A report produced by the Central Transportation Planning Staff for the Massachusetts Bay Transportation Authority

Transcription

1 $ T F D A R Impact Analysis of a Potential MBTA Fare Increase in 2009 A report produced by the Central Transportation Planning Staff for the Massachusetts Bay Transportation Authority

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3 Impact Analysis of a Potential MBTA Fare Increase in 2009 Prepared for Jonathan Davis, MBTA Chief Financial Officer Project Manager Elizabeth M. Moore Authors Robert Guptill Annette Demchur Contributing Staff Ian Harrington Scott Peterson Nand Sharma Cover Design Kim Noonan This report was funded by the Massachusetts Bay Transportation Authority. Central Transportation Planning Staff Directed by the Boston Region Metropolitan Planning Organization. The MPO is composed of state and regional agencies and authorities, and local governments. Draft July 2009

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5 TABLE OF CONTENTS LIST OF TABLES AND FIGURES...iv EXECUTIVE SUMMARY...1 INTRODUCTION...3 DESCRIPTION OF THE PROPOSED FARE INCREASE...4 METHODS USED TO ESTIMATE RIDERSHIP AND REVENUE...7 CTPS Spreadsheet Model Approach...7 Apportionment of Existing Ridership...7 Estimation of Ridership Changes...8 Boston Region MPO Travel Demand Model...8 RIDERSHIP AND REVENUE IMPACTS...11 AIR QUALITY IMPACTS...13 Background...13 Results of the Travel Demand Model Application...13 ENVIRONMENTAL JUSTICE IMPACTS...15 Definition of Environmental Justice Neighborhoods...15 Equity Determination of Proposed Fares...15 APPENDIX: SPREADSHEET MODEL METHODOLOGY...17 Apportionment of Existing Ridership...17 Price Elasticity Estimation...18 Price Elasticity...19 Diversion Factors...19 Examples of Ridership and Revenue Calculations...21 CTPS iii

6 LIST OF TABLES AND FIGURES TABLES E-1 Range of Revenue and Ridership Projections for the Proposed Fare Increase. 1 1 Range of Ridership Projections for Proposed Fare Increase Range of Revenue Projections for Proposed Fare Increase Range of Revenue and Ridership Projections for the Proposed Fare Increase: Summary 12 4 Projected Average Weekday Changes in Selected Pollutants (Regionwide) 14 5 Existing and Proposed Average Fares for Environmental Justice TAZs.. 16 A-1 Single-Ride and Pass Elasticities by Mode A-2 Simplified Price Comparison Summary 17 A-3 Detailed Price Comparison Summary FIGURES 1 AFC Fare Categories. 5 2 AFC Modal Categories.. 6 iv CTPS

7 EXECUTIVE SUMMARY The Massachusetts Bay Transportation Authority (MBTA) was facing a major budget deficit for fiscal year (FY) 2010 (July 1, 2009 June 30, 2010). The state legislature recently passed a budget that will close most of this gap; however, the Authority still faces the prospect of significant budget deficits in future years given the current and expected balances of its revenue and expenses. The MBTA has limited means by which to raise revenue sufficiently to close future budget deficits. These means are raising fares, reducing service, or a combination of both. The MBTA has proposed a fare increase of approximately 19.5 percent, which is projected to raise revenue between $52.3 million and $69.0 million annually. The purpose of this report is to project the impact of this potential fare increase on ridership, revenue, air quality, and environmental justice. CTPS, using a spreadsheet model, assisted the MBTA in determining the fare levels for each mode and fare category that would be needed to reach the revenue targets the MBTA had established. It then used several analysis techniques in estimating and evaluating the impacts of the proposed fare increase. Both the spreadsheet model and the Boston Region Metropolitan Planning Organization (MPO) regional travel demand model were used to estimate the projected ridership loss associated with any fare increase and the net revenue change that would result from the lower ridership. By employing both techniques, a range of potential impacts on ridership and revenue were produced. The travel demand model was also used to predict the effects of the fare increase on regional air quality and environmental justice. A summary of the total ridership and revenue projections for the proposed fare increase for each estimation methodology is presented in the following table. As the table indicates, CTPS estimated a smaller increase in revenue and smaller decrease in ridership when using the travel demand model than when using the spreadsheet approach. The difference in revenue gain is primarily due to the two models differing estimates of the loss in commuter rail and commuter boat ridership. Specifically, when using the travel demand model, CTPS projected a loss in commuter rail and commuter boat ridership from the fare increase almost double that projected when using the spreadsheet approach. Because commuter rail and commuter boat represent two of the MBTA s highest-priced modes, the travel demand model s estimate of a greater ridership loss for these services also results in a smaller estimated systemwide revenue gain. Table E-1 Range of Revenue and Ridership Projections for the Proposed Fare Increase Annual Revenue and Ridership* Spreadsheet Approach Travel Demand Model Existing Projected Change % Chg. Projected Change % Chg. Revenue $459.5M $528.5M $69.0M +15.0% $507.1M $ % Ridership 373.2M 354.5M -18.7M -5.0% 363.6M -9.6M -2.6% *Ridership figures are based on daily boardings or unlinked transit trips. Both the spreadsheet model and the travel demand model had been used to predict the CTPS 1

