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

Shou-Ren Hu, Chang Ming Wang

Research output: Contribution to conferencePaper

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
StatePublished - 1 Jan 2006
Event13th World Congress on Intelligent Transport Systems and Services, ITS 2006 - London, United Kingdom
Duration: 8 Oct 200612 Oct 2006

Conference

Conference13th World Congress on Intelligent Transport Systems and Services, ITS 2006
CountryUnited Kingdom
CityLondon
Period8/10/0612/10/06

Keywords

  • Artificial neural network
  • Kalman filtering
  • Least squares
  • Origin-destination

Fingerprint Dive into the research topics of 'A comparative study on the dynamic estimation of network origin-destination demands'. Together they form a unique fingerprint.

  • Cite this

    Hu, S-R., & Wang, C. M. (2006). A comparative study on the dynamic estimation of network origin-destination demands. Paper presented at 13th World Congress on Intelligent Transport Systems and Services, ITS 2006, London, United Kingdom.