Probit-based multi-class passenger O-D estimation in networks with Public Transport Information System

Liu Yang, Hing Keung William Lam

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

This paper deals with the probit-based multi-class transit passenger origin-destination (O-D) estimation problem in networks with the Public Transport Information System (PTIS) for pre-trip planning. This problem is formulated as an equivalent bi-level programming problem. At the upper level, an objective function is formulated which minimizes the sum of error measurements in passenger flow counts and O-D matrices. The lower level is to solve a probit-based multi-class transit stochastic user equilibrium assignment problem for congested transit network. In the lower-level problem, there are two classes of passengers with different information for their travels as some of them may use the PTIS before their travel in order to minimize their travel time. A heuristic solution algorithm is adapted for solving the multi-class passenger O-D estimation problem. Finally, a numerical example is used to illustrate the application of the proposed method and the solution algorithm.
Original languageEnglish
Title of host publicationTransportation and the Economy - Proceedings of the 10th International Conference of Hong Kong Society for Transportation Studies, HKSTS 2005
Pages510-519
Number of pages10
Publication statusPublished - 1 Dec 2005
Event10th International Conference of Hong Kong Society for Transportation Studies: Transportation and the Economy, HKSTS 2005 - Kowloon, Hong Kong
Duration: 10 Dec 200510 Dec 2005

Conference

Conference10th International Conference of Hong Kong Society for Transportation Studies: Transportation and the Economy, HKSTS 2005
Country/TerritoryHong Kong
CityKowloon
Period10/12/0510/12/05

ASJC Scopus subject areas

  • Automotive Engineering
  • Civil and Structural Engineering
  • Mechanical Engineering
  • Safety, Risk, Reliability and Quality

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