Secrets of success: assessing the large increases in transit ridership achieved by Houston and San Diego transit providers

Transportation Research Part A 33 (1999) 601±624

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Secrets of success: assessing the large increases in transit ridership achieved by Houston and San Diego transit providers John F. Kain *, Zvi Liu University of Texas at Dallas, Mail Station GC21, P.O. Box 830688, Richardson, TX 75083-0688, USA

Abstract This paper summarizes and updates the ®ndings from an earlier study by the same authors of transit systems in Houston (all bus) and San Diego (bus and light rail). Both systems achieved unusually large increases in transit ridership during a period in which most transit systems in other metropolitan areas were experiencing large losses. Based on ridership models estimated using cross section and time series data, the paper quanti®es the relative contributions of policy variables and factors beyond the control of transit operators on ridership growth. It is found that large ridership increases in both areas are caused principally by large service increases and fare reductions, as well as metropolitan employment and population growth. In addition, the paper provides careful estimates of total and operating costs per passenger boarding and per passenger mile for Houston's bus operator and San Diego's bus and light rail operators. These estimates suggest that the bus systems are more cost-e€ective than the light rail system on the basis of total costs. Finally, the paper carries out a series of policy simulations to analyze the e€ects of transit funding levels and metropolitan development patterns on transit ridership and farebox recovery ratio. Ó 1999 Elsevier Science Ltd. All rights reserved. Keywords: Transit ridership; Transitway; Light rail transit; Transit capital; Operating cost; Farebox recovery ratio

1. Introduction This paper summarizes and updates ®ndings from our Federal Transit Administration (FTA) funded study of two transit systems, Houston's Metropolitan Transit Authority of Harris County (METRO) and San Diego's Metropolitan Transit System (MTS) (Kain and Liu, 1995). Since the ®nal year of data included in that study was either 1993 (MTS) or 1992 (METRO), we have made

*

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0965-8564/99/$ ± see front matter Ó 1999 Elsevier Science Ltd. All rights reserved. PII: S 0 9 6 5 - 8 5 6 4 ( 9 9 ) 0 0 0 0 9 - 9

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a reasonable e€ort to update our earlier analyses. Where this was not possible, as with our detailed annual estimates of real transit capital for the two systems,, we have provided less detailed analyses that nonetheless allow us to assess these changes. METRO and MTS achieved unusually large increases in transit ridership during a period in which most transit systems were experiencing large losses. Between FY80 and FY90, Houston's METRO increased its annual passenger boardings by 85% while San Diego's MTS operators increased theirs by 49%. METRO's percentage increase in passenger boardings during the decade was third largest among the nation's 75 largest transit operators; MTS's increase ranked ninth. To provide context for the Houston and San Diego experience, we used cross-section data for the nation's 75 largest transit operators and time series data for METRO and MTS operators to quantify the relative contributions of policy variables and factors beyond the control of transit operators on ridership growth. With these analyses as background, we assessed the e€ect of di€erences in urban development patterns and patterns of employment and population growth in Houston and San Diego on transit demand. In addition, we examined the history of area transit service provision in each area, paying particular attention to institutional arrangements, transit ®nancing and the technological choices of policy makers and transit managers. We also prepared careful estimates of total and operating costs per passenger boarding, per passenger mile, per service mile and per standard vehicle mile for METRO and the major MTS bus and rail operators. Finally, we carried out a series of policy simulations that considered the e€ects of transit funding levels and metropolitan development patterns on transit ridership and farebox recovery ratio. This paper emphasizes the cost estimates and policy simulations. We have been able to update the operating cost comparisons included in our FTA report, but not the total cost ones. Nonetheless, we present more limited data on recent capital spending by operators in the two areas and assess the implications of these expenditures for system coste€ectiveness. 2. Land use trends and transit ridership Houston and San Diego have been two of the nation's most rapidly growing metropolitan areas since the end of World War II. This fact should be kept ®rmly in mind in assessing the performance of their transit operators, and particularly in comparing the increases in transit ridership they achieved with the losses experienced by transit operators in slow growing areas such as Cleveland and Bu€alo. Houston metropolitan area employment grew by 4.4% per year between 1960 and 1990 and San Diego's employment by 3.8% per year over the same period (Kain and Liu, 1995, Table 2-1). While overall metropolitan employment growth has a positive e€ect on transit ridership, the location of these jobs is even more important (Kain and Liu, 1995, pp. 3±12). Central city workers are more likely to use transit for their journeys to work than suburban workers and CBD workers are much more likely to use transit for their journeys to work than either central city workers or suburban workers. Census data for 1980 show that transit mode splits for CBD workers in Houston and San Diego are four to ®ve times as large as those for workers employed in the rest of the central city and six to 15 times those of suburban workers (Kain and Liu, 1995, Table 2-2).

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The fact that Houston's CBD had 3.5 times as many jobs as San Diego's CBD in 1980 is thus of considerable importance.1 While San Diego's small CBD tends to attract few transit riders, this disadvantage is o€set by the area's favorable topography. In contrast to Houston, which is built on a ¯at, unbroken expanse, San Diego is hemmed in by the Paci®c Ocean on one side and by a steep mountain range on the other. As a result, a large fraction of San Diego's jobs and households are concentrated in a narrow North±South corridor along the bay and a large fraction of the remainder in a narrow East±West valley. Not surprisingly, the ®rst and second lines of San Diego's light rail system were built in these corridors. The most comprehensive indicators of land use di€erences between the METRO and MTS service areas are their extent and gross population densities. The MTS service area is limited to the relatively dense, contiguous areas surrounding the City of San Diego, essentially those areas that were designated for rail service in the 1975 service plan. The gross population density of this area, which was 3239 persons per square mile in 1990, was 1.7 times as great as the gross population density of METRO's service area in the same year. The importance of gross population density as a determinant of transit ridership has been demonstrated in several studies including Kain and Liu (1995, 1998) and Liu (1993). 3. The systems METRO is a publicly owned, monopoly provider of transit services in the western two-thirds of Harris County. It replaced a short-lived city-owned operator, HouTran, in August 1978 and at the time the research for our FTA report was completed it served approximately 2.4 million persons living at very low densities. In contrast, MTS, a confederation of several ®xed-route providers operating under the general oversight of the Metropolitan Transit Development Board (MTDB) serves the southwestern part of San Diego County. MTDB, which was created by the California State legislature in 1975 to plan, build, and operate San Diego's light rail transit (LRT) system, subsequently organized MTS. The MTDB/MTS 570 square mile service area contains about 1.8 million people. At the time we completed our FTA study, San Diego Trolley (SDTI) and San Diego Transit Corporation (SDTC), the region's largest bus operator, accounted for 90% of ®xed-route boardings in the MTS service area. The remaining services were provided by private ®rms operating under contract to the communities of Chula Vista and National City, to the County of San Diego, and to the MTDB. While these services accounted for only 10% of total boardings currently, it would be a mistake to regard them as unimportant. Their existence and the fact that SDTC was until recently an independent operator clearly contributed to MTS's success.

1

According to the Census, employment in San Diego's CBD was only 21,000 in 1970 and 29,000 in 1980. In contrast, employment in Houston's CBD was 92,278 in 1970 and 102,240 in 1980. These data are not available from the 1990 Census.