8 changes in ridership and revenue that would result from the 2007 MBTA fare increase. Subsequent to that increase, CTPS conducted an analysis that shows the projections from the spreadsheet model to be closer to the actual changes in ridership and revenue that occurred after that fare increase was implemented than are the projections from the travel demand model. This suggests that the spreadsheet model is the more accurate tool for predicting the ridership and revenue impacts of the currently proposed fare increase. However, using both models in combination helps to define a range of probable outcomes of the increase. Very little impact on air pollution is expected as a result of the proposed fare increase. The predicted increase in the levels of carbon monoxide, nitrogen oxides, volatile organic compounds, carbon dioxide, and fine and coarse particulates is, in each case, at or below 0.13 percent. The low estimated magnitude of the air quality impacts is due in part to projections that approximately a quarter of those who leave the transit system owing to the fare increase will choose to walk and the other three quarters, although shifting to auto, will have higher-than-average occupancy rates, overall, on their trips. The findings indicate that the proposed fare increase does not place a disproportionate burden on environmental justice communities. Low-income and minority communities are currently (pre fare increase) paying lower average fares than the systemwide average fare, and they will continue to do so after the fare increase. 2 CTPS

9 INTRODUCTION The Massachusetts Bay Transportation Authority (MBTA) currently faces serious financial problems. The state legislature recently passed a budget that will provide an additional $160 million in funding to the MBTA through an increase in the state sales tax. This will help to close the MBTA s fiscal year (FY) 2010 (July 1, 2009 June 30, 2010) budget deficit; however, given continued increases in operating expenses, projected decreases in revenue, and growing debt service costs for capital investments, the Authority will continue to face deficits in future years. The primary methods that the MBTA has at its disposal for addressing future deficits are raising fares to increase revenue and reducing service to decrease operating expenses. The MBTA recently explored various combinations of fare-increase and servicereduction levels to close the future budget deficits. It decided that a fare increase alone would be the most cost-effective option and the one least disruptive for MBTA customers. The amount of the fare increase proposed by the MBTA is directly related to the additional revenue that is needed to maintain existing levels of service through FY The first step in the analysis process was for CTPS, in consultation with the MBTA, to determine the fare levels for each mode and fare category that would be needed to reach the MBTA s revenue targets. This was accomplished through an iterative process in which CTPS utilized a spreadsheet model that was specifically developed to analyze the degree to which ridership and revenue would change if fares were raised by varying amounts. CTPS also produced alternative estimates of the increase s impact on both revenue and ridership using the Boston Region Metropolitan Planning Organization (MPO) regional travel demand model. A comparison of the projections of each model provides a range of estimated impacts on ridership and revenue. The impacts on air quality and environmental justice were projected using the travel demand model. In the following sections, this report presents detailed discussions of the proposed fare increase, the estimation methods used by CTPS in its analysis, and the projected impacts on ridership, revenue, air quality, and environmental justice. CTPS 3

10 DESCRIPTION OF THE PROPOSED FARE INCREASE CTPS modeled the impacts of a proposed fare increase that is projected to increase annual revenues by approximately $69.0 million. During development of the proposed fare levels, the MBTA decided not to make any changes to the fare structure. Significant time and effort were expended before the last fare increase, implemented in 2007, to simplify the fare structure and to provide riders with incentives to use the CharlieCard fare media that facilitate transfers between modes. While the proposed fare increase raises prices for various fare-type categories at varying levels, the suggested changes do not conflict with or alter the goals of the previous restructuring. Figure 1 summarizes existing and proposed fares. Figure 2 is a detailed list of existing and proposed fares for each fare category, along with the percentage change in price from the existing to the proposed price. The overall price increase across all modes and fare categories is approximately 19.5 percent. This weighted average was estimated by multiplying the percentage change in fare for each fare category by the existing ridership in that category. The largest proposed percentage increases in price are for local bus, express bus, and rapid transit CharlieTicket and onboard cash fares. These increases are higher than the more modest increases in the CharlieCard fares, due to increases in the surcharge percentages assessed to CharlieTicket and onboard cash fares for the local bus, express bus, and rapid transit modes. As a result, while the CharlieCard fares for local bus and rapid transit service increase by 20.0 percent and 17.6 percent, respectively, CharlieTicket and onboard cash fares increase by 33.3 percent for local bus and 25.0 percent for rapid transit. The increase in CharlieTicket and onboard cash surcharges is most apparent in the difference in price for transfers between local bus and rapid transit service. Under the proposed fare increase, with a CharlieCard, a step-up transfer between the modes would make the total price for a linked trip $2.00. The step-up transfer benefit is not available on CharlieTickets, however, resulting in a total proposed linked-trip price of $4.50 using CharlieTickets or onboard cash. Pass prices increase by less than the respective single-ride fares. They increase by various amounts in order to maintain or revise certain cash-fare equivalents (based on the lowest-priced respective single-ride fare), which are the number of single-ride trips equivalent to the total pass price. The cash-fare equivalent of commuter rail passes, for example, currently ranges from to trips per pass; under the proposed fare increase, the cash-fare equivalent would range from to trips per pass. The cash-fare equivalent would also decrease or remain virtually the same for local bus, express bus, and rapid transit passes. The proposed fare increase sets local bus and rapid transit fares for seniors and for users of the Transportation Access Pass, or TAP (persons with disabilities), at, respectively, 33 percent and 35 percent of the corresponding CharlieCard adult single-ride fare. Student fares and student, senior, and TAP passes are set at 50 percent of the corresponding 4 CTPS

11 CharlieCard adult single-ride fare or pass price for each mode. In addition, the fare for THE RIDE is set to increase by 25 percent. Figure 1 Simplified Price Comparison Summary CTPS 5

