 Peak 58.8 1,608 18 $4,450 74 Peak 66.7 2,118 20 $5,259 20 Local 126.0 1,918 10 $6,913 80 Local 145.2 3,425 14 $8,991 50 Local 101.4 1,945 11 $7,744 103 Appendix D (continued) Southeastern Michigan Transportation Authority Final Model: Daily Peak Route Cost = $30.03 x daily route vehicle hours + S 0.99 x daily route vehicle miles + $38.75 x vehicles used on route Daily Local Route Cost = $30.03 x daily route vehicle hours + $ 0.99 x daily route vehicle miles + $26.54 x vehicles used on route Computed Route Costs: Daily Daily Daily Vehicle Vehicle Peak Daily Route Type Hours Miles Vehicles Cost 820 Peak 50.0 1,355 9 $3,192 851 Peak 39.0 1,000 7 $2,432 810 Peak 34.0 910 6 $2,154 440/450/460 Local 192.0 3,455 17 $9,637 200 Local 134.0 2,270 12 $6,590 560 Local 125.0 2,600 16 $6,752 104 Appendix D (continued) Sacramento Regional Transit District Final Model: Daily Peak Route Cost = $28.54 x daily route vehicle hours i $ 0.76 x daily route vehicle miles + $33.38 x vehicles used on route Daily Local Route Cost = $28.54 x daily route vehicle hours + $ 0.76 x daily route vehicle miles + $22.86 x vehicles used on route Computed Route Costs: Daily Daily Daily Vehicle Vehicle Peak Daily Route Type Hours Miles Vehicles Cost 31 Peak 13.8 154 6 $711 108 Peak 17.5 487 6 $1,070 102 Peak 12.0 342 5 $769 30 Local 61.0 560 6 $2,304 34 Local 95.3 1,097 8 $3,736 51 Local 60.5 819 5 $2,463 105 Appendix D(continued) Jacksonville Transportation Authority Final Model: Daily Peak Route Cost = $17.98 x daily route vehicle hours + $ 0.71 x daily route vehicle miles + $22.79 x vehicles used on route Daily Local Route Cost = $17.98 x daily route vehicle hours + $ 0.71 x daily route vehicle miles + $15.61 x vehicles used on route Computed Route Costs: Daily Daily Daily Vehicle Vehicle Peak Daily Route Type Miles Vehicles Vehicles Cost Mandarin Peak 8.5 234 3 $387 Orange Peak Peak 6.5 178 2 $289 Blanding Peak 6.5 118 2 $246 Northside 6 Local 80.5 1,096 7 $2,335 Northside 5 Local 74.0 950 6 $2,099 Beaches 1 Local 59.0 1,150 8 $2,002 106 Appendix D (continued) Greater Richmond Transit Final Model: Daily Peak Route Cost = $20.41 x daily route vehicle hours + $ 0.75 x daily route vehicle miles + $17.75 x vehicles used on route Daily Local Route Cost = $20.41 x daily route vehicle hours + $ 0.75 x daily route vehicle miles + $12.16 x vehicles used on route Computed Route Costs: Daily Daily Daily Vehicle Vehicle Peak Daily Route Type Hours Miles Vehicles cost 26 Peak 15.3 387 7 $727 67 Peak 6.0 99 3 $250 66 Peak 5.1 97 2 $212 34 Highland Local 154.3 1,740 17 $4,661 6 Broad/Main Local 108.8 1,154 10 $3,208 37 Chamberlane Local 51.5 567 7 $1,561 107 Appendix D (continued) Tidewater Transit Final Model: Daily Peak Route Cost = $18.59 x daily route vehicle hours + S 0.79 x daily route vehicle miles + $25.05 x vehicles used on route Daily Local Route Cost = $18.59 x daily route vehicle hours + $ 0.79 x daily route vehicle miles + $17.16 x vehicles used on route Computed Route Costs: Daily Daily Daily Vehicle Vehicle Peak Daily Route Type Hours Miles Vehicles cost 20 Express Peak 10.0 196 2 $391 35-15 Peak 5.1 120 2 $240 1-23 Local 88.9 1,255 6 $2,747 15-35 Local 94.0 1,137 8 $2,783 20-25 Local 82.1 938 6 $2,370 3-25 Local 85.2 1,011 8 $2,520 108 Appendix D (continued) Hillsborough Area (FL) Transportation Authority Final Model: Daily Peak Route Cost = $16.30 x daily route vehicle hours + $ 0.61 x daily route