National Academies Press: OpenBook

Fixed-Route Transit Ridership Forecasting and Service Planning Methods (2006)

Chapter: Chapter Six - Conclusions and Suggestions for Further Study

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Suggested Citation:"Chapter Six - Conclusions and Suggestions for Further Study." 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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Page 31
Suggested Citation:"Chapter Six - Conclusions and Suggestions for Further Study." 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.
×
Page 31
Page 32
Suggested Citation:"Chapter Six - Conclusions and Suggestions for Further Study." 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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Page 32

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

31 ORGANIZATIONAL ISSUES • The planning department is the most likely home for the forecasting function within a transit agency. 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. • A range of estimates were given for the time and effort required to prepare ridership forecasts. Simple or short- range forecasts can generally be completed in 3 days or less. A wide time range in long-range forecasts reflects the method used; a trend line analysis takes much less time than a four-step model run. • Ridership forecasts are nearly always distributed and used internally. A majority of responding agencies also share the forecasts with their boards. RELIABILITY AND ACCURACY • Nearly all agencies measure the reliability and value of their forecasting methodologies through a comparison of actual ridership with ridership forecasts. Board understanding and approval is also a factor for 27% of respondents. • The question regarding satisfaction with current fore- casting methods yielded interesting results. Roughly one-third of responding agencies are satisfied, one-third are partially satisfied, and one-third are not satisfied with current forecasting methods. Quality and avail- ability of input data and accuracy of the forecasts are the most pressing concerns. • 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 cur- rent data, and data at a finer level. Methodology needs were more diverse, because various agencies are at dif- ferent stages regarding forecasting methods. Among the specific responses were greater sophistication, more consistency, and easier to apply models. LESSONS LEARNED AND CASE STUDY RESULTS • Approximately half of all survey respondents shared lessons learned from the process of developing and using ridership forecasting methodologies. The most commonly mentioned included interpreting results cau- tiously and simplifying the approach to ridership fore- casting. Responding agencies made several other important and useful observations. • Each of the case study agencies was very different in terms of approach to ridership forecasting, response to local issues and concerns, and use of various methods and techniques. All showed a thoughtful response to the issues posed by ridership forecasting. • The VIA Metropolitan Transit (San Antonio) case study provides an example of a traditional approach that relies heavily on professional judgment and an under- standing gained through experience of the factors con- tributing to transit ridership. • The Regional Transit District (RTD) (Denver) case study shows how new technologies such as APCs, inte- grated software, and GIS can improve the quantity and quality of ridership and other data, provide new meth- ods for analyzing and forecasting ridership, and greatly enhance its ability to communicate results to stake- holders. At the same time, RTD relies on research proj- ects such as TCRP Report 95 to provide invaluable documentation of experience elsewhere. • The Greater Richmond Transit Company (GRTC) case study shows that there may not be a real need for a rider- ship forecasting methodology at all transit agencies. The decision-making process at many small and mid-sized agencies is driven more by politics and funding availabil- ity than by ridership analysis. Although many agencies can see the value of employing a forecasting methodol- ogy, it may not rank highly in terms of current needs. • The Metropolitan Transit Authority–New York City Transit (MTA–NYC) case study shows how application of new data collection techniques (automated fare col- lection) and GIS analytical tools can improve ridership forecasting procedures. Successful exploration of new analytical methods (such as inferred origins and desti- nations) as ridership data become more reliable is an important finding that can be applied elsewhere. Encouraging interaction between modelers and end- users through organizational structure and location of the departments can ultimately result in model improvements and greatly increases the likelihood of its being trusted and used on a consistent basis. • The Orange County Transportation Authority (OCTA) case study indicates that GIS programs, formal model- ing efforts, use of elasticities, and professional judgment can together provide a menu of ridership forecasting methodologies for use as appropriate. The various departments that require ridership forecasts are com- fortable with the methodologies and confident in the results. Additional work is ongoing to enhance accuracy and simplify the use of these methodologies; however, OCTA has achieved a high level of confidence in its rid- ership forecasts in a wide variety of situations. • The Tri-County Metropolitan District of Oregon (TriMet) case study provides an example of a ridership forecasting model in use at a transit agency. It is note- worthy that TriMet’s first choice of methodology for incremental service changes is similar-route analysis, but the model is useful in addressing unique situations. TriMet also relies heavily on service headway elastici- ties to assess the impact of changes in frequency. TriMet believes that its model and approach could be used at other transit agencies once calibrated with that agency’s ridership data.