12 Figure 2 Detailed Price Comparison Summary 6 CTPS

13 METHODS USED TO ESTIMATE RIDERSHIP AND REVENUE Two separate approaches were used in this analysis to project the impact of the proposed fare increase on MBTA ridership and revenue. One approach utilized a set of spreadsheets originally created by CTPS and the MBTA to compute ridership and revenue impacts for proposed fare increases. The second approach consisted of applying the Boston Region MPO s regional travel demand model to estimate demand for each MBTA mode using the existing and proposed fare levels. The regional travel demand model was first employed as a complement to the spreadsheet model in the 2007 Pre Fare Increase Impacts Analysis, with the two models together providing some indication of the potential range of impacts. In addition, unlike the spreadsheet model, the regional travel demand model can be used to conduct the air quality and environmental justice impact analyses. CTPS Spreadsheet Model Approach The spreadsheet model used to estimate revenue and ridership impacts of the proposed 2009 fare increase reflects the many fare-payment categories of the MBTA pricing system and applies price elasticities to analyze various changes across these categories. The accuracy of this methodology was proven to be satisfactory through the 2007 Post Fare Increase Impacts Analysis, which analyzed its use in predicting the impacts of the proposed 2007 fare increase. Apportionment of Existing Ridership Inputs to the spreadsheet model include ridership in the form of unlinked trips, by mode, by fare-payment method, and by fare-media type. An unlinked trip represents the individual trip on any one transit vehicle; any trip using multiple vehicles so-called linked trips would count as multiple unlinked trips. Existing ridership (to which price elasticity figures are applied) for the local bus, express bus, and rapid transit networks is provided by automated fare-collection (AFC) data. Data are provided on a monthly basis, with subtotals of transactions (unlinked trips) by the various combinations of product type (single-ride fare or pass) and stock (smart card, magnetic-stripe ticket, etc.). Product types are then coded and summarized by fare type and fare mode, while stock types are coded and summarized by the type of fare media. AFC data are also provided at the modal level at which each transaction occurs. More detailed information on AFC fare types, modes, and media can be found in the appendix. Because AFC equipment has not yet been deployed on commuter rail and commuter boat, the number of trips on these modes was estimated. Single-ride trips on commuter rail and commuter boat were set equal to the number of single-ride fares sold, while pass trips on these modes were estimated by dividing the number of pass sales by the estimated average number of trips made using the respective pass type, calculated as part of the 2007 Post Fare Increase Impacts Analysis. Dividing the number of pass sales by the CTPS 7

14 estimated number of trips per pass results in an estimate of the total number of pass trips by each modal category. Other data used were estimates of the number of trips made using THE RIDE and the number of cars parked at transit stations. These data were provided to CTPS directly by the MBTA. Estimation of Ridership Changes Fares are one of many factors that influence the level of ridership on transit services. Price elasticity is the measure of either the expected or observed rate of change in ridership relative to a change in fares if all other factors remain constant. On a traditional demand curve that describes the relationship between price, on the y-axis, and demand, on the x-axis, elasticities are equivalent to the slope along that curve. As such, price elasticities are generally expected to be negative, meaning that a price increase will lead to a decrease in demand (with a price decrease having the opposite effect). As the absolute value of the price elasticity increases, the projected impact on demand also grows. Larger (or more negative) price elasticities are said to be relatively elastic, while smaller negative values, closer to zero, are said to be relatively inelastic. Thus, if the price elasticity of the demand for transit were relatively elastic, a given fare increase would cause a greater loss of ridership than if demand were relatively inelastic. An example of the application of price elasticities is demonstrated in the appendix. The only modal category where this methodology is not used is parking at transit stations. Since the fare increase scenario does not include an increase in any parking rates, the loss in parking trips was assumed to equal, for each parking lot, the estimated percentage decrease in the corresponding transit modal category. For example, each commuter rail parking lot was assumed to experience the same percentage decrease in parked cars as the overall decrease in the corresponding zone s commuter rail trips. An additional complexity providing increased accuracy of the spreadsheet model occurs with the use of ridership diversion factors. These factors reflect estimates of the likelihood of a switch in demand from one good to another as a result of a change in the relative prices of those goods. The diversion factors essentially work to redistribute demand between the two categories after the respective price elasticities have been applied. The appendix provides examples of the application of diversion factors and the methodology used to combine price elasticities and diversion factors. Boston Region MPO Travel Demand Model The regional travel demand model used by CTPS simulates travel on the transportation network in eastern Massachusetts, including both the transit and highway systems. It covers all MBTA commuter rail, rapid transit, and bus services, as well as all private express bus services. The regional travel demand model reflects service frequency (how often trains and buses arrive at a given transit stop), routing, travel time, and fares for all 8 CTPS

