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24 agencies is driven more by politics and funding availability and transfer locations can be inferred through analysis of indi- than by ridership analysis. Although many agencies can see vidual MetroCard use (MetroCard captures boarding data the value of employing a forecasting methodology, it may not only). MTANYCT recently contracted with a private firm to rank highly in terms of current needs. This is a valid assess- construct subway and bus trip tables from raw MetroCard ment in many cases and is a useful point to keep in mind for data; a significant undertaking given that the database holds this synthesis project. more than 7 million transaction records for each weekday. In the interim, the agency has been using a subway-only MTANEW YORK CITY TRANSIT (NEW YORK, NEW YORK) method for inferring destination stations for each station entry and then using the resulting trip table with the network MTANew York City Transit (NYCT) prepares ridership model to estimate subway travel patterns and route usage. forecasts for most service changes except for minor service For bus trip patterns, MTANYCT designed an iterative adjustments and scheduling changes. There is no specific probability model to predict alightings at bus stops based on threshold triggering the need for a ridership forecast. Signif- total boardings and alightings from ridechecks, the travel icant subway service changes, especially during peak hours, time between stops, and total passengers alighting at a spe- are likely to require a forecast. Bus service changes are less cific bus stop. This model produces an acceptable result likely to trigger ridership forecasts. Management decides within a few iterations. The resulting stop-to-stop origin/ whether a ridership forecast is needed. There is a dedicated destination trip tables are being used to estimate ridership on group that generates ridership forecasts, but they work pri- proposed BRT lines. Recent efforts focus on integrating sub- marily on major projects such as the Second Avenue subway, way origins with feeder bus alightings and combining Metro- the Manhattan Bridge reconstruction, and BRT. Forecasts for Card and census journey-to-work data. The analysis of other changes are sometimes done by transit analysts as part expected transfer levels to the new Second Avenue subway of their regular duties. line has been used in some station designs. The Operations Planning Department takes the lead for MTANYCT uses a variety of data sources beyond the preparing most ridership forecasts, although the Office of MetroCard in developing ridership forecasts. The agency Management and Budget typically prepares annual forecasts does not have APCs, but uses a large contingent of trained for budget purposes. Forecasts are distributed and used inter- traffic checkers to gather data through ridechecks and nally. MTANYCT considers a wide variety of inputs for its pointchecks at peak load points and central business district forecasts, although system-level ridership is used primarily cordon points. MTANYCT also relies on farebox/turnstile for annual forecasts. data, origin/destination data from travel models, census and Census Transportation Planning Package demographic data, MTANYCT maintains and uses a detailed network existing and forecast land use, and economic trends and fore- model of all subway and bus routes in New York City and casts. GIS programs have helped in organizing large collec- walking links for access and transferring. This network tions of data. New York City has developed a new GIS base model is used to analyze current travel patterns by assigning map (NYCMAP) with high-quality aerial photographs, and subway origin/destination trip tables estimated from Metro- MTANYCT is making increasing use of this map. The city Card farecard transactions. It is also used to model future has also developed a land use database in GIS. major service changes or additions by using census-based trip tables projected into the future. Shifts between bus and Technology has clearly had an impact on forecasting subway modes are estimated using this model; however, methodology. AFC equipment provides ridership boarding there is no provision within this model for attracting auto- data in 6-min increments, which allows for origin/destination mobile or taxi users because the existing transit share is gen- estimation. Improved personal computers and software permit erally already high. Mode share modeling as well as regional more detailed methodologies that can be applied more quickly. impacts is addressed by a regional transit forecasting model Input data reliability is a problem in terms of the accuracy of that is maintained by the MTA, which is MTANYCT's par- pointchecks for on-board train volumes, along with the labor- ent agency. It incorporates most of MTANYCT's bus and intensive nature of collecting enough samples to compensate. subway network model. A service elasticity of 0.2 is used Ridechecks are practical on buses, but not on subways. primarily to estimate the impacts of contingency service reductions. The Operations Planning Department and Office Short-term forecasts are based on ridership trends and of Management and Budget work together to mine Metro- known land uses, whereas long-range forecasts use detailed Card data. socioeconomic forecasts. Short-term forecasts can be com- pleted within 1 to 5 days by service planners, including time Introduction of the MetroCard has proven to be very use- for supplementary ridechecks. A simple long-term forecast ful for both subway and bus ridership analysis. The Metro- can be completed in one week; however, more complex Card provides a record of subway station usage by time of day forecasts of alternatives can take up to a year. Typically, and bus route usage by direction and time of day. Linked trips one-quarter to one-half the time of two analysts is needed to

