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Optimal Amount of Data No. Agencies Responding Agencies Responding (%) Yes 23 85 Depends on purpose 2 7 No 2 7 Total responding 27 100 TABLE 26 IS THERE AN OPTIMAL AMOUNT OF DATA NEEDED FOR RIDERSHIP FORECASTING AND PLANNING? INTRODUCTION This is the second of two chapters presenting the results of a survey of transit agencies regarding ridership forecasting. The previous chapter addressed the ânuts and boltsâ of how agen- cies forecast ridership. This chapterâs focus is on agenciesâ evaluations of their ridership forecasting techniques. Specific topics include data availability and reliability, data accuracy, impacts of technology, agency satisfaction with current methods, potential improvements, and lessons learned. DATA AVAILABILITY AND RELIABILITY Several survey questions dealt with data availability and reli- ability. For data availability, the survey asked if there was an optimal amount of data for the agencyâs forecasting and plan- ning process, and if that data are available. Table 26 shows that 85% of respondents believed that an optimal amount of data should be available for forecasting and planning. A majority of respondents reported that they do not have this optimal amount of data available (Table 27). No respondent reported having too much data; the problem is inadequate data at the desired scale or level. The most common concern is availability of ridership data below the route level (by route segment or stop), and many agencies anticipate that APC implementation will resolve this. Table 28 presents other comments regarding data avail- ability as it relates to ridership forecasting. Table 29 shows agency satisfaction with the reliability of input data. Reliability results are mixed, with 44% of respon- dents indicating general but not complete satisfaction. Table 30 summarizes reliability concerns by data type, with the greatest reliability concerns related to ridership data. Issues for ridership data include the accuracy of a limited number of manually collected samples, reliability of farebox data, and debugging issues associated with new technologies such as APCs. Issues for origin/destination data include timeli- ness, quality, and level of detail. Issues for demographic data include timeliness and level of detail. MEASURING RELIABILITY AND VALUE OF FORECASTING METHODOLOGY Table 31 shows that 94% of respondents compare actual rid- ership with ridership forecasts to assess the reliability and 16 value of their forecasting methodologies. Board understand- ing and approval was mentioned by 27% of respondents, whereas âotherâ responses included professional judgment and âmeeting expectations for growth.â IMPACTS OF TECHNOLOGY ON FORECASTING METHODOLOGY The survey asked if technology has affected the agencyâs forecasting methodology. Most respondents have seen an impact from new technologies (Table 32). APCs and farebox upgrades or automated fare collection (AFC) were most fre- quently mentioned as new technologies that have had an effect. Strictly speaking, these technologies do not affect the forecasting methodology itself, but provide more and/or more accurate input data. Several off-vehicle technologies are also noted in Table 33. Table 34 shows the effects of the new technologies. Improvements in data accuracy, reliability, and level of detail all rank highly, along with improved analytical tools. Many agencies also cite improvements in data availability and inte- gration of data from different sources. CHAPTER FOUR AGENCY ASSESSMENT OF FORECASTING METHODS Data Available? No. Agencies Responding Agencies Responding (%) Yes 8 26 Sometimes 6 19 No 17 55 Total responding 31 100 TABLE 27 AVAILABILITY OF OPTIMAL AMOUNT OF DATA
17 Issue No. Agencies Responding Agencies Responding (%) Ridership data at route segment or stop level 10 59 On-board data collected infrequently/expensive to collect 3 18 No access to GIS data/demographic data at stop level 2 12 Question of priorities/balance 2 12 Rail data for new lines 2 12 Better farebox/APC data 2 12 Total responding 17 100 GIS = geographic information system; APC = automatic passenger counter. Input Data Reliability No. Agencies Responding Agencies Responding (%) Satisfied 14 41 Somewhat satisfied 15 44 Not satisfied 5 15 Total responding 34 100 Input Data No. Agencies with Concerns Agencies Responding (%) Ridership 13 65 Origin/destination 5 25 Demographic 5 