A MODAL SPLIT MODEL FOR HIGH DENSITY URBAN CORRIDORS

The objective of the study was to develop a modal split model that would be relatively simple, require little lead time, use readily available data, and be sensitive to policy alternatives. The model is designed to contribute to the evaluation of such policy options as station closing, new route alternatives, addition of park and ride facilities, skip stop policies, and increasing capacity. This study produces modal split models specifically for high density urban corridors. In a two stage process splits are established between the automobile and public transportation, and then bus and rapid transit. The aggregate, trip interchange models are calibrated using weighted least squares, with modal disutility functions, service characteristics, and trip end densities as independent variables. The background for the modeling procedure is established by producing a multitude of computer generated maps displaying the modal split patterns and by graphing the socioeconomic correlates of modal split in the Chicago area. Special attention was given to a thorough application of the model to Howard Corridor with Chicago Transit Authority rail rapid transit service. The application estimated the effects of closing selected peak period reverse commuting platforms to expedite service. In the process the model was improved.

  • Corporate Authors:

    University of Illinois, Chicago

    Urban Transportation Center, 412 South Peoria Street, Suite 340
    Chicago, IL  United States  60607

    Urban Mass Transportation Administration

    400 7th Street, SW
    Washington, DC  United States  20590
  • Authors:
    • Soot, S
    • Sen, A
    • Pagitsas, E
  • Publication Date: 1978-3

Media Info

  • Pagination: 146 p.

Subject/Index Terms

Filing Info

  • Accession Number: 00182023
  • Record Type: Publication
  • Source Agency: National Technical Information Service
  • Report/Paper Numbers: UMTA-IL-11-0008-78-2Res Rpt.
  • Files: NTIS, TRIS, USDOT
  • Created Date: Dec 3 1981 12:00AM