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25 forecast the ridership impacts of major subway service A 10-year ridership forecast as part of a long-range plan: changes or additions, either line-specific or at major station begin by forecasting an origin/destination trip table complexes. Ridership forecasting models are often used as based on existing trips and socioeconomic forecasts by tools to test various scenarios, and this can be an open-ended the MPO. The calibrated network model of all subway, process until a satisfactory service plan is selected. bus, transferring, and walk options in New York City is further calibrated as needed and then modified to reflect MTANYCT is satisfied with the ridership forecasting the long-range plan. A trip assignment model (shortest methods in use and under development, but hopes to make path/stochastic user equilibrium) is run to predict rid- these methodologies faster and easier to use. Needed ership for the long-range plan scenario by route and improvements include the availability and accuracy of input station/stop. Predictions of any significant shifts from or data at the appropriate scale, fewer time-intensive method- to automobile are obtained from the MTA model. ologies, simplification, enhanced accuracy, and flexibility to address a wide variety of situations. The one improvement to forecasting methodology would be to make it easier to apply. Ridership forecasts would be developed under the scenar- ios included in the survey as follows: MTANYCT reports several lessons learned from their experience: Half-mile rerouting of an existing route to serve a new shopping center: ridership forecasts are generally not Neither overly simplistic nor overly complex prepared for this scale of change. approaches work. MTANYCT has tried to make its Extension of an existing route for one mile to serve a model as comprehensive and realistic as possible with- new residential development: ridership forecasts are out getting bogged down in unnecessary details. generally not prepared for this scale of change to the By having a very good representation of existing and bus system. A proposed one-mile westward extension proposed services, the model serves two purposes: (1) of the #7 subway line in Manhattan was analyzed using as a structuring tool that allows service planners to bet- MTANYCT's a.m. peak-hour network assignment ter understand the details of scenarios and interaction model and MTA's regional travel forecasting model. with existing services and (2) for the production of The former uses a stochastic user equilibrium proce- actual ridership forecasts by scenario. dure, whereas the latter uses a Pathfinder procedure AFC (MetroCard) data are a valuable source of current with capacity constraint added. transit usage and transit information, including inferred Change in headway from 12 to 10 min during peak origins and destinations. This overcomes some of the hours: this change is usually too small to model. How- limitations of survey/census-based origin/destination ever, one additional peak-hour train does add capacity. data, particularly their tendency to be out of date. Implementation of a new crosstown route: new Care should be used when applying transit trip rates. A crosstown routes have not been implemented for at least recent study of downtown Brooklyn revealed that tran- the past 10 years. sit trip generation rates typically used for site-specific Implementation of a new mode such as BRT: environmental analysis needed adjustment to produce MTANYCT is treating BRT in similar fashion to accurate results using current data. limited-stop service in its transit trip assignment model An additional factor that emerged in the case study dis- (bus, subway, and walk network); however, for the cussions is the benefit of physical proximity between first time the agency is estimating stop-to-stop origin/ modelers and service planners. Both groups are part of destinations using ridecheck data (see above for a Operations Planning, but being housed in the same description of this process). New or induced transit building has encouraged interaction and in the process travel resulting from the "attractiveness" of BRT will has improved the model. The model as a tool may be be based on careful and realistic quantification of more important than the model as a producer of specific expected time and reliability benefits. These will be results. converted to added riders using elasticity-type methods There is a need for a modified software package that can from the literature and experienced BRT planners and allow smaller systems to conduct the types of analysis operators. done at MTANYCT while being easy to use and Prediction of next year's ridership as part of the budget understand. process: base the forecast on year-to-date ridership trends at the time the forecast is being prepared. The The value of ridership forecasting at MTANYCT can be next year's ridership change applies forecasts of New seen in several ways. Modeling provides a structure for plan- York City employment to the current year's estimate, ning, and this is even truer with complex projects. For exam- with minor adjustments to account for calendar differ- ple, in the ongoing BRT work, modelers need details that ences. Additional changes are made as needed to force service planners to think through their plans in greater account for planned service or fare changes. detail, to the benefit of the plans and the models. The model