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.
- Flow measurement
- Neural networks
- Traffic control
ASJC Scopus subject areas
- Management Science and Operations Research