A Comparison Study of Aggregate and Disaggregate Trip Distribution Modeling

Due to the newly advance technology of Intelligent Transport Systems, the paradigm of transport modeling has been changed. An enriched data have made theoretical models more comparable in terms of forecasting power. This paper uses an archived Automatic Passenger Counting (APC) data of urban rail. The APC data contains information about trip’s origin, destination, ticket type, fare, and distance of 4 million passengers on a daily basis. The objective of this paper is to compare the goodness-of-fit of aggregate and disaggregate-trip distribution modeling using the APC data. A generalized aggregate gravity model is used as the aggregate model. The disaggregate model adopts a multinomial logit as destination choice model. In general, the formulas of aggregate and disaggregate trip distribution model are similar. But, the calibration method and parameter estimation method of the two models are not the same. As a result, this empirical study showed that the goodness-of-fit and forecasting power largely varied depending on the estimation method and selected variables. The forecasting power of disaggregate modeling approach outperformed that of the aggregate model. This paper found that combined travel impedance function and spatial effect had an important role in modeling disaggregate trip distribution.

  • Corporate Authors:

    ITS America

    1100 17th Street, NW, 12th Floor
    Washington, DC  United States  20036
  • Authors:
    • Kim, Chansung
    • Bhiromkaew, Jaturapat
  • Conference:
  • Publication Date: 2005

Language

  • English

Media Info

  • Media Type: Print
  • Features: CD-ROM; Figures; References; Tables;
  • Pagination: 10p
  • Monograph Title: Proceedings of the 12th World Congress on Intelligent Transport Systems

Subject/Index Terms

Filing Info

  • Accession Number: 01015797
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Dec 9 2005 12:42PM