Intelligent simulation and prediction of traffic flow dispersion

Dispersion of traffic flow on urban road segments is often described by som e typical statistical models such as the normal distribution model and the geometric distribution model. These probability-based models can fit traffi c 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 calibrat ed and used for on-line traffic flow forecasting. Data simulation and field -testing show reliable performance of the proposed intelligent approach. (C ) 2001 Elsevier Science Ltd. All rights reserved.