15 these services. In the highway system, all express highways, all principal arterial roadways, and many minor arterial and local roadways are included. The travel demand forecasting procedure used in this analysis is based on a traditional four-step, sequential process: trip generation, trip distribution, mode choice, and trip assignment. This process may be used to estimate average daily transit ridership, primarily on the basis of estimates of population and employment, projected highway travel conditions (including downtown parking costs), and projected transit service to be provided. As such, the model was used to analyze MBTA ridership and revenue impacts due to the proposed fare increase. The eastern Massachusetts geographic area represented in the regional travel demand model is divided into several hundred smaller areas known as transportation analysis zones (TAZs). The travel demand model employs sophisticated and complex techniques in each of the four steps of the process. The following paragraphs describe very briefly what each step does. Trip Generation In this step, the regional travel demand model estimates the number of trips produced in and attracted to each TAZ. To do this, the model uses estimates of population, employment, and other socioeconomic and household characteristics of that zone. Trip Distribution In the trip distribution step, the model links the trip ends estimated in the trip generation step to form zonal trip interchanges (movements between two zones). The output of this second step of the four-step process is a trip table, which is a matrix containing the number of trips occurring in every origin-zone-to-destination-zone combination. Mode Choice The mode choice step allocates the person trips estimated from the trip distribution step to the two primary competing modes, automobile and transit. This allocation is based on the desirability or utility of each choice a traveler can opt for, based on the attributes of that choice and the characteristics of the individual. The resulting output of the mode choice step is the percentage of trips that use automobiles and the percentage that use transit for all trips that have been generated. Trip Assignment In this final step, the regional travel demand model assigns the transit trips to different transit modes, such as subway, commuter rail, local bus, or express bus. To do this, it uses multiple transit paths from one zone to another that minimize generalized cost. These paths may involve just one mode, such as a local bus or commuter rail, or multiple modes, such as a local bus and a transfer to the subway. The model also assigns the highway trips to the highway network in this step. Thus, the traffic volumes on the highways and transit ridership on different transit lines can be obtained from the regional travel demand model outputs. Population and employment data are key inputs to the demand forecasting process. The data used in this study were obtained from the Metropolitan Area Planning Council (MAPC). The highway travel times used in the analysis are those used in recent CTPS CTPS 9

16 transit and highway studies. Downtown parking costs were obtained from recent CTPS studies. The regional travel demand model assumes that, in general, people wish to minimize transfers. It also assumes that they may wish to minimize travel time, even if doing so costs more. 10 CTPS

17 RIDERSHIP AND REVENUE IMPACTS The potential range of ridership and revenue impacts of the proposed 19.5 percent fare increase is presented in Tables 1 and 2. These impacts were projected, as discussed above, using both the spreadsheet model and the regional travel demand model. As can be seen, estimates from the regional travel demand model show a smaller overall decrease in ridership and increase in revenue than are projected using the spreadsheet model. Using the travel demand model, CTPS projects a decrease of 9.6 million unlinked trips, or a 2.6 percent decrease, compared to 18.7 million unlinked trips, or a 5.0 percent decrease, using the spreadsheet model. The projections from the regional travel demand model show smaller ridership decreases in the local bus, express bus, and rapid transit modal categories and larger ridership decreases in the commuter rail and commuter boat modal categories compared to the spreadsheet model. In terms of annual revenue, projections from the regional travel demand model show a gain of $52.3 million in annual revenue, or an 11.4 percent increase, compared to $69.0 million, or a 15.0 percent increase, for the spreadsheet model. Taken together, the projections shown in these tables provide a range of probable outcomes from the proposed fare increase in terms of ridership and revenue impacts. Table 1 Range of Ridership Projections for Proposed Fare Increase Annual Ridership Spreadsheet Model Travel Demand Model Mode Existing Projected # Chg. % Chg. Projected # Chg. % Chg. Local/Express Bus 101.9M 97.5M -4.5M -4.4% 101.0M -0.9M -0.9% Rapid Transit 237.3M 224.6M -12.7M -5.3% 231.4M -5.9M -2.5% Commuter Rail 30.9M 29.5M -1.4M -4.6% 28.4M -2.5M -8.1% Commuter Boat 1.2M 1.2M <-0.1M -3.7% 1.0M -0.2M -16.7% THE RIDE 1.9M 1.8M <-0.1M -3.7% 1.8M <-0.1M -3.7% Total 373.2M 354.5M -18.7M -5.0% 363.6M -9.6M -2.6% Table 2 Range of Revenue Projections for Proposed Fare Increase Annual Revenue Spreadsheet Model Travel Demand Model Mode Existing Projected $ Chg. % Chg. Projected $ Chg. % Chg. Local/Express Bus $76.3M $90.9M +$14.6M +19.1% Rapid Transit $247.9M $287.4M +$39.5M +15.9% Commuter Rail $126.1M $139.7M +$13.5M +10.7% Commuter Boat $5.3M $6.0M +$0.7M +12.6% THE RIDE $3.8M $4.6M +$0.8M +20.3% Total $459.5M $528.5M +$69.0M +15.0% $507.1M +$52.3M +11.4% CTPS 11

