Analysis of Chengdu City's Urban Public Transit System Based on Disaggregate Model Theory

In order to analyse the competition of Chengdu city's urban public transit system to ascertain the share of passenger flow, this paper uses disaggregate model theory and chooses MNL (Multi-nomial Logit) model to estimate the traffic mode split according to Stochastic utility maximization theory. In addition, a questionnaire survey combined with stated preference (SP) and revealed preference (RP) characteristics about the resident trip behavior is presented. Then, this paper utilizes the Maximum likelihood method to estimate the parameters of this travel choice model. Finally, the sensitivities of public concerned factors are analyzed such as speed, departure interval and ticket fare of public transit, and the calculation provides a basis for the transportation departmental policy.

• Record URL:
  https://doi.org/10.1061/41039(345)445
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/9780784410394
• Supplemental Notes:
  • © 2009 American Society of Civil Engineers.
• Corporate Authors:

  American Society of Civil Engineers

  1801 Alexander Bell Drive
  Reston, VA  United States  20191-4400
• Authors:
  • Zhao, Daibin
  • Du, Wen
  • Liu, Jiemei
• Conference:
  • Second International Conference on Transportation Engineering
  • Location: Chengdu , China
  • Date: 2009-7-25 to 2009-7-27
• Publication Date: 2009-7

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 2701-2706
• Monograph Title: International Conference on Transportation Engineering 2009

Subject/Index Terms

• TRT Terms: Mode choice; Passengers; Revealed preferences; Sensitivity analysis; Stated preferences; Traffic models
• Geographic Terms: Chengdu (China)
• Subject Areas: Operations and Traffic Management; Passenger Transportation; Planning and Forecasting; Public Transportation; I72: Traffic and Transport Planning;

Filing Info

• Accession Number: 01529729
• Record Type: Publication
• ISBN: 9780784410394
• Files: TRIS, ASCE
• Created Date: Jun 30 2014 9:44AM