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METRO and MTDB have been technological leaders. MTDB, using largely state funds, implemented the ®rst LRT system built in the United States since World War II. It set a high standard in its implementation of LRT; one that has not been equaled by any of the new light rail systems that have been completed since MTDB completed its South Line. At the time the research for our FTA report was completed, the San Diego Trolley operated two lines totaling 34 miles in length. By April 1989 San Diego's operational LRT mileage had grown to 46 miles and in February 1995 the North County Transit District (NCTD), with assistance from MTDB and the San Diego Association of Governments (SANDAG), began operating a 43 mile commuter rail line between Oceanside and downtown San Diego. By 2010, if funding permits, MTDB expects to have further expanded its LRT network to a total of 70 miles (MTDB, 1998). METRO made di€erent technological choices. At the time the research for our FTA report was completed, it had already implemented ®ve radial transitways (high occupancy vehicle lanes) totaling 63.6 miles. By September 1997, its transitways had grown to 71.1 miles. In addition, an additional 42.6 miles were under proposal, development, design or construction and it is anticipated that the ®nal system will total 113.7 miles (METRO, 1998, p. 4). Transitways, as the term is used in Houston, are physically segregated, usually reversible, one-lane roadways, built mostly in the median of radial expressways. These high-speed bus and carpool facilities are connected to park-and-ride lots by exclusive ramps that permit express buses using them to avoid congestion entirely and operate non-stop at high speed to the central area or to other parts of the region. Depending on the extent of transit demand, Houston's transitways may be used by buses, vanpools, and carpools with either three or more or two or more persons (Kain et al., 1992). Houston's evolving express bus and transitway system is among the most ambitious and innovative e€orts to provide urban area residents with high-performance, high-speed, and coste€ective commuter services implemented in any North American city since World War II. Using the revenues from its dedicated one-percent sales tax, highway funds, and federal capital subsidies, METRO built its transitways without borrowing, while at the same time aggressively expanding local bus services for central city residents and maintaining low fares. 4. Ridership trends As the indexes of annual boardings for Houston, San Diego and the entire United States, shown in Fig. 1 indicate, transit ridership trends in the two metropolitan areas were similar to those for the US as a whole before METRO and MTDB were created. With the exception of a blip in the Houston index during FY60±FY68, transit use declined steadily in all three cases. These similarities, of course, re¯ect common in¯uences, i.e. the suburbanization of jobs and residences, rising incomes and car ownership that have adversely a€ected transit ridership in all US metropolitan areas since the end of World War II. Between FY72 and FY92 transit ridership grew in Houston, San Diego, and the entire US. These increases were made possible by huge increases in transit subsidies by federal, state, and local governments. Since FY92, METRO and MTS have continued to outperform the nation as a whole. During this period boardings for the entire US declined by nearly 4%, while MTS boardings grew by 9.2% and METRO boardings grew by 3.9%.

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Fig. 1. Index of annual transit boardings for Houston, San Diego, and the entire US, 1972 ˆ 100. Source: The US data are from statistical abstract of the US, various years. Houston and San Diego data are provided by METRO and MTDB, respectively.

A more careful comparison of the METRO and MTS indexes reveals some striking di€erences. Between its creation in FY79 and FY91, METRO ridership (annual boardings) grew steadily, totaling 106% between FY79 and FY91, from 39 to 86 million boardings per year. The history of ridership gains for transit operators in what now comprises the MTS service area is more complex. Between FY71 and FY76, transit operators serving this area reversed a long period of decline and increased transit ridership by 143% (from 15.6 to 37.8 million boardings per year). These large increases were obtained before MTDB was created and resulted from decreases in real fares and large increases in vehicle miles of service. These successful e€orts to increase transit ridership were funded by large increases in subsidies to area operators from the State of California's Transportation Development Act (TDA) moneys and federal operating subsidies. In the period immediately following its creation in 1975, MTDB concentrated on planning and building the ®rst line of San Diego's LRT system and within two years, annual boardings in its service area once again declined. This downward trend continued until FY83. Between FY83 and FY91, MTS operators doubled their boardings (from 30 to 60 million boardings per year). This spurt coincides roughly with the introduction of LRT service, ®rst on the South Line in FY81 and then on the East Line in FY89, and with the implementation of new, TDA funded services by Chula Vista, National City, and San Diego County. If the entire period of MTDB oversight is considered (FY76 to FY97) the growth in ®xed-route boardings was 68%. Some of this growth in boardings is undoubtedly due to increased transfer rates that resulted from changes in bus operations that were designed to feed the LRT. Linked trip data are not available for the MTS system, and thus it is not possible to accurately assess the extent of this overstatement. In Atlanta, where linked trip data are collected, the introduction of a new rail system in 1980 increased the system-wide transfer rate from about one-third to over 100% (Kain, 1997). As a result, between 1981 and 1993, annual boardings grew by 39% while linked trips grew by only 3% (Kain, 1997, p. 29). In our FTA study we estimated both cross-section and time series ridership models (Kain and Liu, 1995). The cross-section models, which are based on data for the nation's 75 largest transit operators, quantify the independent contributions of policy and exogenous variables in determining ridership gains and losses. Policy variables, principally real fares and vehicle miles of

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service, are subject to the direct control of transit management, regional policy makers, and voters. In contrast, changes in metropolitan area employment and central city population, which, as these analyses demonstrate strongly, a€ect transit ridership, are largely beyond the control of individual operators and local policymakers. Kain and Liu's (Kain and Liu, 1995, 1998) econometric analyses of the determinants of transit ridership (passenger boardings) clearly show that the fact that METRO and MTS are both located in rapidly growing metropolitan areas contributed to their success. As the percentage change data in Table 1 indicate, Houston metropolitan area employment grew by 42% between 1980 and 1990 while San Diego metropolitan area employment grew by 60% during the same period. The rate of growth of San Diego's central city population, which was 29% during 1980±1990, greatly exceeded Houston's much more modest central city population growth, which was less than 1% for the same period. Combining these growth rates with the elasticities shown in Table 1, we estimated that 1980-90 employment and population growth would have been expected to increase METRO's transit ridership by 10% and MTS's ridership by a third. The data in Table 1 also quantify the e€ects of real fares and service levels on transit ridership in the METRO and MTS service areas. The service and fare elasticities, which we obtained from our cross-section regressions, indicate that METRO's 80% increase in service and 14% decrease in real fares between 1980 and 1990 would have been expected to increase system ridership by 62%. These predicted increases in combination with the predicated 10% increase from employment and population growth account for 85% of the 85% actual increase in boardings during 1980±1990.

Table 1 Factors contributing to ridership changes, 1980±1990 Year 1980

1990

Percent change (%)

Elasticity

Contribution to ridership (%)

Houston SMSA employment (0 000) Central city population (0 000) Bus and rail miles (0 000) Real fares (US$) All contributing factors Actual ridership change

993 1,618 17,325 0.53 ÿ ÿ

1,409 1,631 31,228 0.46 ÿ ÿ

42 1 80 ÿ14 ÿ ÿ

0.25 0.61 0.71 ÿ0.32 ÿ ÿ

10 0 57 5 72 85

San Diego SMSA employment (0 000) Central city population (0 000) Bus and rail miles (0 000) Real fares (US$) All contributing factors Actual ridership change

745 942 11,885 0.56 ÿ ÿ

1,188 1,219 14,961 0.64 ÿ ÿ

60 29 26 14 ÿ ÿ

0.25 0.61 0.71 ÿ0.32 ÿ ÿ

15 18 18 ÿ4 47 49

Source: Both employment and population data are from the US Census of Population, 1980 and 1990. Data of bus and rail miles and fares are provided by METRO and MTDB.