18 A summary of the total ridership and revenue projections for the proposed fare increase for each estimation methodology is presented in Table 3. As this and the previous tables indicate, slightly smaller changes in revenue and ridership are estimated using the travel demand model than are estimated using the spreadsheet approach. These differences are primarily due to the two models differing estimates of the loss in commuter rail and commuter boat ridership. Specifically, the loss in commuter rail and commuter boat ridership resulting from the fare increase when estimated using the travel demand model is almost double that estimated using the spreadsheet approach. The estimates from the travel demand model are based on the assumption that, because the cost for commuter rail and commuter boat riders is already high, the fare increase brings them to a level where driving becomes a more competitive option than it is for users of other transit modes. Riders on commuter rail and commuter boat are, therefore, more likely to shift to the auto mode, even though the percentage increase in their fares is the same as for other modes. Because commuter rail and commuter boat are two of the MBTA s highest-priced services, the travel demand model s estimate of a greater ridership loss for these services also results in a slightly smaller estimated overall revenue gain for the system as a whole. Table 3 Range of Revenue and Ridership Projections for the Proposed Fare Increase: Summary Annual Revenue and Ridership Spreadsheet Approach Travel Demand Model Existing Projected Change % Chg. Projected Change % Chg. Revenue $459.5M $528.5M $69.0M +15.0% $507.1M $ % Ridership 373.2M 354.5M -18.7M -5.0% 363.6M -9.6M -2.6% The $69.0 million gain projected using the spreadsheet model is in keeping with the target amount that the MBTA identified as needed to meet budget deficits in the short term. The 2007 Post Fare Increase Impacts Analysis shows the projections from the spreadsheet model, which uses elasticities to project ridership and revenue changes based on a detailed analysis by mode and fare category, to be close to the actual changes in ridership and revenue that occurred after that fare increase was implemented. In addition, through the 2007 Post Fare Increase Impacts Analysis, CTPS was able to adjust the elasticities in the spreadsheet model to even better reflect the changes that occurred. This presumably makes the spreadsheet model more accurate for predicting the ridership and revenue impacts of the currently proposed fare increase. However, using both models in combination helps to define a range of probable outcomes of the increase. 12 CTPS

19 AIR QUALITY IMPACTS Background The air quality impacts of alternative transportation scenarios can be analyzed using standard transportation-forecasting models, including the one maintained by CTPS. These models can be used to estimate future traffic volumes, average highway speeds, and vehicle-miles and vehicle-hours traveled within the transportation network. Since the amount of air pollution emitted by highway traffic depends on the prevailing highway speeds and vehicle-miles traveled, it is possible to estimate these air quality impacts with reasonable accuracy. Air pollutants produced by vehicles generally fall into two groups: gaseous and particulate. Examples of gaseous pollutants include carbon monoxide (CO), volatile organic compounds (VOC, also known as hydrocarbons), nitrogen oxides (NOx), and carbon dioxide (CO2). In addition, there are the photochemical oxidants, which are not directly emitted from vehicles but are formed when VOC and NOx chemically react in the presence of sunlight and warm temperatures. Particulate pollutants produced by vehicles include carbon particles and lead compounds and are commonly broken into two categories: fine particulates those with a diameter of 2.5 micrometers or less, and coarse particulates those with a diameter between 2.5 and 10 micrometers. In the case of Boston, which is in attainment of the U.S. Environmental Protection Agency (EPA) standards for particulate emissions, the EPA is primarily interested in the gaseous pollutants produced by the transportation sector. More particularly, the EPA requires that planning agencies report the amount of CO, NOx, and VOC produced by the transportation system in such documents as the Transportation Improvement Program and the Metropolitan Transportation Plan. CTPS employs EPA MOBILE 6.2 emission factors 1 for calculating the pollutants. For each link within the highway network, the travel demand model applies the MOBILE 6.2 emission factors corresponding to the link s average speed and estimates the pollutants based on the vehicle-miles traveled on that link. The amount of total pollutants for the entire region is obtained by summing all the pollutants associated with each link in the system. Results of the Travel Demand Model Application With respect to the proposed fare increase, the air quality impacts are primarily those associated with existing transit users who choose to drive to their destinations instead of using transit. A reduction in transit trips and addition of automobile trips generally causes an increase in the generation of carbon monoxide, nitrogen oxides, volatile organic compounds, carbon dioxide, and particulate matter, which can be measured in the manner described in the preceding paragraph. It should be noted that as the number of automobile trips increases, the congestion on area roadways also increases. This additional 1 In kilograms of pollutant per vehicle-mile traveled. CTPS 13

20 congestion results in lower travel speeds for all automobiles, not just those of former transit users. After calculating the ridership impacts of the proposed fare increase as described earlier in this analysis, CTPS used the travel demand model to estimate the change in regional automobile vehicle-miles traveled and average miles per hour. Specifically, the model identifies the path of each automobile trip made by a former transit user and also estimates the travel times for all automobile trips. CTPS applied to these data the emission factors provided by the EPA that are associated with each of the pollutants identified above. As shown in Table 4, vehicle-miles and vehicle-hours traveled are estimated to increase slightly and average miles per hour are estimated to decrease slightly because of the proposed fare increase. The projected regional increase in each of the selected pollutants is at or below 0.13 percent. Any loss in transit ridership that results in a gain in vehiclemiles and -hours traveled will lead to some level of increase in pollutants. However, in the present case, given that the margin of error associated with the model is likely larger than the estimated increase, the actual change could be negligible. Table 4 Projected Average Weekday Changes in Selected Pollutants (Regionwide) Indicator/Pollutant Absolute Change Percent Change Vehicle-miles traveled +104, % Vehicle-hours traveled +7, % Average miles per hour % Carbon monoxide (kg) +1, % Nitrogen oxides (kg) % Volatile organic compounds (kg) % Carbon dioxide (kg) +58, % Fine particulates (kg) % Coarse particulates (kg) % The low estimated magnitude of the air quality impacts is due in part to projections from the travel demand model that approximately one quarter of those who leave the transit system due to the fare increase will choose to walk instead. This is particularly true for riders in the urban core who make short transit trips. In addition, the model projects that the other approximately three quarters of those who leave transit, who would shift to the auto mode, would have a slightly higher-than-average vehicle occupancy rate, overall, on those auto trips. This result is related to the demographic inputs to the model that associate income levels and vehicle ownership to geographic location. Some riders who leave the transit system owing to increased cost are projected to live in areas where average income and rates of auto ownership tend to be lower, making them less likely to shift to driving alone. 14 CTPS

