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15 Generally a fairly rough estimate based on change in overall ser- Develop ridership trend and develop a target based on demo- vice level plus some adjustment for expected population changes graphic trends and professional judgment. Also work with MPO (regional plan assumed 1.5 percent population increase per and use transit inputs into regional transportation model. year). Our budget estimates have two very separate components. We SUMMARY generally apply an underlying system growth rate that is usually a conservative continuation of the previous year's trend. In this system, that is usually about one percent. We then add in the sep- Analysis of how transit agencies prepare ridership forecast- arate calculation of the impacts of any service changes we have ing for seven scenarios supports and amplifies other survey programmed for the coming year, discounted to allow for a start- responses. The findings included: up period. We normally factor for a three-year start-up curve-- 50% of projected in the first year, 75% in the second, and 90% in the third. A wide variety of data sources are used in ridership forecasting. The most often used data sources include ridership data from the farebox and from recent Scenario G: A 10-Year Ridership Forecast as Part ridechecks, existing and forecast land use, census of a Long-Range Plan demographic data, and origin/destination data from on- board surveys. APCs have made inroads, but are the This scenario shows a split between formal and informal rid- least likely source of ridership data among those listed. ership forecasting techniques (see Table 25). As in Scenario Origin/destination data, although frequently consid- F, the need to consider planned service changes was cited, ered, are not a major component of ridership forecast- and several agencies do not prepare a 10-year forecast. ing for a majority of respondents. The planning department is the most likely home for the Examples of specific responses include: forecasting function within a transit agency. However, Based on service levels, impact of any fare changes, and it is not unusual for multiple departments to be involved growth/loss rate trends from recent years. in different levels of ridership forecasting. Simpler, less formal approaches are used for route-level We would start with this year's ridership and change it as needed and other small-scale service changes. The examples for any planned improvements, service reductions, fare changes, show that some of these "simpler" approaches have or anticipated economic changes based on professional judgment. grown more sophisticated as GIS databases are used to However, we would factor in any model-based projections from our MPO if we are introducing major new service. assess demographic characteristics and identify similar routes and as APCs and ongoing programs improve the accuracy of ridership data. Use of elasticities is widespread for changes to existing TABLE 25 RIDERSHIP FORECASTING FOR SCENARIO G: service, particularly frequency changes. 10-YEAR RIDERSHIP FORECAST More formal methods, including use of the four-step Agencies travel model are used when either the change or the No. Agencies Responding time frame is beyond the scope of the current system; Response Responding (%) for example, introduction of a new mode and forecast- Four-step travel model 16 44 ing over the next 10 years. Trend line 12 33 Service level changes 8 22 The next chapter summarizes agencies' assessments of Would not analyze 5 14 their ridership forecasting methods.