MULTIVARIATE TIME-SERIES MODEL OF TRANSIT RIDERSHIP BASED ON HISTORICAL, AGGREGATE DATA: THE PAST, PRESENT AND FUTURE OF HONOLULU (WITH DISCUSSION AND CLOSURE)

Historical data on a small number of economic, demographic, and transportation variables from 1958 to 1986 were analyzed by multiple regression techniques to develop two models for forecasting transit ridership in Honolulu. A model predicting revenue trips and another for linked trips were consistent in their determination that the same five variables could account for 97 to 98 percent of the variance in bus ridership over this 29-year period. The four major variables were per capita income, employment, fares, and size of bus fleet, with a dummy variable included for strikes. The income elasticity for transit demand was found to be negative, indicating that mass transit is an inferior good. The model forecasts a continuing decline in bus ridership for Honolulu, mainly caused by this effect. The forecasting models for rapid transit ridership for Honolulu are examined, and alternative approaches to assessing demand elasticities are discussed. The advantages of using aggregate historical data and regression analyses for developing inexpensive forecasting models from time series data are emphasized.

Media Info

  • Features: Figures; References;
  • Pagination: p. 76-84
  • Monograph Title: Public transit research: management and planning 1991
  • Serial:

Subject/Index Terms

Filing Info

  • Accession Number: 00615778
  • Record Type: Publication
  • ISBN: 0309051037
  • Files: TRIS, TRB
  • Created Date: Sep 30 1991 12:00AM