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16 CHAPTER FOUR AGENCY ASSESSMENT OF FORECASTING METHODS INTRODUCTION TABLE 26 IS THERE AN OPTIMAL AMOUNT OF DATA NEEDED This is the second of two chapters presenting the results of a FOR RIDERSHIP FORECASTING AND PLANNING? survey of transit agencies regarding ridership forecasting. The Agencies previous chapter addressed the "nuts and bolts" of how agen- No. Agencies Responding Optimal Amount of Data Responding (%) cies forecast ridership. This chapter's focus is on agencies' Yes 23 85 evaluations of their ridership forecasting techniques. Specific Depends on purpose 2 7 topics include data availability and reliability, data accuracy, No 2 7 impacts of technology, agency satisfaction with current Total responding 27 100 methods, potential improvements, and lessons learned. DATA AVAILABILITY AND RELIABILITY value of their forecasting methodologies. Board understand- Several survey questions dealt with data availability and reli- ing and approval was mentioned by 27% of respondents, ability. For data availability, the survey asked if there was an whereas "other" responses included professional judgment optimal amount of data for the agency's forecasting and plan- and "meeting expectations for growth." 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 IMPACTS OF TECHNOLOGY ON FORECASTING METHODOLOGY majority of respondents reported that they do not have this optimal amount of data available (Table 27). No respondent The survey asked if technology has affected the agency's reported having too much data; the problem is inadequate forecasting methodology. Most respondents have seen an data at the desired scale or level. impact from new technologies (Table 32). APCs and farebox upgrades or automated fare collection (AFC) were most fre- The most common concern is availability of ridership data quently mentioned as new technologies that have had an below the route level (by route segment or stop), and many effect. Strictly speaking, these technologies do not affect the agencies anticipate that APC implementation will resolve forecasting methodology itself, but provide more and/or this. Table 28 presents other comments regarding data avail- more accurate input data. Several off-vehicle technologies ability as it relates to ridership forecasting. are also noted in Table 33. Table 29 shows agency satisfaction with the reliability of Table 34 shows the effects of the new technologies. input data. Reliability results are mixed, with 44% of respon- Improvements in data accuracy, reliability, and level of detail dents indicating general but not complete satisfaction. Table all rank highly, along with improved analytical tools. Many 30 summarizes reliability concerns by data type, with the agencies also cite improvements in data availability and inte- greatest reliability concerns related to ridership data. Issues gration of data from different sources. 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 TABLE 27 as APCs. Issues for origin/destination data include timeli- AVAILABILITY OF OPTIMAL AMOUNT ness, quality, and level of detail. Issues for demographic data OF DATA include timeliness and level of detail. Agencies No. Agencies Responding Data Available? Responding (%) MEASURING RELIABILITY AND VALUE Yes 8 26 OF FORECASTING METHODOLOGY Sometimes 6 19 No 17 55 Table 31 shows that 94% of respondents compare actual rid- Total responding 31 100 ership with ridership forecasts to assess the reliability and