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The analyses summarized in Table 1 indicate that the increases in ridership achieved by MTS operators are due less to their policies and more to rapid regional growth than is true of METRO. In contrast to METRO's decision to reduce real fares by 14% between 1980 and 1990, MTS increased them by 14%. Applying the fare elasticities in Table 1 indicates that these fare increases would have reduced boardings by 4% during the decade. Similarly, MTS' much more modest (26% vs. 80% for METRO) increase in service miles (bus plus rail) for ®xed route operations would have been expected to increase transit ridership by 18%. When the predicted ridership loses from a real fare increase are added to the predicted gains from service expansions, these policy changes accounted for an estimated 26% of the actual 49% increase in transit ridership over the decade. The two land use variables and the two policy variables in combination would have been expected to produce a 47% increase in transit ridership. These same two policy variables, it should be recalled, accounted for 85% of METRO's much larger increase in boardings over the same period. It is dicult to avoid the impression that METRO spent the larger part of its subsidy dollars to increase transit ridership, while MTDB spent the larger part of its to built LRT.2 5. Operating costs of Houston and San Diego transit providers Our assessment of the cost-e€ectiveness of the Houston and San Diego systems began with estimates of operating costs per boarding and per passenger mile for METRO, for San Diego Transit, for the San Diego Trolley and for all MTS ®xed route operators. This analysis posed few problems because operators in both metropolitan areas maintain detailed operating cost data and because uniform reporting of these data is insured by FTA's Section 15 Reports. For these same reasons, it was relatively easy for us to obtain the data we needed to update the operating cost analyses included in our FTA report. The resulting estimates of SDTC's real operating costs per boarding for each year from FY68 to FY97 are shown in Fig. 2. As METRO's and the San Diego Trolley's ®rst years of operation were FY79 and FY82, respectively, there are fewer years of data for them. As the data in Fig. 2 show, METRO had the highest operating costs per boarding. METRO's real operating costs, however, declined steadily between FY82 and FY91, but increased slightly between FY92 and FY97; and by FY97 its operating cost per boarding at US$2.13 (in 1993 dollars) was 42% greater than San Diego Transit's for the same year. Operating costs per boarding for the San Diego Trolley were substantially less than either METRO's or San Diego Transit's. In FY97, the most recent year in which these data were available for all three operators, operating costs per boarding for the San Diego Trolley at US$1.14 were only 54% as large as METRO's and only 76% as large as San Diego Transit's. The trolley's much lower operating costs per boarding

2 MTDB ocials might reasonably respond by pointing out that the state and federal funds they used to build LRT were earmarked to rail construction and could not be used to reduce fares or increase bus operations. Whatever the merits of this argument may be, the e€ect was to produce a smaller increase in transit ridership than if the money had been spent in another way.

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Fig. 2. Real operating cost per passenger boarding by area and operator (in constant 1993 Dollars), FY68±FY97. Source: Calculated from data provided by METRO and MTDB.

are due in part to lower operating costs per service or seat mile, but the higher levels of ridership per service mile are even more important. In FY92, for example, San Diego Trolley had 4.15 boardings per revenue mile, as contrasted to 3.02 boardings per revenue mile for all of San Diego Transit's ®xed route services combined. The trolley's much higher ridership per revenue mile is no accident. While the availability of bargain-priced rail right-of-ways was a critical factor in MTDB's selection of trolley alignments, system planners chose the corridor with the greatest existing and projected transit demand for the trolley's ®rst line. Implementation of San Diego Trolley's South Line, moreover, replaced two of San Diego Transit's most productive bus routes. As is clear from Fig. 2, opening of the South Line had a major impact on bus ridership. Real operating costs per boarding for San Diego Transit increased by 39% between FY81, the year prior to the beginning of trolley operation, and FY83, the second year of trolley operation. A more detailed discussion of these issues is presented in Gomez-Ibanez (1985). Since FY88 San Diego Transit, in spite of further LRT expansion, has been able to either reduce real operating costs per boarding or keep them relatively level. With the continued expansion of LRT service, MTDB has attempted, with some success, to persuade MTS bus operators to restructure their services to feed the trolley. The average length of Trolley trips exceeds that of METRO trips in all years.3 Similarly, the average length of METRO trips exceeded those of SDTC trips in all but one year, 1983, when they were both 4.9 miles. As a result, data on operating cost per transit passenger mile may provide a fairer comparison of the trends in Houston and San Diego real operating costs. As the data shown in Fig. 3 reveal, METRO's real operating costs per passenger mile declined much more rapidly between FY84 and FY92 than its real operating cost per boarding. METRO's costs per passenger 3

Average trip length is obtained by dividing passenger miles by the number of boardings. Average trip length of each individual operator remains quite stable, except San Diego Trolley in the years 1994 and 1995, where the estimated trip length dropped sharply, probably due to a di€erent method of estimating passenger miles. We use the average of trip length for 1993 and 1996 to re-estimate the passenger miles for 1994 and 1995. Moreover, MTS passenger miles data for 1994±1997 are not available. Because average trip length of MTS remains around 5.5 miles from 1987 to 1992, we used 5.5 times the number of boardings to obtain the passenger miles for 1994±1997.

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Fig. 3. Real operating cost per passenger mile by area and operator (in constant 1993 Dollars), FY80. Source: Calculated from data provided by METRO and MTDB.

mile also became very close to those of San Diego Transit in every year between FY87 and FY94. During FY95±FY97, San Diego Transit's costs per passenger mile have declined while METRO's have somewhat increased. San Diego Trolley's operating costs per passenger mile were signi®cantly lower than either METRO's or San Diego Transit in every year since the Trolley began operation. The indexes of real operating costs per boarding and per passenger mile shown in Figs. 2 and 3 are strongly a€ected by demand and are in every sense policy outcomes. As the ridership models in Kain and Liu (1995, Ch. 3 and Ch. 6) demonstrate, either METRO or San Diego Transit could have reduced operating costs per boarding or per passenger mile by eliminating low productivity routes or by reducing vehicle miles of service. Similarly, they could have increased transit ridership at given levels of service by charging lower fares. 6. Capital costs for Houston and San Diego transit providers Operating costs are only part of the cost-e€ectiveness story. A complete assessment of bene®ts and costs must include the full costs of providing transit services, which in addition to operating costs include depreciation on transit capital and the opportunity, or ownership, costs of the transit capital owned by each system. Depreciation cost data are available from published ®nancial statements, although few systems maintain reserves for depreciation or pay much attention to it. The reason appears to be that transit managers assume that federal, state, or local governments, or taxpayers will provide the funds required to replace transit vehicles and for major rehabilitation of their systems. Preparing estimates of real transit capital for San Diego and Houston was surprisingly dicult. Because most signi®cant capital assets are purchased with federal or state grants, or other dedicated revenue sources, publicly owned transit providers tend to view transit capital as free. None that we know of makes an e€ort to calculate the economic value or cost of their transit capital. FTA's Section 15 report, moreover, includes no information on the real quantities of transit capital owned by transit operators.

