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Suggested Citation:"Chapter One - Introduction." National Academies of Sciences, Engineering, and Medicine. 2006. Fixed-Route Transit Ridership Forecasting and Service Planning Methods. Washington, DC: The National Academies Press. doi: 10.17226/14001.
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Suggested Citation:"Chapter One - Introduction." National Academies of Sciences, Engineering, and Medicine. 2006. Fixed-Route Transit Ridership Forecasting and Service Planning Methods. Washington, DC: The National Academies Press. doi: 10.17226/14001.
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3BACKGROUND Traditional transit ridership forecasting relies on a four-step travel demand forecasting model. This model has tradition- ally been used to compare and identify major travel invest- ment policy choices. Because the traditional forecasting process is data and labor intensive, transit agencies have developed and applied other methods for transit demand forecasting and service planning. Some methods may be used to estimate system- wide ridership for budgeting purposes and others to estimate the ridership impacts of new or revised services. These meth- ods vary according to: • Geographic scale (from a single route or route segment to the entire system), • Scale of the service change (from a minor schedule adjustment to a major system restructuring), and • Time frame for the ridership forecast (from one day to 10 years). Given the wide variation in the purposes of a ridership forecast, it is not surprising that most transit agencies have not developed a single formal methodology. From the broader transit industry perspective, the transferability of a particular methodology to other transit agencies is uncertain. The end result is a widespread reliance on “back of the enve- lope” (improvised) methods, the accuracy of which depends on the knowledge and experience of the individual(s). Infor- mation on ridership forecasting approaches that bridges the gap between the back of the envelope and the four-step travel demand model would be very useful for transit agencies. Technological changes have affected the forecasting process. One example is the increasing use of automated pas- senger counters (APCs) that enhance the quantity, reliability, and level of detail of ridership data. Another is the prevalence of geographic information system (GIS) tools that greatly simplify the process of summarizing demographic and employment data and relate these spatially to transit routes or route segments. METHODOLOGY This synthesis included a literature review, a survey of tran- sit agencies, and telephone interviews with six agencies selected as case studies. A Transportation Research Infor- mation Services (TRIS) search was conducted to aid the lit- erature review. In addition, a message was posted on the Travel Model Improvement Program (TMIP) e-mail list (see http://tmip.tamu.edu/email_list for additional information) describing the synthesis project and requesting assistance. Several respondents suggested studies for inclusion in the lit- erature review. The survey on transit ridership forecasting was designed to elicit information on methodologies in use in a variety of sit- uations, satisfaction with these methods, and suggestions for improvements. A survey was sent to 45 selected transit agen- cies in the United States and Canada. Each agency was con- tacted by e-mail or telephone before the surveys were sent to ascertain interest and identify the correct recipient. Follow-up e-mails and telephone calls were placed approximately 6 and 10 weeks after the original survey to encourage responses. The selection of agencies for the sample was guided by the existence of ongoing ridership forecasting activities, partici- pation in similar studies, random selection of additional agen- cies to ensure adequate representation by size and location, and recommendations from other transit agencies. Initially, 25 transit agencies were identified for inclusion in the sample. An additional 15 agencies were randomly selected from the National Transit Database (NTD) to make the sample more representative in terms of geographic region and system size. At least 5% of agencies in each FTA district were included in the sample. Finally, respondents recommended five addi- tional agencies for inclusion in the sample, bringing the total to 45 transit agencies. Thirty-six agencies completed the sur- veys, a response rate of 80%. Table 1 presents the distribution of responding agencies by size. Table 2 shows the distribution of responding agencies by FTA region. Figure 1 is a map of FTA regions. ORGANIZATION OF REPORT Following this introductory chapter, chapter two summarizes the findings of the literature review. Chapter three, the first of two chapters to present the results of the survey, focuses on forecasting methodologies, resource requirements, data inputs, and organizational issues. In the process of survey CHAPTER ONE INTRODUCTION

development, the wide variety of circumstances that could generate the need for a ridership forecast became apparent. To address this issue, the survey provided seven specific scenarios and asked how the agency would forecast rider- 4 ship under each scenario. This chapter includes agency responses. Chapter four discusses the responding agencies’ assess- ment of their own forecasting methods. This chapter summa- rizes perceptions of data reliability and accuracy, satisfaction with current methodologies, desired improvements, lessons learned, and advice for other transit agencies. Chapter five reports detailed findings from each of the six case studies. Agencies were selected for the case studies for a variety of reasons. Some approaches can be characterized as “best practices.” One case study presented a setting in which forecasting methodologies are not considered to be necessary. All six show a thoughtful response to the issues posed by ridership forecasting. Chapter six summarizes the findings, presents conclusions from this synthesis project, and offers suggestions for further research. Findings from the surveys and particularly the case studies provide an assessment of strengths and weaknesses in current methods and likely future directions. FTA Region No. of Agencies Responding I 2 II 5 III 2 IV 4 V 3 VI 3 VII 2 VIII 1 IX 9 X 3 Canada 2 Total 36 TABLE 2 SAMPLE AND RESPONDING TRANSIT AGENCIES BY FTA REGION FIGURE 1 Map of FTA regions. No. of Vehicles Op erat ed in Maximum Se rvice No. of Agencies Responding 1–50 5 51–100 4 101–250 11 251–500 3 501+ 13 To ta l 36 TABLE 1 SAMPLE AND RESPONDING TRANSIT AGENCIES BY SIZE

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TRB's Transit Cooperative Research Program (TCRP) Synthesis 66: Fixed-Route Transit Ridership Forecasting and Service Planning Methods examines the state of the practice in fixed-route transit ridership forecasting and service planning. The report also explores forecasting methodologies, resource requirements, data inputs, and organizational issues. In addition, the report analyzes the impacts of service changes and reviews transit agency assessments of the effectiveness and reliability of their methods and of desired improvements.

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