25 General 5 25 Total responding 20 100 Method No. Agencies Responding Agencies Responding (%) Comparison of actual and projected ridership 31 94 Board understanding and approval 9 27 Other 2 6 Total responding 33 100 Technology Effects? No. Agencies Responding Agencies Responding (%) Yes 22 63 No 13 37 Total responding 35 100 Technology No. Agencies Responding Agencies Responding (%) APC 10 56 Farebox upgrade/Automated Fare Collection 5 28 Travel model upgrade/new appl ication 4 22 GIS 4 22 Improved personal computers/software 3 17 AVL/GPS 2 11 Data integration software 1 6 Total responding 18 100 APC = automatic passenger counter; GIS = geographic information system; AVL = automatic vehicle location; GPS = global positioning system. Effect No. Agencies Responding Agencies Responding (%) Data reliability/accuracy 7 30 Level of detail in data 7 30 Improved analytical tools 7 30 Data availability 6 26 Data integration from different sources 4 17 Origin/destination estimation possible 3 13 Faster analysis time 3 13 Better reporting 2 9 Total responding 23 100 TABLE 28 DATA AVAILABILITY ISSUES TABLE 29 SATISFACTION WITH RELIABILITY OF INPUT DATA TABLE 30 RELIABILITY CONCERNS BY DATA TYPE TABLE 32 EFFECTS OF TECHNOLOGY ON RIDERSHIP FORECASTING TABLE 31 MEASURING RELIABILITY AND VALUE OF FORECASTING METHODOLOGY TABLE 33 SPECIFIC TECHNOLOGICAL CHANGES AFFECTING RIDERSHIP FORECASTING TABLE 34 HOW TECHNOLOGIES HAVE AFFECTED FORECASTING METHODOLOGIES SATISFACTION WITH RIDERSHIP FORECASTING Table 35 shows transit agency satisfaction with current rid- ership forecasting methods. Responses to this open-ended question are distributed very evenly across the spectrum. Most respondents see a need for improvements to their cur- rent procedures. Table 36 shows the types of improvements envisioned by respondents. Quality and availability of input data and accu- racy of the forecasts are the most pressing concerns. Among other desired improvements were an automated short-range forecasting procedure, incorporation of TCRP Report 95 into service guidelines, a greater commitment to high-quality input data throughout the region, and changes in FTA proce- dures for non-rail New Starts. Respondents were asked, âIf you could change one aspect of your agencyâs ridership forecasting methodology, what would you change?â Unlike the question regarding types
18 Level of Satisfaction No. Agencies Responding Agencies Responding (%) Satisfied 11 31 Partially satisfied 12 34 Not satisfied 12 34 Total responding 35 100 TABLE 35 SATISFACTION WITH CURRENT RIDERSHIP FORECASTING METHODS Improvement No. Agencies Responding Agencies Responding (%) Availability and/or accuracy of input data at the appropriate scale 22 81 Accuracy of the results 16 59 Inclusion of more predictive variables 11 41 Less time-intensive methodology 11 41 Flexibility to address a wider variety of situations 11 41 Simplification of the procedures 8 30 Other 7 26 Total responding 27 100 TABLE 36 DESIRED IMPROVEMENTS TO RIDERSHIP FORECASTING METHODS Improvement No. Agencies Responding Agencies Responding (%) Input data 11 44 Methodology 10 40 Approaches 3 12 In-house staff expertise/understanding 2 8 Linkages (GIS, regional indicators) 2 8 Total responding 26 100 GIS = geographic information system. TABLE 37 ONE IMPROVEMENT TO RIDERSHIP FORECASTING METHODOLOGY Lessons Learned No. Agencies Responding Agencies Responding (%) Caution regarding results 7 37 Simplify the approach 4 21 Caution regarding data and applications 4 21 Communication and partnering 2 11 Develop local factors 2 11 Simplify the model 2 11 Other 7 37 Total responding 19 100 TABLE 38 LESSONS LEARNED ⢠Simplify the approachâfocus on one or two tools for synergy and absence of conflicting forecasts; trend forecasting and professional judgment can be as accu- rate as regression and econometric models; in-house expertise is more effective and less expensive than consultants. ⢠Caution regarding data and applicationâunderstand the limits of the data being used; use trip generation rates with careâthese may not apply across the metropolitan area; use caution in applying regional model outputs at a different scale (e.g., route or station level); AFC data overcome limitations of survey/census-based origin/ destination data, particularly the out-of-date issue. ⢠Communication and partnershipâinform and cooper- ate with other local agencies (such as the MPO) and peers within the transit