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Fig. 4. Alternative estimates of real capital, METRO, FY80±FY90 (in constant 1993 Dollars). Source: calculated from data provided by METRO.

Our estimates of real transit capital for transit operators in Houston and San Diego were based principally on published ®nancial statements. Data from these reports were used to estimate both annual additions to capital and annual capital consumption (depreciation) for each system. In the case of Houston, we also used expenditure data to prepare an alternate estimate of capital stock additions (Kain and Liu, 1995, Ch. 7).4 For San Diego, we prepared separate estimates of the real value of transit capital for San Diego Transit, for the San Diego Trolley and for all MTS ®xed route operator combined. In spite of several speci®c di€erences in data and procedures used to estimate the annual stock of transit capital for the four entities included in our analysis, the overall approach was the same.5 All of the capital stock estimates began with either the book value of transit capital for each system in each year or annual outlays for transit capital. For the estimates based on balance sheet data, the ®rst step was to di€erence these estimates, thereby providing an estimate of annual current dollar additions to each system's transit capital. Retirements were then added to each year's capital stock estimates, and the resulting current dollar ®gures were converted to constant 1993 dollars using the GNP de¯ator. The real value of transit capital for each year was then obtained by cumulating these annual constant dollar additions net of the depreciation of the previous year's stock of transit capital. It was simply not possible to completely update the annual estimates of real transit capital that were central to the total cost analyses included in our FTA report. We were able to obtain some data on capital outlays and system expansion for both METRO and the San Diego Trolley and these will be used at a later point in the paper for some back-of-the-envelop calculations of their impact on the cost-e€ectiveness of these systems. In the meantime, Fig. 4 depicts three estimates 4

The alternative estimate of Houston's transit capital was based on detailed annual capital expenditure data (Kain and Liu, 1995, Table 7-1). Analysis of these data revealed that a signi®cant fraction of METRO's ``capital outlays'' were for items other than transit capital. In 1992, for example, US$107 million of METRO's total capital expenditures of US$178 million were devoted to general mobility and trac management improvements. These expenditures were not included in our alternative estimate of Houston's transit capital. 5 A more detailed description of the data and procedures used in estimating the annual value of real transit capital for each operator is available from Kain and Liu (1995, Ch. 7).

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611

Fig. 5. METRO's real transit capital assuming alternative allocations of transitway capital between bus passengers and other users, FY80-FY92 (in constant 1993 Dollars). Source: calculated from data provided by MTDB.

of METRO's transit capital in constant 1993 dollars for the period FY80±FY92. The high estimate is based on METRO's capital expenditure data. Both the middle and the bottom one, like those for all San Diego entities, are derived from balance sheet data. The middle estimate is clearly the best since it makes an e€ort to account for asset retirements. In spite of our best e€orts, the book value of retirements may be understated in some years with the result that the value of transit capital may be overstated. We doubt if this is a very serious problem, however. Delays in booking work in progress for multi-year construction programs also cause something of an understatement of real transit capital in some years. This problem is greater for San Diego's LRT lines than for METRO's transitway projects.6 It should be clear from the above discussion that none of the three METRO estimates of real transit capital shown in Fig. 4 is perfect. At the same time, we are con®dent that the one based on ®xed asset data, including retired assets, which is used in all subsequent analyses, is the most accurate. It has the further virtue of being closest in both concept and estimation methods to our estimates of real transit capital for San Diego's transit providers. Houston's transitways are multi-purpose facilities that are used by buses, vanpools, and carpools. This raises the question of how the capital costs of these facilities should be allocated among these various users. Fig. 5 shows three estimates of METRO's real transit capital that assume di€erent allocations of transitway capital costs. All three are based on the middle estimate in Fig. 4. The high estimate in Fig. 5 allocates all transitway capital costs to METRO buses, the middle one charges METRO a share of annual transitway costs that is equal to the bus share of total transitway users and the low one charges METRO a share of transitway capital costs that is equal to the bus share of total passenger car equivalents (PCEs). In making these calculations, each bus is considered equivalent to 2.5 passenger cars in terms of its use of transitway capacity.

6 The highest of the three METRO estimates, which it should be recalled is obtained from METRO's capital improvement program data, clearly overstates the quantity of transit capital that is in service in each year. This is because it includes unknown amounts of expenditures in each year for uncompleted capital projects that are not yet in service. Its principal value is as a check on our preferred estimate which, like the San Diego estimates, is based on ®xed asset data.

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While the low estimate is arguably the most appropriate of the three, we nonetheless use the middle one for the total cost comparisons. 7. The real value of transit capital for San Diego transit operators Annual estimates of real transit capital are shown in Fig. 6 for San Diego Transit between FY68 and FY92 and for the San Diego Trolley and for all MTS operators combined between FY81 and FY93. As discussed above, the procedures used to prepare annual estimates of real transit capital for San Diego Transit, for San Diego Trolley, and for all MTS ®xed route operators are very close to those used for our best METRO estimate. The estimates of real transit capital for San Diego Transit are based entirely on ®xed assets data obtained from San Diego Transit's annual ®nancial statements. To the extent possible, we added retirements to each year's book value of ®xed assets before we di€erenced them and converted them to constant 1993 dollars. The retirement data, however, were less complete than those used in preparing the METRO estimates. Only total retirements were available, and even this ®gure was unavailable for some years. As a result, it is likely that our annual estimates of the amounts of real transit capital that is owned by San Diego Transit is too low, and that this underestimate is larger than for METRO. We would be surprised if this underestimate exceeds 10%, however. To develop the estimates of real transit capital for the San Diego Trolley we began with a ®le containing each of the ®xed assets acquired by MTDB for either its own use or for the trolley in each year since it began operations. The biggest problem with these data is that assets are ``booked'', i.e., included in the ®xed assets accounts, at the time they are purchased or, in the case of complex facilities that require several years to build, at the time they are contributed by MTDB to the trolley. This convention understates their real value. For capital projects that were built before the trolley began operations, we used expenditure and work-in-progress data to annualize these project expenditures and used price indexes that were keyed to the time path of these expenditures. We also included an annual charge for the opportunity cost of these capital expenditures, which we accumulated and added to the real value of the capital at the time the facilities

Fig. 6. Real transit capital owned by MTDB area transit operators, FY68±FY93 (in constant 1993 Dollars). Source: calculated from data provided by MTDB.

