A comparative study on the dynamic estimation of network origin-destination demands

Shou-Ren Hu, Chang Ming Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The purpose of the present research is to conduct a comparative study on the dynamic estimation of network origin-destination (OD) demands using two statistical methods, that is least squares and Kalman filtering(KF) methods, and an artificial intelligence (AI) approach, i.e., Artificial Neural Network (ANN)model. The numerical test results based on field data collection and simulation experimentsindicate that the ordinary least squares (OLS) method with nonnegative constraintprovides a satisfactory resultin solvingthe intersection turning proportionsproblem. Besides, in the freeway/expressway and general network cases, both the KFand ANNmethodsshowstatistically acceptable results, even though the ANN method provides a more stable and betterresult.In accordance with the above model evaluation results, one can design beneficial traffic control and/ormanagement strategiesto achieve some system-wide objectives.

Original languageEnglish
Title of host publication13th World Congress on Intelligent Transport Systems and Services
PublisherIntelligent Transport Systems (ITS)
Publication statusPublished - 2006
Event13th World Congress on Intelligent Transport Systems and Services, ITS 2006 - London, United Kingdom
Duration: 2006 Oct 82006 Oct 12

Other

Other13th World Congress on Intelligent Transport Systems and Services, ITS 2006
Country/TerritoryUnited Kingdom
CityLondon
Period06-10-0806-10-12

All Science Journal Classification (ASJC) codes

  • Mechanical Engineering
  • Transportation
  • Electrical and Electronic Engineering
  • Computer Science Applications
  • Automotive Engineering
  • Artificial Intelligence
  • Control and Systems Engineering
  • Computer Networks and Communications

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