industry. ⢠Develop local factorsâforecast models from external sources do not work well. They are complicated, time- intensive, data-intensive, and provide inferior results; local elasticities preferred over industry; use experience and results from the past. ⢠Simplify the modelâcar ownership and income do not provide enough improvement to warrant the time and difficulty in acquiring the data at the appropriate scale. ⢠Other: â Smaller versus larger agencies: for smaller agencies, trip rates and population and employment numbers can suffice; for larger agencies, network impacts are importantâevaluate impacts on systemwide basis. of improvements desired, this question was open ended. Table 37 summarizes the results. Improvements to input data and methodology were most frequently mentioned. There is a need for greater data availability, more current data, and data at a more detailed level. Methodology needs were more diverse, reflecting that various agencies are at different stages regarding forecasting methods. Among the specific responses were greater sophistication, more consistency, and easier to apply models. âApproachesâ is a catch-all category that includes adopting written guidelines, basing ridership forecasting on industry standards and best practices, and allowing alternate specific constants in FTA procedures. LESSONS LEARNED Roughly half of all survey respondents shared lessons learned from the process of developing and using ridership forecasting methodologies. The lessons learned can be grouped into seven broad categories, as shown in Table 38. Responses are summarized by category below. ⢠Caution regarding resultsâbe realistic in ridership esti- mates; use a range and confidence levelâspecific pre- dictions are almost always wrong; review model results with peers, other corridors, and elasticities; temper with experience; a full understanding of current ridership behavior is critical for forecasting.
19 â Neither overly simple nor overly complex approaches work. â GIS as data integration tool simplifies data manage- ment. â Transferability: Institute of Transportation Engineers trip generation rates are very accurate; our mode split is very similar across our service area. â Take the time to develop patronage forecasts. â Interpretation and presentation in lay terms is as important as the forecasts themselves. â Admitting that forecasts were wrong and finding out why is the best teacher. SUMMARY This chapter has described agency assessments of ridership forecasting methods. Findings include: ⢠Results regarding agency satisfaction with the reliability of input data are mixed, with 44% of respondents indi- cating 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. ⢠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. ⢠A majority of responding agencies do not have the opti- mal 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), and many agencies anticipate that APC implementation will solve this. ⢠New technologies have had an effect on agenciesâ fore- casting methods. APCs and farebox upgrades or auto- mated fare collection were most frequently mentioned among new technologies, but several off-vehicle tech- nologies were also noted. Improvements in data accu- racy, reliability, and level of detail are among the primary effects of new technologies. Many agencies also cite improvements in data availability and integration of data from different sources. ⢠The question regarding satisfaction with current fore- casting methods yielded very 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 availability 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 report a need for greater data availability, more current data, and data at a more detailed level. Methodology needs were more diverse, reflecting that various agencies are at different stages regarding forecasting methods. Among the specific responses were greater sophistica- tion, more consistency, and easier to apply models. ⢠Roughly half of all survey respondents shared lessons learned from the process of developing and using rider- ship forecasting methodologies. The most commonly mentioned lessons included interpreting results cau- tiously and simplifying the approach to ridership fore- casting. Responding agencies made several other important and useful observations. The following chapter describes findings from six case studies that explore issues related to ridership forecasting in greater detail.