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613

Fig. 7. Real total cost per passenger boarding by area and operator (in constant 1993 Dollars), FY68±FY93.

were put into service. In the case of multi-year projects that were built after the trolley began operations, for example, the East (El Cajon) Line, we lacked sucient information to make these corrections. As a result, the real value of these assets is underestimated. A similar, though smaller, problem exists for METRO's transitway capital. 8. Total cost per passenger boarding Fig. 7 presents our estimates of total cost per boarding for San Diego Trolley, for San Diego Transit (Bus), for all MTDB service area operators (including all ®xed route and demand responsive operators in the MTDB service area), and for Houston METRO. Total costs are the sum of operating costs, depreciation, and annual capital ownership costs. Following United States Government Oce of Management and Budget guidelines, we use 7% as the annual opportunity or ownership cost of real transit capital.7 Because we were unable to prepare estimates of the real stocks of transit capital for each system for the period 1993±1997, the estimates of total cost per passenger boarding and total cost per passenger mile, presented in the next section, end in 1992 (METRO) or 1993 (MTS). Using total cost per boarding to compare the four systems produces di€erent rankings than when operating costs per boarding are used. This is an important ®nding, as both policymakers and publicly owned transit operators tend to focus on operating costs in assessing system performance, to the virtual exclusion of any consideration of capital costs or of the total cost of providing transit services. When operating costs per boarding are used as the index of system performance (Fig. 2), the San Diego Trolley far outperforms any of the bus operators. In contrast, San Diego Transit has the lowest total cost per boarding by a signi®cant amount. As Fig. 7 also indicates, real (constant 1993 dollars) total costs per boarding for the San Diego Trolley declined rapidly from a high of US$6.74 per boarding in FY83 to a low of US$3.13 per 7

Oce of Management and Budget (1992), p. 53523) in its circular on bene®t cost analyses of federal programs recommends using ``a real discount rate of 7%''. . .``in assessing government projects''. It adds that ``this rate approximates the marginal pre-tax rate of return on an average investment in the private sector in recent years''.

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boarding in FY90. After FY90, however, this ®gure increased by 18% in just three years reaching US$3.68 per boarding in FY93. These increases in trolley total costs per boarding are presumably related to the introduction of additional East Line service. As we discuss in greater detail in the next section, these increases in total costs per trip have escalated with the large extensions in the region's LRT system that have occurred since we completed the research for our FTA study. Using total cost per boarding as the criterion, San Diego Transit is clearly the low cost provider. After a fairly sharp (38%) increase immediately after service on the South Line began, from US$1.74 in FY81 to US$2.40 in FY83, San Diego Transit's real cost per boarding remained constant or declined slightly. In FY93, these costs were US$1.99 per boarding. Fig. 7 also presents total costs per boarding for the entire MTS system, which is clearly the more appropriate comparison. As these data demonstrate, the total per trip cost of providing transit services in the region increased by 42% when the trolley was introduced, from US$1.66 in FY81 to US$2.35 in FY83. Thereafter the real total cost per boarding declined annually until FY90 when it reached US$1.89. Thereafter, it increased, reaching US$1.99 in FY93. It is well to keep in mind when assessing these trends that the use of boardings, rather than linked trips, may paint a somewhat more favorable picture than is justi®ed. METRO's real total costs per boarding for the entire period FY82±FY93 were higher than San Diego Transit's but lower than the San Diego Trolley's. As noted above, a more appropriate comparison is between METRO and the combined MTS operations. METRO's cost are higher in every year, but the gap is small. 9. Total costs per passenger mile As we discussed previously, cost per passenger mile is a better measure than cost per boarding for assessing the performance of transit operators. The principal drawbacks are that passenger mile data are less accurate than boardings data and they are available for fewer years. Comparisons of real total costs per passenger mile are shown in Fig. 8. Because trolley trips tend to be longer than bus trips in San Diego, using per passenger mile as the basis of comparison makes the

Fig. 8. Real total cost per passenger mile by area and operator (in constant 1993 Dollars), FY80±FY93.

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trolley look better. The trolley still has the highest cost in all but the last few years, when its real total costs per passenger mile dip just below the same statistic for METRO. Even though bus trips in San Diego are shorter than trolley trips, San Diego Transit still has the lowest real total cost per passenger trip in all of the years for which passenger mile data are available. Trip length data ®rst became available in 1980. Even so, while the pre-trolley period is much shorter, real total costs per passenger mile data of San Diego Transit and all MTS operators, nonetheless, exhibit the same sharp increases in cost that is evident in the per boarding data. In FY80 real total cost per passenger mile for all MTDB area operators was US$0.37; by FY83 it had increased to US$0.57 or by 54.0541%. 10. Impacts of recent capital outlays on cost-e€ectiveness While we were unable to develop annual estimates of the real stock of transit capital for the period 1993±1997, substantial growth occurred in both Houston and San Diego. The San Diego Trolley increased its LRT route miles by 36% since we completed the research for our FTA report. The capital cost of this system expansion was at least US$1.8 billion in nominal dollars (MTDB, 1998). Using conservative procedures, we ®nd this is a 91% increase in the value of the Trolley real transit capital. During 1992±1997, the Trolley also increased revenue miles of service by 9% and its operating cost by 8%. During the same period, Trolley boardings grew by 7%.8 Because the system expansion undoubtedly increased LRT-LRT and bus-LRT transfers, the growth in linked trips was presumably somewhat smaller. The increase in the total cost per trip was clearly very large. During 1992±1997, METRO also added to its transitways. Its expansion, however, was both smaller and cheaper. It increased its transitway route miles by only 11% as contrasted to the Trolley's 36% expansion. In assessing this smaller system expansion, it should be recalled that only a fraction of the transitway costs are chargeable to transit as a large fraction of transitway capacity is used by vanpools and carpools. During the same period, METRO increased its revenue miles of service by 19% and operating costs by 13%. Boardings increased by 4%. We have not obtained any capital cost data for 1992±1997 for SDTC or the other MTS ®xed route bus operators. Nonetheless, we would be very surprised to learn that any of them made capital investments that a€ected their total cost per trip by more than a cent or two. During 1992± 1997, STDC boardings declined by 3%, its passenger miles grew by 4%, its bus service miles declined by 17% and its operating costs fell by 15%. These changes presumably re¯ect in large part the impacts of Trolley expansion and the replacement of many of its longer routes by feeders.

8

Between 1997 and 1998, MTDB reports that trolley ridership (boardings) and passenger miles increased by an unprecedented 26%. When asked to explain this rapid growth in trolley ridership, MTDB ocials stated that ``the big increase in trolley boardings and passenger miles was due to the opening of the trolley from Old Town to Mission Valley as well as big events such the Super Bowl, Charger football games and San Diego State football games''. While we have no basis for doubting this increase in trolley ridership, it, nonetheless, seems awfully large. We will be more comfortable when a couple more years of data become available.

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Table 2 MTDB boardings and revenue miles (Bus and Rail) in 1992 and 1997 by operator Boardings (000)

Revenue miles (000)

Percent change

1992

1997

1992

1997

Boardings (%)

Revenue miles (%)

Fixed route operators MTDB contract services CTS-east county suburban CTS-express CTS-poway suburban Chula vista transit National city transit San Diego transit San Diego trolley Fixed route total

3,489 2,094 301 247 2,026 1,340 33,319 17,163 59,979

4,594 3,411 270 275 2,646 1,624 32,289 18,287 63,396

1,474 1,721 428 394 1,084 405 12,221 4,507 22,234

2,703 2,215 320 327 1,194 402 10,141 5,088 22,390

32 63 ÿ10 11 31 21 ÿ3 7 6

83 29 ÿ25 ÿ17 10 ÿ1 ÿ17 13 1

Fixed route bus With SDTC Without SDTC

42,816 9,497

45.109 12.820

17,727 5,506

17,302 7,161

5 35

ÿ2 30

Dial-A-Ride General public Senior and disabled Total Dial-A-Ride MTDB Total-all services

689 484 1,173 61,152

474 213 687 64,083

1,660 1,915 3,575 25,809

1,510 1,096 2,606 24,996

ÿ31 ÿ56 ÿ41 5

ÿ9 ÿ43 ÿ27 ÿ3

Operator

Source: Provided by MTDB.

