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FIXED-ROUTE TRANSIT RIDERSHIP FORECASTING AND SERVICE PLANNING METHODS SUMMARY This report documents the state of the practice in fixed-route transit ridership forecasting and service planning. A survey of transit agencies in North America identified methodologies used to analyze the effects of service changes and to forecast ridership. The survey included transit agency assessments of the effectiveness and reliability of their methods and desired improvements. An emphasis was placed on repeatable, timely, and transferable methodolo- gies. Case studies provided additional details on innovative and successful practices. This report will be useful to transit planners and managers as they develop and refine forecasting methodologies for their own agencies. A survey was distributed to 45 selected transit agencies in the United States and Canada. Twenty-five transit agencies were originally identified for inclusion in the survey sample. An additional 15 were randomly selected from the National Transit Database to make the sample more representative in terms of geographic region and system size. Five additional transit agencies, recommended by respondents, were also included bringing the total to 45. A total of 36 agencies completed and returned the survey. Key findings included the following: A wide variety of data sources are used in ridership forecasting. Automated passenger counters (APCs) have become more popular, but are still the least likely source of rid- ership data among those listed. Origin/destination data, although frequently considered, are not a major component of ridership forecasting for a majority of respondents. A majority of responding agencies do not have the optimal amount of data available for forecasting ridership. The most common concern is availability of ridership data below the route level (by route segment or stop). Results regarding agency satisfaction with the reliability of input data are mixed, with 44% of respondents indicating general, but not complete, satisfaction. The greatest reli- ability concerns center on ridership data; however, the timeliness and level of detail for origin/destination and demographic data are also of concern. Simpler, less formal approaches are used for route-level and other small-scale service changes. The examples show that some of these "simpler" approaches have grown more sophisticated as geographic information system databases are used to assess demo- graphic characteristics and identify similar routes, and as APCs and ongoing programs improve the accuracy of ridership data. More formal methods, including use of the four- step travel model, are used when either the change or the time frame is beyond the scope of the current system. The planning department is the most likely home within a transit agency for the fore- casting function. However, it is not unusual for multiple departments to be involved in different levels of ridership forecasting. Responsibility for ridership forecasting is more likely to be part of general duties for all but major changes. Roughly one-third of responding agencies are satisfied with their current forecast- ing methods, one-third are partially satisfied, and one-third are not satisfied. The quality and availability of input data and accuracy of the forecasts are the most press- ing concerns.

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2 Input data and methodology were the most frequently mentioned aspects of ridership forecasting procedures that transit agencies would like to change. Agencies reported a need for greater data availability, more current data, and more detailed data. Method- ology needs were more diverse, because various agencies are at different stages regard- ing forecasting methods. The most commonly mentioned lessons learned included the need to interpret results carefully and simplify the approach to ridership forecasting. Major conclusions included the following: Qualitative forecasting techniques relying on professional judgment and experience continue to be widely used by transit agencies, especially for small-scale and near-term changes. Some consider these too subjective and too dependent on the skill of the ana- lyst. Examples cited throughout this synthesis demonstrate that qualitative procedures can involve consideration of a wide variety of factors, often directed toward identify- ing similar circumstances elsewhere in the transit system that can provide guidance for likely ridership response. Use of service and headway elasticities is widespread among transit agencies. Broad-based studies such as TCRP Report 95: Traveler Response to Transportation System Changes are very useful in providing information on "typical" elasticities; how- ever, several agencies emphasized the need to adapt these to their service areas using their own experiences. Formal travel modeling expertise is found at the metropolitan planning organization (MPO), not usually at the transit agency. The literature review noted that several MPOs are actively engaged in development of forecasting methodologies at a more appropri- ate scale for transit needs than the traditional four-step travel model. At the same time, widespread use of new technologies such as geographic information systems and APCs allow transit agencies to develop more sophisticated ridership forecasting tools. These developments suggest the possibility of convergence in the near future. Transit agencies reported that a strong ongoing working relationship with their MPOs is beneficial to both parties. Modelers and transit planners often work in different time frames and geographic scales, and ongoing communication helps to bridge these gaps. Transit agencies reported value in ridership forecasting methodologies. Several noted that ridership forecasts provide a basis for prioritizing among competing proposals and, more generally, for decision making at the senior management and board levels. Inter- nally, ridership forecasting can encourage discipline in the service planning process, particularly where there is ongoing interaction between modelers and service planners. This interaction can also result in improved methodologies. Sound ridership forecast- ing methodologies can also enhance a transit agency's credibility among stakeholders and peer local and regional agencies. At many agencies, forecasting is more of an art than a science and is likely to remain so in the near future. However, new technologies that provide more accurate ridership data and enhance the ability to summarize demographic and socioeconomic data at an appropriate level of detail are fostering continued development of ridership forecasting techniques and increasing the confidence level in forecasting results.