vehicle miles + $22.47 x vehicles used on route Daily Local Route Cost = $16.30 x daily route vehicle hours + $ 0.61 x daily route vehicle miles + $1539 x vehicles used on route Computed Route Costs: Daily Daily Daily Vehicle Vehicle Peak Daily Route Type Hours Miles Vehicles Cost 22X Peak 35.0 412 4 $912 21X Peak 11.6 303 4 $464 99X Peak 17.7 442 3 $625 12 USF/22nd Local 62.2 745 5 $1,545 1 Florida Avenue Local 56.9 835 5 $1,514 5 USF/40th Local 64.1 996 5 $1,729 109 Appendix D (continued) Tucson Sun Tran Final Model: Daily Peak Route Cost = $19.13 x daily route vehicle hours + $ 0.71 x daily route vehicle miles + $17.67 x vehicles used on route Daily Local Route Cost = $19.13 x daily route vehicle hours + S 0.71 x daily route vehicle miles + $12.10 x vehicles used on route Computed Route Costs: Daily Daily Daily Vehicle Vehicle Peak Daily Route Type Hours Miles Vehicles cost Hughes Peak 34.6 466 12 $1,205 Bear Canyon Peak 2.5 26 1 $84 8 Local 1783 2,345 16 $5,269 4 Local 86.1 1,171 10 $2,599 3 Local 90.2 1,266 9 $2,733 110 Appendix D (continued) Fort Worth Transportation Authority Final Model: Daily Peak Route Cost = $16.78 x daily route vehicle hours + S 0.74 x daily route vehicle miles + $21.50 x vehicles used on route Daily Local Route Cost = $16.78 x daily route vehicle hours + $ 0.74 x daily route vehicle miles + $14.73 x vehicles used on route Computed Route Costs: Daily Daily Daily Vehicle Vehicle Peak Daily Route Type Hours Miles Vehicles Cost 54 Peak 1.4 26 2 $86 61 Peak 1.0 19 2 $74 14 Peak 1.2 17 2 $76 38/73 Local 121.5 1,449 10 $3,258 33/57 Local 97.5 1,250 9 $2,694 32/58/59 Local 119.3 1,693 13 $3,446 111 Appendix D (continued) Metropolitan Tulsa Transit Authority Final Model: Daily Peak Route Cost = $19.40 x daily route vehicle hours + S 0.86 x daily route vehicle miles + $20.66 x vehicles used on route Daily Local Route Cost = $19.40 x daily route vehicle hours + $ 0.86 x daily route vehicle miles + $14.15 x vehicles used on route Computed Route Costs: Daily Daily Daily Vehicle Vehicle Peak Daily Route Type Hours Miles Vehicles Cost 103 Peak 2,7 52 2 $138 107 Peak 3.8 65 2 $171 104 Peak 3.4 62 2 $161 1 Local 56.4 792 5 $1,846 20 Local 75.5 1,187 6 $2,570 5 Local 35.1 574 3 $1,217 112 Appendix D(continued) Transit Authority of Northern Kentucky Final Model: Daily Peak Route Cost = $17.89 x daily route vehicle hours + $ 0.69 x daily route vehicle miles + $15.58 x vehicles used on route Daily Local Route Cost = $17.89 x daily route vehicle hours + $ 0.69 x daily route vehicle miles + $10.67 x vehicles used on route Computed Route Costs: Daily Daily Daily Vehicle Vehicle Peak Daily Route Type Hours Miles Vehicles Cost 1A Peak 16.8 265 4 $546 1C Peak 10.8 192 2 $357 1B Peak 8.6 157 2 $293 24 Local 48.4 920 11 $1,618 6 Local 22.7 232 4 $609 5 Local 28.2 290 5 $758 113 NOTICE This document is disseminated under the sponsorship of the U.S. Department of Transportation in the interest of information exchange. The United States Government assumes no liability for its contents or use thereof. The United States Government does not endorse manufacturers or products. Trade names appear in the document only because they are essential to the content of the report. This report is being distributed through the U.S. Department of Transportation's Technology Sharing Program. DOT-T-89-13 DOT-T-89-13 TECHNOLOGY SHARING A Program of the U.S. Department of Transportation
