Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/32626
Title: Intelligent simulation and prediction of traffic flow dispersion
Authors: Qiao, F
Yang, H
Lam, WHK 
Keywords: Flow measurement
Identification
Neural networks
Simulation
Traffic control
Issue Date: 2001
Publisher: Pergamon Press
Source: Transportation research. Part B, Methodological, 2001, v. 35, no. 9, p. 843-863 How to cite?
Journal: Transportation research. Part B, Methodological 
Abstract: Dispersion of traffic flow on urban road segments is often described by some typical statistical models such as the normal distribution model and the geometric distribution model. These probability-based models can fit traffic flow well under ideal physical environments but may not work satisfactory in certain complex cases because of their strict mathematical assumptions. A neural network-based system identification approach is used to establish an auto-adaptive model for simulating traffic flow dispersion. This model, being feasible to a wide variety of traffic circumstances, can be calibrated and used for on-line traffic flow forecasting. Data simulation and field-testing show reliable performance of the proposed intelligent approach.
URI: http://hdl.handle.net/10397/32626
ISSN: 0191-2615
EISSN: 1879-2367
DOI: 10.1016/S0191-2615(00)00024-2
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