Research on Prediction of Traffic Mode Choice of Urban Residents

Reasonable prediction of trip mode choice of urban residents is the foundation of effective implementation of traffic demand management and traffic control strategy. Combined with trip survey data of residents, this paper first analyzes main factors influencing trip mode choice of urban residents. Then, prediction models are built respectively based on a multinomial logit model and probabilistic neural network. By using these two models, probability of each trip mode choice is calculated and residents' final choice is estimated. Finally, an accuracy test of these two models is completed by calculating their hit rates and errors. The results indicate that the prediction model built based on probabilistic neural network can enhance prediction accuracy and overcome the inherent defects of a multinomial logit model. Therefore, it can predict trip mode choice of urban residents more effectively.

• Record URL:
  • https://doi.org/10.1061/41186(421)45
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/9780784411865
• Supplemental Notes:
  • Copyright © 2011 ASCE
• Corporate Authors:

  American Society of Civil Engineers

  1801 Alexander Bell Drive
  Reston, VA  United States  20191-4400
• Authors:
  • Zhou, Miaomiao
  • Lu, Jian
• Conference:
  • 11th International Conference of Chinese Transportation Professionals (ICCTP)
  • Location: Nanjing , China
  • Date: 2011-8-14 to 2011-8-17
• Publication Date: 2011

Language

• English

Media Info

• Media Type: Digital/other
• Features: References;
• Pagination: pp 449-460
• Monograph Title: ICCTP 2011: Towards Sustainable Transportation Systems

Subject/Index Terms

• TRT Terms: Logits; Mathematical prediction; Mode choice; Neural networks; Surveys; Traffic control; Urban areas
• Geographic Terms: China
• Subject Areas: Highways; Passenger Transportation; Planning and Forecasting; Public Transportation; I72: Traffic and Transport Planning;

Filing Info

• Accession Number: 01453790
• Record Type: Publication
• ISBN: 9780784411865
• Files: TRIS, ASCE
• Created Date: Nov 15 2012 12:32PM