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7 transit ridership forecasting tools that are more user-friendly effort presents a framework for forecasting stop-level transit than formal four-step travel models. patronage (27) and also provides a good overview of previ- ous transit modeling efforts. The T-BEST model accounts As part of its Regional Transit Access Plan, the Georgia for network connectivity, temporal and spatial accessibility, Regional Transportation Authority in Atlanta developed a time-of-day variations, and competing and complementary sketch planning tool that produced ridership forecasts for routes through the use of a wide range of socioeconomic and various transit improvement scenarios (22). The flexible service attributes. Results can be aggregated to time period, nature of this tool allowed for an iterative forecasting process day of the week, route segment or route, sub-area, or the in which refinements could be introduced to improve overall entire system from the individual stop level. The model performance and effectiveness. Ridership forecasting using distinguishes between direct and transfer boardings and this sketch planning tool focused on rail, BRT, bus-only therefore can quantify trip-linking and provide a means of lanes, and streetcars running in traffic lanes. analyzing the effects of transfer opportunities on ridership. An earlier version of this model has been documented in the The Maricopa Association of Governments in Phoenix, literature (28) after calibration using data from Jacksonville, Arizona, created the Sketch Plan Model, which estimates Florida. T-BEST is now being applied in Broward County, light rail ridership (23). This model uses a set of trip rate fac- Florida. Florida DOT plans to use the T-BEST model tors developed from other light rail systems in the western statewide for transit ridership forecasting. United States. These factors are based on the number of households and the percentage of regional jobs within a Research related to improved ridership forecasting tech- given distance from a light rail station. Four geographic cat- niques is continuing, as indicated by two papers presented at egories are used for access and egress distances, resulting in the TRB 85th Annual Meeting in January 2006. Lane et al. 16 average trip rate factors. (29) presented a sketch-level ridership forecasting tool for light rail and commuter rail. This model improved on the The North Central Texas Council of Governments has a 1996 TCRP Report 16 (30) by taking into account reverse- transit analysis process integrated within its four-step travel commute trips to employment areas outside the central busi- demand model (24). Its major advantages are that it is sim- ness district and by introducing service-related variables pler and faster than a full model run (4 h versus 12 h) and it such as travel speed, fare, and midday headways. Marshall uses the already available coded travel system. Although and Grady described a sketch transit planning model for the closer to a four-step model than a sketch planning tool, it Washington, D.C., region that supports transit/land use sce- results in faster model runs and is somewhat simpler to use. nario analysis (31). This model better matches suburban tran- sit ridership, is sensitive to land use effects, and is less costly The Knoxville Regional Transportation Planning Organi- to use than the traditional four-step model. Transferability of zation developed the Knoxville Transit Analysis Tool this model to other regions is not clear. (KTAT) as an independent sketch-planning add-on to its regional travel demand model (25). Inputs to KTAT include a traffic analysis zone (TAZ) layer with socioeconomic data SUMMARY and a selection set on a line layer to define the route being tested. KTAT operates in TransCAD to produce an estimate There are other ridership forecasting models. Several transit of ridership per revenue hour based on a regression model agencies have developed models for internal use and might not with population density, mean household income, workers find it worthwhile or cost-effective to publish a report on the per vehicle, and retail employment density as independent subject. The studies cited here provide a good cross section of variables. The independent variables are calculated for a one- work done in this area. The more straightforward approaches quarter mile buffer around the route. The model resulted in exemplified by Mayworm et al. (7) and Pratt and Coople (9) an R-squared value of 0.835. The user guide cautions that the are more user-friendly and are appropriate for ridership fore- ridership per revenue hour is best viewed relative to other casts resulting from small-scale changes. Efforts at the MPO routes and not necessarily as an absolute forecast. However, or state levels to develop simpler and more usable sketch plan- this tool provides a means to test various routes to determine ning tools show promise. Transferability across different met- the most promising alternatives. ropolitan areas has not been established and is an important factor inhibiting widespread use of ridership forecasting mod- CURRENT STUDIES els. T-BEST development in Florida may provide insight into model transferability. A ridership forecasting tool that is still under development is Transit Boardings Estimation and Simulation Tool The intent of this synthesis is not to recommend one (T-BEST) (26), which is a model being developed for the approach over another, but to catalogue the various forecast- Florida Department of Transportation (DOT) Public Transit ing procedures currently used by transit agencies. The fol- Office that works with ArcGIS to simulate travel demand at lowing two chapters describe the results of a survey of more the individual stop level. A resource paper in support of this than 30 transit agencies in the United States and Canada.