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20 CHAPTER FIVE CASE STUDIES INTRODUCTION VIA METROPOLITAN TRANSIT (SAN ANTONIO, TEXAS) Survey results provide a comprehensive overview of the major issues regarding transit ridership forecasting. Follow- One person in the operations planning department has ing a review of these results, six agencies were selected as responsibility for ridership forecasts. The data collection case studies. Personnel directly involved with development process previously relied on a staff of nine checkers with and use of ridership forecasting methodologies agreed to be hand-held devices that could be linked directly to an Access interviewed by telephone. In several cases, more than one database. VIA has acquired 50 APCs for its bus fleet; how- person at an agency either participated in the interviews or ever, the APC data are not viewed as accurate. Ridership reviewed the draft summary of the case study. The case stud- forecasts now use farebox data, available in 30-min incre- ies are intended to provide additional details on innovative ments and summarized by time of day and day of the week. and successful practices. This information is supplemented with origin/destination data gathered by means of an onboard survey every 5 years; also broken down by time of day and day of the week. VIA The selection process for case studies had several criteria: develops a ridership forecast for any type of service change (1) to include transit agencies of various sizes in different other than very minor schedule adjustments as part of its ser- parts of the country, (2) to include a variety of approaches vice revision form. This form is circulated internally up to the and methods related to ridership forecasting, and (3) to select Chief Executive Officer, who at his discretion may share it agencies that could offer useful insights to the transit indus- with board members. Board approval is only required when try as a whole. Nearly 70% of responding agencies offered to initiating new routes and discontinuing current routes. serve as a case study and, as shown by examples from non- case study respondents in chapter three, these agencies VIA considers several factors in developing ridership offered very interesting responses based on their experi- forecasts. System ridership is used for annual forecasts tied ences. The six agencies chosen do not necessarily consider to the budget process. Existing route or route segment rider- themselves as examples of best practices; however, together ship serves as input for any service change. Farebox data can- they provide a representative overview of the state of transit not provide ridership at the route segment level; therefore, ridership forecasting. either APC data are used to estimate the percentage of board- ings along a given segment or checkers are sent out to collect The six case study agencies are: the data. For new routes or added route segments, VIA looks at land use and economic trends within the new service area · VIA (VIA Metropolitan Transit), San Antonio, Texas and relies on analysis of similar routes serving similar areas · RTD (Regional Transportation District), Denver, to forecast ridership. Origin/destination information and Colorado demographic factors are used primarily for service to new · GRTC (Greater Richmond Transit Company) Transit areas. Typically, a new development such as a major employ- System, Richmond, Virginia ment or retail center will generate analysis of a new route or · NYCT (Metropolitan Transit AuthorityNew York a route extension. City Transit), New York, New York · OCTA (Orange County Transportation Authority), The input data are considered reliable; however, VIA Orange, California notes that the analyst needs to understand the limitations and · TriMet (Tri-County Metropolitan Transportation Dis- accuracy of the data in terms of sample size and seasonal trict of Oregon), Portland, Oregon. effects. Ideally, 3 days of ridership data should be available within the past 6 months. This is always the case for farebox The case studies summarize survey responses and inter- data, usually for APC data, and usually not for onboard view observations from each agency. The interviews ridecheck data, especially on weekends. explored issues raised by the survey responses in greater depth and also included a question regarding the value of The forecasting techniques include rules of thumb, similar passenger forecasting as experienced by each agency. route analysis, and professional judgment. A similar route