Table 2 provides boardings and revenue mile (bus and rail) data for all MTDB operators during 1992±1997 as well as percentage changes for the 1992±97 period. These data reveal that San Diego Trolley's 6.5% growth in boardings and 12.9% growth in passenger miles during 1992±1997 exceeded these same ®gures for all MTDB ®xed route providers combined. Examination of the data in the second panel, however, reveals that the smaller increase for MTDB bus operators is entirely due to SDTC's 3.1% decline in boardings and 17% decline in revenue miles of service. The six smaller bus operators experienced a 35% increase in boardings, which was accompanied by 30.1% increase in revenue miles. These increases far outstripped those for the Trolley. The ®nal panel in Table 2 provides these same data for dial-a-ride operators. These data indicate that both general public and senior and disabled citizens dial-a-ride services experienced large percentage declines during this period. The combined decrease in boardings for general public and senior and disabled services during this period was 41.1%. This ridership decline was associated with a 27.1% decrease in revenue miles of service. While these services comprise a small part of total transit ridership, the 1992±1997 decline in Dial-A-Ride boardings, which was 486,000 trips, was about 40% of the growth in Trolley boardings over the same period, which was 1.12 million boardings. This fact in combination with the large decrease in Dial-A-Ride service miles makes us wonder whether some of the subsidy dollars spent to expand the LRT might better have been spent in providing more Dial-A-Ride service.

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11. Comparing performance and levels of e€ort While San Diego is a low-density sunbelt city, its development is much more compact and dense than Houston's. As we noted previously, the gross population density of METRO's service area was only 58.2% of the gross population density of the MTS service area. Houston's much higher CBD employment levels are something of an o€set to its lower overall densities, but it is reasonably clear that they are insucient to compensate for the other unfavorable aspects of Houston's urban development in terms of transit use. San Diego's development pattern is simply much more conducive to ecient transit operations than Houston's dispersed and sprawling pattern of urban development. We are persuaded that, in addition to the di€erences in urban development patterns between San Diego and Houston, San Diego's higher productivity is due, to a signi®cant extent, to conservative operating policies and less external funding for operating subsidies. San Diego has simply been less ambitious than Houston and many other metropolitan areas in terms of subsidy levels and the levels of transit service it has chosen to provide.

Table 3 Selected transit ®xed route service operating and ®nancial statistics for San Diego MTS/MTDB and Houston METRO, 1998 Item

San Diego MTS/MTDB

Houston METRO

Ratio (Houston over San Diego)

Service area population (0 000) Service area size (square miles) Service area density (persons/sq.mi.)

1,846 570 3,239

2,403 1,275 1,885

1.30 2.24 0.58

Total passenger boardings (0 000) Total passenger miles (0 000) Passenger miles per boarding

59,012 320,000 5.4

85,035 469,187 5.5

1.44 1.47 1.02

Revenue miles (0 000) Revenue miles per person Revenue miles per square mile Boardings per revenue mile

21,121 11 37.1 2.8

39,772 17 31.2 2.1

1.88 1.55 0.84 0.75

Total fare revenues (0 000) Average fare per boarding

US$43,337 US$0.73

US$45,676 US$0.54

1.05 0.74

Total operating cost (0 000) Total annual capital cost (0 000) Operating cost per capita Total cost per capita Operating cost per passenger mile Total cost per passenger mile Operating cost per revenue mile

US$81,845 US$50,595 US$44 US$72 US$0.26 US$0.41 US$3.88

US$181,801 US$75,246 US$76 US$107 US$0.39 US$0.55 US$4.57

2.22 1.49 1.73 1.49 1.50 1.34 1.18

Operating subsidy per capita Farebox recovery ratio

US$21 52.9%

US$57 25.1%

2.71 0.47

Source: Kain and Liu (1995).

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Table 3 lists several system performance and e€ort indicators for Houston and San Diego. These data support the notion that Houston's residents have made a much larger commitment to transit than San Diego's residents and that the di€erences in transit productivity and farebox recovery ratios between METRO and the MTS/MTDB consortia are to a signi®cant extent due to these di€erences. METRO's average fare per boarding in FY92 was only 74% as much as the average fare charged by MTS operators in the same year. In addition, METRO's spending for operations, which was US$76 per person in its service area, was 73% larger than the same ®gure for the MTS system. Finally, METRO's farebox recovery ratio at 25%, was less than half of the farebox recovery ratio for all MTS ®xed route operators, which was 53% in the same year. In Tables 4 and 5, we present the results of several simulations that illustrate the transit policy trade-o€s that were available to taxpayers and policymakers of Houston and San Diego and examine their impacts on transit ridership and on farebox recovery ratios. These several simulations, which use fare and service elasticities obtained by Kain and Liu (1995, Ch. 6) for San Diego and Houston, indicate how changes in various transit policy and exogenous variables would have a€ected FY92 transit ridership in these two areas. The policy variables include fares and service miles, while the exogenous variables include population and employment levels, per capita income, car ownership, and gasoline prices. The ®rst two policy simulations assess the impact on METRO's FY92 boardings and farebox recovery ratios of raising METRO's real fare level from its FY92 systemwide average of US$0.54 per boarding to US$0.73, the average fare per boarding charged by MTS ®xed route operators in the same year. The two simulations shown in Table 4 are obtained using parameters from dif-

Table 4 Boardings, farebox recovery ratios, fare revenues, and operating costs for alternative scenarios, Houston METRO, FY92 Performance indicators under alternative scenarios Model I

Model II

Fare elasticity ÿ0.23

Fare elasticity ÿ0.33

Estimate

% of Base

Estimate

% of Base

Total passenger boardings ( 000) 1. Base case: actual METRO fares and services 2. METRO service levels and MTS fares

85,035 77,854

n.a. 91.6

85,035 74,731

n.a. 87.9

Farebox recovery ratio 1. Base case: actual METRO fares and services 2. METRO service levels and MTS fares

25.1% 31.4%

n.a. 125.2

25.1% 30.2%

n.a. 120.2

Total passenger fare revenues (0 000) 1. Base case: actual METRO fares and services 2. METRO service levels and MTS fares

US$45,676 US$57,173

n.a. 125.2

US$45,676 US$54,880

n.a. 120.2

Total operating costs (0 000) 1. Base case: actual METRO fares and services 2. METRO service levels and MTS fares

US$181,801 US$181,801

n.a. 100.0

US$181,801 US$181,801

n.a. 100.0

0

n.a. not applicable. Source: Kain and Liu (1995).

