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NEW METHOD FOR TRANSIT RIDERSHIP FORECASTING

The feasibility - and fundability -- of a new transit service hinges greatly on ridership projections. Rail ridership is traditionally forecast with region-wide travel demand models, which often represent a region's transportation network and land use at an aggregate scale. At this aggregate scale, these models often are unresponsive to changes in station-level land use and transit service characteristics. The paper first summarizes recent relevant literature on station area development and rail transit ridership. It then provides a general description of the methodology used to develop a quick-response approach for directly forecast rail ridership for three different rail services in two California regions: (1) Bay Area Rapid Transit (BART) - Heavy Rail; (2) Sacramento Regional Transit (RT) - Light Rail; and (3) Sonoma Marin Area Rail Transit (SMART) - Commuter Rail. In each instance multivariate regression was used to determine the station characteristics that most influence rail transit patronage for light rail, commuter rail, and heavy rail in the Bay Area and Sacramento regions. The resulting equations are designed to be directly and quantitatively responsive to land use and transit service characteristic changes within the immediate areas of prospective transit stations. The forecasting models developed incorporate variables such as parking, rail service levels and characteristics, feeder bus levels, as well as data on station-area households and employment to estimate ridership.

  • Availability:
  • Supplemental Notes:
    • Full conference proceedings available on CD-ROM.
  • Corporate Authors:

    Institute of Transportation Engineers (ITE)

    Washington, DC  United States 
  • Authors:
    • Saur, G J
    • Lee, R
    • Gray, C
  • Conference:
  • Publication Date: 2004

Language

  • English

Media Info

  • Features: References; Tables;
  • Pagination: 14p

Subject/Index Terms

Filing Info

  • Accession Number: 00981538
  • Record Type: Publication
  • ISBN: 0935403876
  • Files: TRIS
  • Created Date: Nov 4 2004 12:00AM