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6 Chapter 10, "Bus Routing and Coverage" (12), uses service study noted that, although a service improvement increases elasticities as the measure of ridership response to service ridership on a given route, it is likely to later cause a ridership expansion and riders per hour or per capita as the measure of decrease on parallel or competing routes. Neither of these ridership response to successful new areawide transit systems. models, although theoretically appealing, has been widely The authors report an average elasticity in the range of 0.6 to adopted by transit agencies. 1.0. First year ridership on new bus systems averages three to five trips per capita or 0.8 to 1.2 riders per bus mile. Service Several transit agencies have attempted to develop rider- restructuring is more difficult to quantify, but several factors ship forecasting procedures. Three procedures that have been contributing to operating efficiencies and ridership growth are published (and there are likely many more that are used inter- reported including high service levels on major routes, consis- nally) were prepared for Lane Transit District (LTD) in tency in scheduling, enhancement of direct travel and ease of Eugene, Oregon; Metropolitan Transit Development Board transferring, quantitative investigation of travel patterns, and (MTDB) in San Diego, California; and the Capital District favorable economic conditions. Among other findings, flexi- Transportation Authority in Albany, New York. These ble service designs such as hub-and-spoke have a slight but not attempts have all taken advantage of GIS programs to iden- universal edge over grid systems. New bus routes take 1 to 3 tify demographic and employment characteristics within years to realize their full ridership potential. walking distance of a given transit route. A study that examined service and fare changes in Europe The LTD route-level ridership forecasting model used found that long-run elasticities (from 3 to 7 years) are larger route ridership rates as the dependent variable and buffer-area than short-term elasticities by a factor of 1.84 (13), although demographics, service levels, and competition from other it is more difficult to isolate changes from a particular action routes as independent variables (19). This effort developed over a long period of observation. separate least-squares regression models for four weekday time periods plus Saturday, and converted the models to elas- ticity form for use in forecasting. Median household income RAIL-ORIENTED STUDIES and vehicle service hours were the only variables to appear in more than one model. LTD is not currently using this model, Several studies published since the 1980s have addressed rid- citing difficulties in obtaining the required input data. ership forecasting for rail systems. Although the FTA's New Starts program mandates ridership forecasts, these forecasts The second effort developed a preliminary ridership are done in the context of a traditional four-step travel model. model for urban bus routes operated by the MTDB in San The Chicago Transit Authority developed a spreadsheet ver- Diego (20). As at LTD, models were developed by time sion of the Chicago Area Transportation Study's mode choice period (three daily time periods); however, an all-day model model on its West Corridor project to forecast ridership in was also developed. This effort found that service-related response to service revisions (14). Model inputs included variables tended to overwhelm demographic and employ- line-haul times and costs and access times and costs. Results ment factors. Also, the model was not transferable to other confirmed the importance of transit access. An example of route types such as feeder routes or community circulators. ridership forecasting for commuter rail included a methodol- ogy based on historical passenger rail travel patterns, origin/ New transit modes can also stimulate ridership forecast- destination surveys, and population for an extension of pas- ing estimates. Capital District Transportation Authority senger train service to San Luis Obispo, California (15). Rail developed ridership projections in the NY5 corridor as part passenger forecasters have also developed a quick-response of a bus rapid transit (BRT) study (21). The forecasting tech- approach using multivariate regression to examine the effect nique involved several steps, including a determination of of station-level variables, including surrounding land use and which trips would be likely to shift to BRT and an assessment service characteristics at a given station, on heavy rail, light of the impacts of headway and travel time changes and other rail, and commuter rail ridership (16). improvements on ridership. Headway elasticities were taken from Pratt and Coople (9), while the travel time elasticity was ROUTE-LEVEL STUDIES taken from Mayworm et al. (7). Other BRT case studies were used to estimate ridership impacts of branding, image, and Two interesting papers addressed the issue of route-level rid- amenity improvements. ership forecasting. Stopher (17) developed a model to predict ridership changes at route and time-of-day levels resulting METROPOLITAN PLANNING from headway changes, route extensions, new routes, route ORGANIZATION STUDIES shortenings, short-lines on existing routes, service span changes, or a combination of actions. Peng et al. (18) proposed Not surprisingly, transit agencies are not the only agencies to a ridership model operating at the route segment level by time prepare ridership forecasts. Several metropolitan planning of day and direction. This model incorporated transit demand, organizations (MPOs), where modeling expertise is gener- supply, and inter-route effects in a simultaneous system. The ally focused, and other regional agencies have developed