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 language | English |
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Title of host publication | 13th World Congress on Intelligent Transport Systems and Services |
Publisher | Intelligent Transport Systems (ITS) |
Publication status | Published - 2006 |
Event | 13th World Congress on Intelligent Transport Systems and Services, ITS 2006 - London, United Kingdom Duration: 2006 Oct 8 → 2006 Oct 12 |
Other
Other | 13th World Congress on Intelligent Transport Systems and Services, ITS 2006 |
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Country/Territory | United Kingdom |
City | London |
Period | 06-10-08 → 06-10-12 |
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