21 ENVIRONMENTAL JUSTICE IMPACTS Definition of Environmental Justice Neighborhoods To assess the impacts of the proposed fare increase on minority and low-income communities, an environmental justice impacts analysis was undertaken. Environmental justice neighborhoods were identified based on a methodology developed from Federal Transit Administration guidance to the MBTA s ongoing Title VI program and past practice of CTPS. For the purposes of Title VI analyses, the MBTA has defined two service areas with different demographic characteristics. The Authority, therefore, has two sets of criteria for identifying environmental justice neighborhoods: one for the urban fixed-route transit system and another for the commuter rail system. For each service area, the average annual income level and the percentage of minority population in each transportation analysis zone (TAZ) were identified. A TAZ was then defined as low-income if its income level was at or below 60 percent of the median household income in the service area; this meant at or below $32,120 in the urban fixedroute transit service area and $32,582 in the commuter rail service area. Minority TAZs were defined as those in which the percentage of non-white population (including the Hispanic population) was greater than the average for the service area. The average percentage of minority residents is 24.7 percent in the urban fixed-route transit service area, and it is 19.9 percent in the commuter rail service area. Any TAZ which qualifies as either minority or low-income is considered an environmental justice community. Equity Determination of Proposed Fares After identifying the minority and low-income communities, the equity of the system s fare levels was assessed in terms of both the existing and proposed conditions, using the Boston Region MPO s regional travel demand model. Under the current fare levels, the average fare for low-income TAZs is estimated to be $1.57 for the urban fixed-route service area (which is $0.26 below the average for all TAZs in this service area), and $1.69 for the commuter rail service area (which is $0.38 below the average for all TAZs in this service area). The estimated average fare for minority TAZs is $1.57 for the urban fixed-route service area (which is $0.26 below the average for all TAZs in this service area), and $1.73 for the commuter rail service area (which is $0.34 below the average for all TAZs in this service area). Under the proposed fare increase, the average fares for low-income and minority TAZs are estimated to be $1.66 and $1.67, respectively, in the urban fixed-route service area, while the average for all TAZs in this service area is estimated to be $1.95. The average fares for low-income and minority TAZs are estimated to be $1.81 and $1.85, respectively, in the commuter rail service area, while the average for all TAZs in this service area is estimated to be $2.22. Table 5 compares the existing and proposed average fares associated with each environmental justice category and with each of the service areas overall. CTPS 15

22 Table 5 Existing and Proposed Average Fares for Environmental Justice TAZs Urban-Fixed-Route Service Area Proposed Average Absolute Change in EJ Classification Existing Average Fare Fare Fare Low-income TAZs $1.57 $1.66 $0.09 Minority TAZs $1.57 $1.67 $0.10 Service area average $1.83 $1.95 $0.12 Commuter Rail Service Area Proposed Average Absolute Change in EJ Classification Existing Average Fare Fare Fare Low-income TAZs $1.69 $1.81 $0.12 Minority TAZs $1.73 $1.85 $0.12 Service area average $2.07 $2.22 $0.15 The table indicates that the proposed fare levels, as well as the current fares, do not place a disproportionate burden on environmental justice communities. Riders boarding in lowincome and minority TAZs continue to pay lower average fares than the average passenger. In moving from the existing to the proposed fare levels, the increase in average fare paid in low-income and minority TAZs is lower than the increase in fare for the average passenger in both the urban fixed-route service area and the commuter rail service area. 16 CTPS

23 APPENDIX: SPREADSHEET MODEL METHODOLOGY Apportionment of Existing Ridership Automated fare-collection (AFC) data are provided on a monthly basis, with subtotals of transactions (unlinked trips) by the various combinations of product (type of single-ride fare or pass) and stock (smart card, magnetic-stripe ticket, etc.). Product types are then coded and summarized by fare type and fare mode, while stock types are coded and summarized by the type of fare media. The various categories and combinations of fare type, fare mode, and fare media that are used in the AFC system are presented in Table A-1. Table A-1 AFC Fare Categories Fare Type Adult/Senior/TAP/Student/Free Fare Mode Single-Ride Fare Media CharlieCard CharlieTicket Adult/Senior/TAP/Student Transfer Onboard Cash CharlieCard CharlieTicket Short (fares below the full value) Single-Ride Onboard Cash Bus/Inner Express/Outer Express LinkPass: Monthly/1-Day/7-Day Commuter Rail Zone and Interzone/Commuter Boat Senior/TAP/Student Pass Pass Pass Pass CharlieCard CharlieTicket CharlieCard CharlieTicket CharlieTicket CharlieCard CharlieTicket AFC data are also provided at the modal level at which each transaction occurs. Table A- 2 lists the modal and summary categories used in the Post Fare Increase Impacts Analysis. Table A-2 AFC Modal Categories Mode Bus/ Trackless Trolley Rapid Transit Local Bus Inner Express Bus Outer Express Bus Red, Orange, Blue, Green, Silver Waterfront Subway Silver Line Waterfront Surface Silver Line Washington Street Green Line Surface B, C, D, E Mattapan AFC Equipment Farebox Farebox Farebox Faregate Farebox Farebox Farebox/Validator Farebox CTPS 17