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Table 5 Boardings, farebox recovery ratios, Fare revenues, and Operating costs for alternative scenarios, Houston METRO, FY92 Performance indicators under alternative scenarios

Model I fare elasticity ÿ0.23

Model II fare elasticity ÿ0.33

Estimate

% of Base

Estimate

% of Base

Total passenger boardings ( 000) 1. Base case: actual METRO fares and services 2. METRO service levels and MTS fares

85,035 77,854

n.a. 91.6

85,035 74,731

n.a. 87.9

Farebox recovery ratio 1. Base case: actual METRO fares and services 2. METRO service levels and MTS fares

25.1% 31.4%

n.a. 125.2

25.1% 30.2%

n.a. 120.2

Total passenger fare revenues (0 000) 1. Base case: actual METRO fares and services 2. METRO service levels and MTS fares

US$45,676 US$57,173

n.a. 125.2

US$45,676 US$54,880

n.a. 120.2

Total operating costs (0 000) 1. Base case: actual METRO fares and services 2. METRO service levels and MTS fares

US$181,801 US$181,801

n.a. 100.0

US$181,801 US$181,801

n.a. 100.0

0

n.a. not applicable. Source: Kain and Liu (1995).

ferent ridership models. The simulation labeled Model I is based on the Houston ridership equation and employs a fare elasticity of ÿ0.23. Model II replaces the estimated Houston fare elasticity (coecient) with the industry consensus estimate (the so-called Simpson±Curtin rule-ofthumb), which is ÿ0.33. Model I results reveal that increasing METRO's average fare to the level charged by MTS ®xed route operators in FY92 would increase METRO's farebox recovery ratio from 25.1% to 31.4% and reduce its annual boardings from 85.0 to 77.8 million per year. Using the Model II parameters results in a smaller increase in METRO's farebox recovery ratio and a larger decrease in annual boardings. An alternative way of raising farebox recovery ratio is to reduce services because operating costs per boarding is higher than the average fares. By making selective, rather than across the board service reductions, METRO might be able to reduce the amount of service it provides with little or no adverse impact on system ridership. In this case, the increases in METRO's farebox recovery ratio would be larger than in Table 4. The results of more elaborate analyses for San Diego are shown in Table 6, which gives several performance indicators for a base case (the actual experience of MTS operators in FY92) and for ®ve policy scenarios. These policy simulations are designed to show how the ridership and farebox recovery ratios of MTS operators might be a€ected by an increase in per capita subsidies to Houston levels. For these analyses, we assume MTS operators would use these added funds for more service, to reduce real fares, or some combination of the two. As with the above METRO simulations, we obtain two estimates for each of the ®ve policies. The San Diego simulations, however, use both fare and service elasticities because some of them entail both real fare and service level changes. The two fare elasticity estimates are one obtained by Kain and Liu (1995), Ch. 6) from their MTS ®xed route ridership model (ÿ0.48) and the

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Table 6 Boardings, farebox recovery ratio and other performance indicators by alternative policy scenarios for San Diego MTDB/MTS, FY92 Performance indicators under alternative scenarios

Total passenger boardings (0 000) Base case: actual MTS fares and services 1. MTS service levels and METRO fares 2. Subsidy based on per capita operating di€erence 2.1. MTS fares 2.2. METRO fares 3. Subsidy based on per capita operating di€erence 3.1. MTS fares 3.2. METRO fares Farebox recovery ratio Base case: actual MTS fares and services 1. MTS service levels and METRO fares 2. Subsidy based on per capita operating di€erence 2.1. MTS fares 2.2. METRO fares 3. Subsidy based on per capita operating di€erence 3.1. MTS fares 3.2. METRO fares Total revenue miles (0 000) Base case: actual MTS fares and services 1. MTS service levels and METRO fares 2. Subsidy based on per capita operating di€erence 2.1. MTS fares 2.2. METRO fares 3. Subsidy based on per capita operating di€erence 3.1. MTS fares 3.2. METRO fares Total operating costs (0 000) Base case: actual MTS fares and services 1. MTS service levels and METRO fares 2. Subsidy based on per capita operating di€erence 2.1. MTS fares 2.2. METRO fares 3. Subsidy based on per capita operating di€erence 3.1 MTS fares 3.2 METRO fares

cost

subsidy

cost

subsidy

cost

subsidy

cost

subsidy

Model I

Model II

Fare elasticity ÿ0.48 service elasticity 0.62

Fare elasticity ÿ0.33 service elasticity 0.60

Estimate

% of base

Estimate

% of Base

59,012 66,620

n.a. 112.9

59,012 64,242

n.a. 108.9

90,381 99,841

153.2 169.2

88,739 92,643

150.4 157.0

94,120 104,143

159.5 176.5

92,266 96,520

156.3 163.6

52.9% 43.7%

n.a. 82.6

52.9% 42.2%

n.a. 79.6

40.8% 35.8%

77.0 67.5

40.4% 34.1%

76.2 64.3

39.8% 34.9%

75.2 65.8

39.3% 33.1%

74.3 62.6

21,121 21,121

n.a. 100.0

21,121 21,121

n.a. 100.0

41,986 38,697

198.8 183.2

41,675 37,700

197.3 178.5

44,823 41,422

212.2 196.1

44,472 40,366

210.6 191.1

US$81,845 US$81,845

n.a. 100.0

US$81,845 US$81,845

n.a. 100.0

US$162,696 198.8 US$149,952 183.2

US$161,490 197.3 US$146,086 178.5

US$173,691 212.2 US$160,512 196.1

US$172,329 210.6 US$156,417 191.1

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Table 6 (continued ) Performance indicators under alternative scenarios

Model I

Model II

Fare elasticity ÿ0.48 service elasticity 0.62

Fare elasticity ÿ0.33 service elasticity 0.60

Estimate

% of base

Estimate

% of Base

US$43,337 US$35,784

n.a. 82.6

US$43,337 US$34,507

n.a. 79.6

US$66,373 US$53,629

153.2 123.7

US$65,167 US$49,763

150.4 114.8

US$69,119 US$55,940

159.5 129.1

US$67,757 US$51,845

156.3 119.6

0

Total passenger fare revenues ( 000) Base case: actual MTS fares and services 1. MTS service levels and METRO fares 2. Subsidy based on per capita operating cost di€erence 2.1 MTS fares 2.2 METRO fares 3. Subsidy based on per capita operating subsidy di€erence 3.1 MTS fares 3.2 METRO fares n.a. Not applicable.

industry rule of thumb (ÿ0.33). The service elasticities similarly are from the same MTS ®xed route ridership model and a consensus estimate based on the results from similar ridership models for Atlanta, Portland (Oregon), and Ottawa (Kain, 1989, 1997; Liu, 1993). The ®rst of the policy simulations in Table 6 is similar to the equal fare simulation for METRO described previously. In this case, we assume that MTS operators use an unspeci®ed increase in operating subsidies to reduce their average real fare to the level charged by METRO in FY92. Using a real fare elasticity of ÿ0.48, annual boardings increase from the base case level of 59 million per year to 66.6 million per year. When the more inelastic fare coecient is used, the increase is somewhat smaller. The second panel in Table 6 gives the farebox recovery ratios for each policy simulation. For the simulation that assumes MTS operators charge METRO fares, the farebox recovery ratio falls from its FY92 level of 52.9 % to 43.7% when Model I is used and to 42.2% when Model II is used. The next three panels in Table 6 provide supporting information on total annual operating costs, annual service levels (annual bus revenue miles), and total annual fare revenues for each policy simulation. For the ®rst scenario (Policy 1), lower real fare levels cause total fare revenues to decline, even as ridership increases. This analysis indicates that it would cost MTS operators about US$7.55 million dollars a year to increase annual boardings by 7.61 million per year, or about one dollar per boarding. This is less than half of the total cost per boarding for MTS operators. The second and third San Diego policy simulations (Policies 2.1 and 2.2) assume MTS operators receive additional operating assistance in the amount of US$62.9 million per year (in 1992 dollars). This ®gure is equal to the di€erence in per capita operating expenditures by METRO and by San Diego's MTS operators, which is US$34 per capita (US$78 per capita minus US$44 per capita) times the population residing in the MTS service area. In the ®rst of two simulations that are based on this budget, we keep MTS fares at their FY92 levels and use the entire increase

