Click for next page ( 31

The National Academies | 500 Fifth St. N.W. | Washington, D.C. 20001
Copyright © National Academy of Sciences. All rights reserved.
Terms of Use and Privacy Statement

Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.

OCR for page 30
30 CHAPTER SIX CONCLUSIONS AND SUGGESTIONS FOR FURTHER STUDY INTRODUCTION Results regarding agency satisfaction with the reliabil- ity of input data are mixed, with 44% of respondents This chapter summarizes the findings, presents conclusions indicating general but not complete satisfaction. The from this synthesis project, and offers recommendations for greatest reliability concerns center on ridership data; further research. Findings from the surveys and particularly however, the timeliness and level of detail for origin/ the case studies provide an assessment of strengths and destination and demographic data are also issues. weaknesses in current methods and likely future directions. The chapter is organized in six sections: METHODOLOGY Data Methodology The literature review provided a good sampling of pre- Organizational Issues vious work related to ridership forecasting. The more Reliability and Accuracy straightforward approaches exemplified by Pratt et al. Lessons Learned and Mayworm et al. are more user-friendly (given Conclusions and Further Research Needs that modeling expertise is not necessarily present at many transit agencies) and are appropriate for rider- At the outset, this study noted the need for ridership ship forecasts resulting from small-scale changes. forecasting methodologies that fall between "back-of-the- Efforts at the metropolitan planning organization (MPO) envelope" methods and a formal four-step travel demand or state level to develop simpler and more usable sketch model. During the process of survey development for this planning tools are promising. Ongoing work with the synthesis, the wide variety of circumstances under which a T-BEST (Transit Boardings Estimation and Simulation ridership forecast may be required became apparent, sup- Tool) model in Florida should provide insight into porting the need for intermediate methodologies. model transferability. Simpler, less formal approaches are used for route-level The conclusions offered here attempt to place these find- and other small-scale service changes. The examples ings in a larger context of how ridership forecasting method- show that some of these "simpler" approaches have ologies are evolving and might continue to evolve at transit grown more sophisticated as geographic information agencies. system (GIS) databases are used to assess demographic characteristics and identify similar routes and as APCs DATA and ongoing programs improve the accuracy of rider- ship data. A wide variety of data sources are used in ridership fore- Use of elasticities is widespread for changes to existing casting. The most often used data sources include rider- service, particularly frequency changes. ship data from the farebox and from recent ridechecks, More formal methods, including the use of the four-step existing and forecast land use, census demographic data, travel model, are used when either the change or the and origin/destination data from on-board surveys. Auto- time frame is beyond the scope of the current system mated passenger counters (APCs) have made inroads but (e.g., introduction of a new mode and forecasting over are still the least likely source of ridership data among the next 10 years). those listed. Origin/destination data, although frequently New technologies have had an effect on agencies' fore- considered, are not a major component of ridership fore- casting methods. APCs and farebox upgrades or auto- casting for a majority of respondents. mated fare collection were most frequently mentioned Most responding agencies do not have the optimal among new technologies; however, several off-vehicle amount of data available for forecasting ridership. The technologies were also noted. Improvements in data most common concern is availability of ridership data accuracy, reliability, and level of detail are among the below the route level (by route segment or stop). Many primary effects of new technologies. Many agencies agencies anticipate that APC implementation will solve also cite improvements in data availability and integra- this issue. tion of data from different sources.