24 Price Elasticity Estimation Subsequent to the 2007 fare increase, CTPS conducted a Post Fare Increase Impacts Analysis, which was intended, in part, to test the accuracy of the elasticities used for the Pre Fare Increase Impacts Analysis to project changes in ridership that would result from higher fares. The price elasticities used in the spreadsheet model for the proposed 2009 fare increase are based on the demonstrated elasticities from the 2007 Post Fare Increase Impacts Analysis. In that analysis, CTPS used the percentage change in trips and price for each fare payment category and calculated the price elasticities. While the elasticities of smaller, individual categories were likely unreliable, CTPS was able to determine realistic elasticity figures for larger and more general categories. Those figures were generally found to be more elastic than the elasticity inputs used in the 2007 Pre Fare Increase Impacts Analysis, which resulted in underestimation of the ridership loss from the 2007 fare increase. As such, the elasticities used in the 2009 Pre Fare Increase Impacts Analysis reflect those observed following the previous fare increase. It is admittedly difficult to isolate the effects of price elasticity alone on changes in demand. Over the course of a year (in which time it is assumed the effects of price changes are largely internalized by the population), economic, demographic, and other factors may play as much, if not more of, a role in influencing transit demand than price. Indeed, the upcoming year promises to be characterized by significant uncertainty with regard to these larger trends. Table A-3 presents the elasticities used in the spreadsheet model categorized by the type of fare payment and mode. Elasticities are divided between two fare-payment categories: single-ride (or pay-per-ride) and pass. According to the 2007 Post Fare Increase Impacts Analysis, single-ride price elasticities are assumed to be less elastic than pass price elasticities, signifying that the spreadsheet model projects single-ride customers to be slightly less responsive to changes in price than pass customers. Elasticities are further divided into several modal categories: Bus/Trackless Trolley, Subway and Silver Line Waterfront, Silver Line Washington Street, Surface Green Line, Commuter Rail, Commuter Boat, and THE RIDE. All modes have both single-ride and pass price elasticity except for THE RIDE, which applies only to single-ride customers. Table A-3 Single-Ride and Pass Elasticities by Mode Price Elasticities Single-Ride Pass Bus/Trackless Trolley Subway and Silver Line Waterfront Silver Line Washington Street Surface Green Line Commuter Rail Commuter Boat THE RIDE n/a According to the 2007 Post Fare Increase Impacts Analysis, the least elastic of the single-ride modal price elasticities is that for THE RIDE, followed by Bus/Trackless 18 CTPS

25 Trolley and Silver Line Washington Street, Subway and Silver Line Waterfront, Commuter Boat, Surface Green Line, and, finally, Commuter Rail. For pass price elasticities, Commuter Rail, along with Commuter Boat, is the least elastic of the modes. Bus/Trackless Trolley is the next least elastic, followed by Silver Line Washington Street, Subway and Silver Line Waterfront, and Surface Green Line. Price Elasticity Price elasticity is the measure of either the expected or observed rate of change in ridership relative to a change in fares if all other factors remain constant. On a traditional demand curve that describes the relationship between price, on the y-axis, and demand, on the x-axis, elasticities are equivalent to the slope along that curve. As such, price elasticities are generally expected to be negative, meaning that a positive price increase will lead to a decrease in demand (with a price decrease having the opposite effect). As the absolute value of the price elasticity increases, the projected impact on demand also grows. Larger (or more negative) price elasticities are said to be relatively elastic, while smaller negative values, closer to zero, are said to be relatively inelastic. Thus, if the price elasticity of the demand for transit is assumed to be elastic, a given fare increase would cause a greater loss of ridership than if demand were assumed to be inelastic. At its most elemental level, the spreadsheet model is based on this simple price elasticity relationship, and requires four inputs: original demand, original fare, new fare, and the price elasticity. The formula for calculating new demand is the following: New Demand = Original Demand [1 + Price Elasticity * (New Fare / Old Fare - 1)] As an example, assume that original demand equals 100 and that the impact that is being modeled is a 10 percent fare increase from $1.00 to $1.10. Also assume that the price elasticity is New Demand = 100 [ * ($1.10 / $1.00-1)] = Thus, using an elasticity of -0.25, a simple price elasticity model projects that a 10 percent increase in price will lead to a 2.50 percent decrease in demand. With the fare increased from $1.00 to $1.10, this simplified model projects a 7.25 percent increase in revenue ($ to $107.25). Diversion Factors An additional complexity providing increased accuracy of the spreadsheet model occurs with the use of ridership diversion factors. These factors reflect estimates of the likelihood of a switch in demand for one good to another that is related to the change in the relative prices of those goods. Using price elasticities and diversion factors for cash and pass customers as an example, assume that original ridership equals 100 cash riders and 1,000 pass riders. Also assume that original prices for cash tickets and passes equal $2.00 and $100.00, respectively, and that the new prices are set at $1.50 for cash tickets and $50.00 for passes, representing price decreases of 25 percent and 50 percent. Assume CTPS 19