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in operating subsidies to buy additional service miles. Using Model I this policy leads to an increase in total boardings of 53.2% and a reduction in the farebox recovery ratio of MTS operators from 52.9 % to 40.8%. Even with this large decline, the MTS' FY92 farebox recovery ratio still exceeds METRO's FY92 ratio, which was 25.1%. In addition, predicted MTS boardings for this scenario (which are 90.4 million) exceed METRO's actual FY92 boardings of 85 million, which, of course, are for a signi®cantly larger population. This result provides strong evidence that Houston is a signi®cantly more dicult region to serve with transit than San Diego. The results using Model II are similar, except that both predicted FY92 boardings and the farebox recovery are smaller than when Model I is used. Even so, predicted MTS boardings exceed actual FY92 boardings by 4%. In the third policy simulation (Policy 2.2), the increased operating budget is used to reduce MTS fares to the FY92 METRO level. The remaining subsidy dollars are used to buy additional service miles. Policy 2.2 is superior to Policy 2.1 that uses the entire increased operating budget to purchase more service miles. It produces 10% more boardings and its farebox recovery ratio is less than for Policy 2.1, 35.8% instead of 40.8%. This result demonstrates the doubtful value of farebox recovery ratios as performance indicators, since the total dollar amount of operating subsidies is the same in both cases. At best the farebox recovery ratio is redundant, as the goal should presumably be to obtain the largest possible ridership with a given subsidy. Worse yet, an in¯exible commitment to maintain, say, a 40% farebox recovery, might keep a transit authority from lowering fares, even though these analyses suggest that a commitment for a ®xed farebox recovery might reduce ridership below the level that would be achieved by the combination of lower fares and more service represented by Policy 2.2. Again, it cannot be overemphasized that the two policies cost the same in terms of the amount of subsidy required. The ®nal two policy simulations, Policies 3.1 and 3.2, both assume that MTS operators have the same level of operating subsidy per capita as METRO in FY92. The operations budget for these scenarios is greater than for Policies 2.1 and 2.2. This is because the use of equal per capita subsidies, rather than equal per capita operating expenditures, increases the available operating subsidies by an amount that is equal to the di€erence in MTS and METRO fare levels in FY92. The increase in operating funds in this case is US$66 million. These simulations o€er no surprises. Given that 72% more subsidy dollars are available, Policies 3.1 and 3.2 produce more boardings, 4.4% more for Policy 3.1 vs. Policy 2.1 and 4.3% more for Policy 3.2 vs. Policy 2.2, when Model I is used. In addition, the farebox recovery ratio for Policy 3.1 is slightly smaller than for Policy 2.1, and, similarly, the farebox recovery ratio is smaller for Policy 3.2 than for Policy 2.2. The results using Model II are qualitatively the same. As we suggested previously, these analyses o€er strong evidence that METRO confronts a much more dicult problem than transit operators in the MTDB service area. Some of the inferior per capita results for METRO at equal per capita operating budgets and subsidy could be explained by lower levels of eciency, but we doubt that this accounts for very much, if any, of the di€erence. A more likely explanation is that sprawling Houston is simply a very dicult environment to serve with transit. Rather than having done a poor job, our sense is that METRO has performed very well, and, in particular has made technology choices that made sense given Houston's urban development pattern.

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This is not to suggest that San Diego has done a poor job. It did an exemplary job in implementing its LRT system, and particularly the South Line. The system, moreover, is well suited to the region's topography and urban development patterns. By some measures, policymakers and voters in the region have made less of a commitment to transit than policymakers and voters in Houston. This, however, may simply re¯ect the fact that acceptable results can be achieved more cheaply in San Diego than in Houston. 12. Conclusions The details of the above comparisons and our discussion of speci®c di€erences should not be allowed to obscure the central ®nding that the large increases in transit use achieved by Houston and San Diego transit providers were caused principally by large service increases and fare reductions. These increases were made possible by large subsidies from federal, state and governments. The same experience, moreover, characterized a number of other urban areas where transit operators used large amounts of subsidy to increase service and reduce real fares. These successful policies were implemented in Portland (Oregon), Los Angeles, and Atlanta (Liu, 1993; Rubin and Moore, 1996; Kain, 1997). Regrettably, in all three cases, regional transit authorities abandoned their highly successful policies of increasing service levels and reducing real fares for policies that entailed using growing shares of available subsidy dollars to build and subsidize the operations of costly and ine€ectual rail systems. Frequently, the introduction of rail services was accompanied by increases in real transit fares. While MTDB's South Line, re¯ecting a variety of favorable factors, is arguably the most successful LRT line built in a US city since the end of World War II, the e€ectiveness of subsequent extensions have been more problematic. In the case of the most recent expansions, which became operational between August 1995 and November 1997, a 36% increase in mileage and a 91% increase in real capital costs, produced only a 7% increase in boardings. Part of this increase, of course, may be attributable to continued employment and population growth within the region and if the system-wide transfer rate increased as a result of system changes that were designed to feed the LRT, the story might be even less favorable. On the other side, if the unprecedented growth in boardings is real, it may be too early to fairly assess the impact of these system expansions on total transit ridership in the region. References Gomez-Ibanez, J., 1985. The dark side of light rail. Journal of the American Planning Association, 337±351. Kain, J.F., 1989. Estimation of a time series ridership model for Ottawa. Memorandum prepared for the Metropolitan Transit Authority of Harris County, Houston, Texas. Kain, J.F., et al., 1992. Increasing the Productivity of the Nation's Urban Transportation Infrastructure: Measures to Increase Transit Use and Carpooling: Final Report. US Department of Transportation, Urban Mass Transportation Administration, DOT-T-92-17. Kain, J.F., Liu, Z., 1995. Secrets of success: how Houston and San Diego transit providers achieved large increases in transit ridership. Report prepared for Federal Transit Administration. Kain, J.F., 1997. Cost-e€ective alternatives to Atlanta's costly rail rapid transit system. Journal of Transport Economics and Policy, 25±49.

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Kain, J.F., Liu, Z., 1998. An econometric analysis of the determinants of transit ridership, 1960±1990. Report prepared for the US Department of Transportation, Transport System Center, Cambridge, Massachusetts. Liu, Z., 1993. Determinants of public transit ridership: analysis of post World War II trends and evaluation of alternative networks. Ph.D. Dissertation, Harvard University. Metropolitan Transit Authority of Harris County, Texas (METRO), 1998. Comprehensive Annual Financial Report for the Fiscal Year Ended 30 September 1997 (12 February). Metropolitan Transit Development Board (MTDB), 1998. San Diego Regional Rail Transit Plan. San Diego, CA. Oce of Management and Budget, 1992. Bene®t-cost analysis of federal programs: guidelines and discounts. Circular No. A-94, Revised Transmittal Memorandum No. 64, 29 October. Federal Register, vol. 57, no. 218, Tuesday, 10 November, 53519±53528. Rubin, T.A., Moore II, J.E., 1996. Ten transit myths: misperceptions about rail transit in Los Angeles and the nation (Part 2 of a series on the MTA). Reason Foundation, Los Angeles, Policy Study No. 218.