CONCLUSIONS AND FURTHER RESEARCH NEEDS • Qualitative forecasting techniques relying on profes- sional 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 analyst. Examples cited throughout this synthesis demonstrate that “qual- itative” does not equal “simplistic.” Qualitative proce- dures can involve consideration of a wide variety of factors, often geared toward identifying similar cir- cumstances 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 are very useful in providing informa- tion on “typical” elasticities; however, several agencies have emphasized the need to adapt these to their service areas using their own experiences. • Formal travel modeling expertise is found at the MPO, not usually at the transit agency. The literature review noted that several MPOs are actively engaged in the development of forecasting methodologies at a more appropriate scale for transit needs than the traditional four-step travel model. At the same time, widespread use of new technologies such as GIS and APCs allow transit agencies to develop more sophisticated ridership forecasting tools. These developments suggest the pos- sibility of convergence in the near future. • Transit agencies reported that a strong, ongoing work- ing relationship with their MPOs is beneficial to both parties. Modelers and transit planners often work in dif- ferent time frames and geographic scales, and ongoing communication helps to bridge these gaps. The New York City case study findings emphasize the benefits of interaction between modelers and planners within large transit agencies. • Transit agencies reported value in ridership forecasting methodologies. Several noted that ridership forecasts provide a basis for prioritizing among competing pro- posals and, more generally, for decision making at the senior management and board levels. Internally, rider- ship 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 method- ologies. Sound ridership forecasting methodologies can also enhance a transit agency’s credibility among stakeholders and peer local and regional agencies. • Does the state of the art in transit ridership forecasting justify the high value that transit agencies place on this function? 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 32 ridership forecasting techniques and are increasing the confidence level in forecasting results. There will always be a role for professional judgment and experience, par- ticularly in understanding the underlying factors affect- ing ridership behavior. The continued integration of rid- ership, service, demographic, and other data will provide new tools to assist in this understanding. Findings from this synthesis suggest five major research needs: 1. Transferability of ridership forecasting methodologies. How well does a methodology developed at one transit agency work at another agency? Calibration to local con- ditions is a given; however, how extensive is the needed calibration and how accurate are the resulting forecasts? Ongoing work with the T-BEST model in Florida, spon- sored by the Florida Department of Transportation, has as one of its purposes calibration and use of this model at all transit agencies within the state, and should offer interesting findings regarding transferability. 2. GIS applications in ridership forecasting. The use of GIS by transit agencies continues to increase. Although many GIS applications are oriented toward simple mapping functions, the true value of GIS in transit may be as a data integration platform that sim- plifies data management. Additional research in this area should have a positive return. 3. Easy-to-use methodologies. As previous experience has shown, forecasting procedures relying on data that are not readily available to transit agencies are unlikely to be used. User acceptance should be a primary focus of future research efforts in this field. 4. Implementation of new technologies. Transit agen- cies in the process of acquiring APC systems antici- pate that the use of APCs will solve problems with the availability of ridership data at the route segment or stop level. However, APC implementation has not always been successful. Several agencies, including VIA and OCTA among the case studies, have expe- rienced problems in obtaining usable data from APCs and/or in convincing all departments within the agency that APC data are equally or more reli- able than farebox or manually collected data. Other agencies, including RTD and TriMet among the case studies, are very confident in and rely extensively on their APC data. Future research into factors affect- ing successful implementation would be useful not only in relation to APCs but also for the variety of ITS applications that will come on line in the near future. 5. The need for cost-effective and reliable data collection efforts. Quality and availability of input data continue to be among the primary concerns of transit agencies. Research geared toward reliable data collection at the appropriate level and at an affordable price could have enormous practical value.

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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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