26 that the cash price elasticity equals and the pass price elasticity equals Finally, assume a cash-to-pass diversion factor of 0.05 and a pass-to-cash diversion factor of In these pairs of diversion factors, one of the factors must always equal zero, indicating that the diversion is expected to occur in one direction only. The direction of the diversion, and thus the diversion factor values, depends on the categories respective price changes. The category with the greater relative price decrease (or the smaller relative price increase), in this case, passes, where the price decrease is 50 percent compared to 25 percent for cash tickets, would gain riders from the diversion, while the other category with the smaller relative price decrease (or the greater relative price increase) would lose riders from the diversion. One would therefore expect that cash customers would switch to passes, but not that pass customers would switch to cash tickets (hence the 0.05 cash-to-pass and 0.00 pass-to-cash diversion factors). The diversion factors essentially work to redistribute demand between the two categories after the respective price elasticities have been applied. For instance, after the cash fare is decreased from $2.00 to $1.50, the projected effect of price elasticity is that cash demand grows to Similarly, the pass price decrease from $100 to $50 leads to a projected increase in pass demand, due to price elasticity, to 1,125, for a total ridership of 1, However, the percentage decrease in the pass price is larger than that for cash fares (50 percent versus 25 percent); thus, one would expect some customers to switch from cash to passes. This diversion is estimated by taking the ratio of new-to-original cash prices ($1.50/$2.00, or 75 percent), dividing that ratio by the ratio of new-to-original pass prices ($50/$100, or 50 percent), subtracting 1, and multiplying this result by the 0.05 diversion factor and the price-elasticity-estimated cash ridership (108.75). The number of riders diverted from cash to pass equals 2.72, giving final ridership estimates of for cash and 1, for passes, again summing to a total ridership of 1, New Cash Demand (Price Effect), C p = 100 [ * ($1.50 / $2.00-1)] = New Pass Demand (Price Effect), P p = 1,000 [ * ($50 / $100-1)] = 1, Total Demand = , = 1, $ NewCash / $ OldCash Diverted Riders from Cash to Pass = 1 Diversion C $ NewPass / $ OldPass $1.50 / $2.00 Diverted Riders from Cash to Pass = C $50 / $100 New Cash Demand = C p Diverted Riders from Cash to Pass = New Pass Demand = P p + Diverted Riders from Cash to Pass = 1, Total Demand = , = 1, P = 2.72 Diversion rates are broken down into cash-versus-pass categories (for example, bus cash versus bus pass, subway cash versus subway pass, etc.), or bus versus rapid transit (in P 20 CTPS

27 other words, bus cash versus subway cash, bus pass versus subway pass), and CharlieTicket and onboard cash versus CharlieCard (for example, bus onboard cash versus bus CharlieCard, subway CharlieTicket versus subway CharlieCard, etc.). The rates were determined based on the 2007 Post Fare Increase Impacts Analysis. Examples of Ridership and Revenue Calculations Simple Example: Price Elasticity Only Original Demand: 100,000 Original Fare: $1.50 New Fare: $2.50 Price Elasticity: New Demand = 100,000 [ * ($2.50 / $1.50-1)] = 96, More Complex Example: Price Elasticity plus Ridership Diversion Cash to Pass Original Cash Demand: 10,000 Original Cash Fare: $2.25 New Cash Fare: $2.00 Cash Price Elasticity: New Cash Demand (Price Effect), C p = 10,000 [ * ($2.00 / $2.25-1)] = 10, Original Pass Demand: 5,000 Original Pass Price: $71.00 New Pass Price: $50.00 Pass Price Elasticity: New Pass Demand (Price Effect), P p = 5,000 [ * ($50 / $71-1)] = 5, Total Demand = 10, , = 15, Percentage Change in Cash Price: $2.25 to $2.00: -11% Percentage Change in Pass Price: $71 to $50: -30% Cash to Pass Diversion Factor: 0.05 Pass to Cash Diversion Factor: 0.00 Diverted Riders from Cash to Pass = $2.00 / $ C $50 / $71 P = New Cash Demand = C p Diverted Riders from Cash to Pass = 10, CTPS 21

28 New Pass Demand = P p + Diverted Riders from Cash to Pass = 5, Total Demand = 10, , = 15, Additionally Complex Example: Price Elasticity Plus Two Ridership Diversions Single-Ride CharlieCard (SR-CC) to Pass and Single-Ride CharlieTicket (SR-CT) to Single-Ride CharlieCard (SR-CC) Original Single-Ride CharlieCard Demand: 10,000 Original Single-Ride CharlieCard Fare: $2.20 New Single-Ride CharlieCard Fare: $3.50 Single-Ride CharlieCard Price Elasticity: New SR-CC Demand (Price Effect), CC p = 10,000 [ * ($3.50 / $2.20-1)] = 8, Original Pass Demand: 50,000 Original Pass Price: $71.00 New Pass Price: $90.00 Pass Price Elasticity: New Pass Demand (Price Effect), P p = 50,000 [ * ($90 / $71-1)] = 46, Original Single-Ride CharlieTicket Demand: 5,000 Original Single-Ride CharlieTicket Fare: $2.50 New Single-Ride CharlieTicket Fare: $4.50 Single-Ride CharlieTicket Price Elasticity: New SR-CT Demand (Price Effect), CT p = 5,000 [ * ($4.50 / $2.50-1)] = 3, Total Demand = , , = 58, Percentage Change in Single-Ride CharlieCard Fare: $2.20 to $3.50: 59.09% Percentage Change in Pass Price: $71 to $90: 26.76% Percentage Change in Single-Ride CharlieTicket Fare: $2.50 to $4.50: 80.00% Single-Ride CharlieCard to Pass Diversion Factor: 0.05 Pass to Single-Ride CharlieCard Diversion Factor: 0.00 Single-Ride CharlieCard to Single-Ride CharlieTicket Diversion Factor: 0.00 Single-Ride CharlieTicket to Single-Ride CharlieCard Diversion Factor: 0.25 Diverted Riders from SR-CC to Pass = $3.50 / $ * 0.05 * CC p = $90 / $71 22 CTPS