Click for next page ( 27

The National Academies | 500 Fifth St. N.W. | Washington, D.C. 20001
Copyright © National Academy of Sciences. All rights reserved.
Terms of Use and Privacy Statement

Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.

OCR for page 26
26 TABLE 39 OCTA FORECASTING METHODS Purpose Method Time Frame Geography Budgeting Trend line, group Annual Countywide consensus New starts (bus/rail) Traditional four -step Up to 20+ years Regionwide Short-term service planning Service elasticities Up to 5 years Route specific Special purpose/commuter rail Apply specific mode Vari es Route specific with choice components broader service area Special purpose/paratransit Time series re gression Vari es Countywide also provides an accounting system that can identify any data as well as ridership on similar routes. A similar routes inconsistencies in underlying systems. As noted earlier, the approach is also used for new routes. Origin/destination infor- process has fostered interaction between service planners and mation and demographic factors are used in the mode choice modelers and encouraged new ideas about model uses. model. MTANYCT is often not used as a case study because of Table 39 summarizes OCTA forecasting methods. its size relative to other transit agencies. This case study shows how application of new data collection techniques For short-term service planning, similar routes and pro- (AFC) and tools such as TransCAD can improve ridership fessional judgment are used along with elasticities. OCTA forecasting procedures. Successful exploration of new ana- uses a range of service elasticities depending on the extent lytical methods (such as inferred origins and destinations) as of the service change, but within each category the analyst ridership data become more reliable is an important finding has some leeway. Table 40 shows guidelines for service that can be applied elsewhere. Encouraging interaction elasticity factors. between modelers and end-users through organizational structure and location of the departments ultimately results Choice of elasticity within a category can be based on in model improvements and greatly increases the likelihood knowledge of the route, reason for the service change (e.g., of its being trusted and used on a consistent basis. to add service at a major activity center), other market con- MTANYCT maintains and regularly uses an in-house net- siderations, or time of day. Interestingly, OCTA used its work model specifically for analyzing and forecasting tran- long-range model to estimate service elasticities and found sit usage in New York City, whereas regional transit agency an elasticity of 0.56 for peak-period headway changes. and the MPO maintain larger and more complex demand Then, by holding service levels constant, it estimated the forecasting models that include suburban transit services elasticity of ridership with respect to demographic changes and non-transit modes. This allows MTANYCT to focus at 0.19. on its transit service planning needs while improving and updating its route coding, which is passed along to the larger OCTA uses an extensive array of input data, but does not models. currently use APC data. The Operations Department has questioned the reliability of APC data; therefore, OCTA con- tinues to rely on ridecheck and especially farebox data. The ORANGE COUNTY TRANSPORTATION automatic vehicle location (AVL) system supplies GPS coor- AUTHORITY (ORANGE, CALIFORNIA) dinates with every farebox transaction, thus greatly enhanc- OCTA prepares ridership forecasts for virtually every service ing the reliability of farebox data (good at the bus stop level, change. There is no specific threshold triggering the need for very accurate at the TAZ level). This has been the primary a ridership forecast; however, changes in route alignment or impact of technology on ridership forecasting. One advantage in the number of daily trips generate a forecast. Forecasts are used internally and are part of the general planning and mod- TABLE 40 eling duties. OCTA GUIDELINES FOR SERVICE ELASTICITY FACTORS The Operations Planning Department prepares ridership Percent Increase in Service Recommended forecasts related to short-term service changes. The Planning Level (buses/hour) Elasticity Factor 20% or less +0.50 to +0.70 Department forecasts long-term ridership as well as ridership Examples: 30 min to 25 min changes for major service changes. The Finance Department, 15 min to 12 min in consultation with the Operations Planning Department, More than 20% to 50% +0.50 to +0.75 Examples: 60 min to 45 min prepares annual forecasts for budget purposes. 45 min to 30 min More than 50% to 100% +0.75 to +0.90 OCTA uses systemwide ridership in its annual forecasts. Examples: 60 min to 30 min Changes to routes or service spans use route and route segment 30 min to 15 min