The object of this research report is to present a simpler method of predicting the patronage of rural public transportation systems. Previous methods of estimating the need or demand for rural public transportation are discussed. These methods include subjective, gap analysis, surveys, per capita aggregate estimates, and simulation of demand functions. Using data from approximately 100 existing rural transport systems, simulation models of factors influencing the number of riders were developed. It was found that reliable estimates of demand could be produced by using a small number of variables that described characteristics of the area and people served, and attributes of the transportation system. The elements affecting the ridership of rural demand-responsive transit systems are similar to those affecting the demand for rural fixed-route transit systems, but there are significant differences in the definition of area served, trip generation, and measurement of service responsiveness. The following factors were identified as having a major influence on the number of persons that can be expected to ride a given rural transit system: Monthly bus miles; availability of service; population served; other public transportation systems; distance; and fares. The greatest benefit of the demand equations is that they provide a rough estimate of how many people might use a system according to specific rural area and transit system conditions.

  • Availability:
  • Corporate Authors:

    Eno Transportation Foundation

    1250 I Street, NW, Suite 750
    Washington, DC  United States  20005
  • Authors:
    • Burkhardt, J E
    • Lago, A M
  • Publication Date: 1978-1

Media Info

  • Features: Figures; Tables;
  • Pagination: p. 105-129
  • Serial:
    • Traffic Quarterly
    • Volume: 32
    • Issue Number: 1
    • Publisher: Eno Transportation Foundation
    • ISSN: 0041-0713

Subject/Index Terms

Filing Info

  • Accession Number: 00177333
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Aug 27 1982 12:00AM