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

Shou Ren Hu, Chang Ming Wang

研究成果: Paper同行評審

摘要

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.

原文English
出版狀態Published - 2006
事件13th World Congress on Intelligent Transport Systems and Services, ITS 2006 - London, United Kingdom
持續時間: 2006 10月 82006 10月 12

Other

Other13th World Congress on Intelligent Transport Systems and Services, ITS 2006
國家/地區United Kingdom
城市London
期間06-10-0806-10-12

All Science Journal Classification (ASJC) codes

  • 機械工業
  • 運輸
  • 電氣與電子工程
  • 電腦科學應用
  • 汽車工程
  • 人工智慧
  • 控制與系統工程
  • 電腦網路與通信

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