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9 TABLE 5 TABLE 7 FACTORS CONSIDERED AS INPUTS TO FORECASTING DATA SOURCES FOR RIDERSHIP FORECASTS METHODOLOGY Agencies Agencies No. Agencies Responding No. Agencies Responding Data Sources Responding (%) Factors Responding (%) Ridership data from the farebox 30 86 Existing route or route segment ridership 31 89 Ridership data from recent ridechecks 28 80 Ridership on similar routes 30 86 Existing land use 25 71 Existing system ridership 28 80 Census demographic data 23 66 Demographic factors in the service area 27 77 Origin/destination data from on-board 22 63 Land use within the affected service area 25 71 surveys Origin/destination information 24 69 Forecast land use 19 54 Economic trends within the service area 21 60 Ridership data from APCs 14 40 Other 10 29 Origin/destination data from models 14 40 Total responding 35 100 Economic forecasts 11 31 Economic trends 10 29 CTPP demographic data 9 26 Table 5 reveals that agencies use a wide variety of factors Other 11 31 Total responding 35 100 as inputs to ridership forecasts. Ridership factors are most fre- quently mentioned, followed by demographic characteristics, APC = automated passenger counter; CTPP = Census Transportation land use, origin/destination data, and economic trends. Several Planning Package. factors were mentioned in the "other" category, including travel time, fares, congestion levels, automobile ownership, of ridership forecasting (29%). Nearly one-quarter of respond- land use changes, and market research survey results. ing agencies (23%) do not consider origin/destination data. If a factor is involved for some types of changes or fore- The findings in this section suggest that a wide variety of casts but not others, the agencies indicated this on the survey. data sources are used in ridership forecasting. Certain sources Roughly half of the agencies noted the type of change or may be very important across all forecasts, whereas others forecast for which a particular factor is used. Table 6 high- may be useful only for particular types of forecast. Subsequent lights the primary uses for each factor. sections explore how data are used in forecasting procedures. The most often used data sources include ridership data from the farebox and from recent ridechecks, existing and ANALYTICAL TECHNIQUES forecast land use, census demographic data, and origin/ destination data from on-board surveys, as shown in Table 7. Ridership forecasting techniques can differ by mode, time APCs have made inroads and are now used at 40% of frame, and scope of the change being analyzed. This section responding agencies; however, APCs are still the least likely presents agency responses regarding analytical techniques source of ridership data among those listed. In the "other" cat- used to forecast ridership. egory, three agencies mentioned household travel surveys. Most agencies use more than one method of forecasting The use of origin/destination data was of particular interest, ridership, depending on the scope of the change and the pur- and a question addressing this was included on the survey. pose of the forecast (Table 9). The majority of responding As Table 8 shows, the most common response was that origin/ agencies use different forecasting methods for long-range and destination data are considered (43%), but are not a major part short-range forecasts (Table 10). Interestingly, multimodal agencies are slightly more likely to use the same methodol- ogy for all modes (Table 11). TABLE 6 PRIMARY USES FOR INPUT FACTORS Factors Primary Use TABLE 8 Existing route or route segment ridership Change in route ROLE OF ORIGIN/DESTINATION DATA IN RIDERSHIP Ridership on similar routes New route or corridor FORECASTING Change in route Agencies Existing system ridership Annual budget forecast No. Agencies Responding Long-range plan Role Responding (%) Demographic factors in the service area Change in route Considered, but not a major part 15 43 Land use within the affected service area Change in route Major part 10 29 New route or corridor Origin/destination information Major new service Not considered 8 23 Four-step travel model Depends on time frame/level of detail 2 6 Economic trends within the service area No consensus Total responding 35 100

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10 TABLE 9 TABLE 12 SINGLE VERSUS MULTIPLE METHODS OF FORECASTING TECHNIQUES USED BY TRANSIT AGENCIES RIDERSHIP FORECASTING Agencies Agencies No. Agencies Responding No. Agencies Responding Forecasting Technique Responding (%) Methods Responding (%) Professional judgment 29 83 Multiple 23 66 Rules of thumb/similar routes 28 80 Single 12 34 Service elasticities 22 63 Total responding 35 100 Four-step travel demand model 18 51 Econometric model 7 20 Regression analysis 7 20 TABLE 10 FORECASTING METHODS: SHORT-RANGE Other 7 20 VERSUS LONG-RANGE FORECASTS Total responding 35 100 Agencies No. Agencies Responding Methods Responding (%) Different by time frame 22 71 centers, high-density residential development, or the general Same by time frame 9 29 character of the neighborhoods along a particular route. Total responding 31 100 If a technique is used for only certain types of changes or forecasts, the agencies indicated this on the survey. Roughly half of the agencies noted the type of change or forecast for The most frequently used techniques are qualitative in which a particular technique is used. Table 13 highlights the nature: professional judgment and rules of thumb/similar primary uses for each factor. Professional judgment and rules routes. At least half of responding agencies use elasticities of thumb/similar routes are most often used for route, ser- and a traditional travel demand model to forecast ridership. vice, and schedule changes. Service elasticities are used for Table 12 shows techniques included in agency methodology. these types of changes as well, whereas fare elasticities are Three of the "other" agency responses reflect trend line analy- used for fare changes. The four-step travel model is used sis; others mentioned Institute of Transportation Engineers most often for major new service. trip generation rates, GIS, and an unspecified agency model. Several agencies reported using a range of service elastici- It may be useful to distinguish among the qualitative tech- ties, as suggested in national studies (9,11). Service elasticities niques in Table 12, because they are referred to later in this were different depending on existing service frequency, service chapter. "Similar routes" forecasts ridership on a given route area density, time of day, or analyst judgment. For agencies based on the experiences on other routes with similar service reporting a single service elasticity value, this value was as low areas and frequencies. An analyst might base a ridership as 0.2 (in New York City) and as high as 0.5. Reported fare forecast for a new crosstown route on the productivity of elasticities varied from 0.175 to 0.35. other crosstown routes or develop a ridership estimate for an extension of a route to a mall based on ridership on other Calculating ridership using passenger boardings (unlinked routes at similar malls. "Rules of thumb" codify past experi- ridership) is common among transit agencies. Unlinked rid- ence in general rules. Examples can take the form of "new ership is the reporting standard for the NTD, and APCs and routes generate x riders per revenue hour," or "route exten- fareboxes register boardings. For modeling purposes, knowl- sions to suburban residential developments generate y riders edge of the number of linked trips is often desirable. Table 14 per 100 households." "Professional judgment" relies on the shows that transit agencies are much more likely to forecast judgment and experience of the analyst and is the most sub- ridership in terms of unlinked trips than linked trips. jective qualitative technique. For example, an analyst might use professional judgment to adjust a ridership estimate developed by means of another technique upward or down- TABLE 13 ward depending on the presence or absence of schools, retail PRIMARY USES FOR FORECASTING TECHNIQUES Forecasting Technique Primary Use Professional judgment Any route or service change TABLE 11 Rules of thumb/similar routes Any route or service change FORECASTING METHODS: MULTIMODAL Change in route AGENCIES Headway/schedule change Agencies Elasticities Any route or service change No. Agencies Responding Fare changes Methods Responding (%) Headway/schedule change Different by mode 9 45 Four-step travel demand model Major new service Same by mode 11 55 Econometric model No consensus Total responding 20 100 Regression analysis No consensus