A traffic flow forecasting model based on BP neural network

Estimation of traffic flow with reasonable accuracy is essential for successful implementation of an intelligent transportation system (ITS). Crossroads are important part of urban traffic system, whose flow prediction on each direction is one of the most extraordinary key functions in the urban ITS. Some forecasting models have been developed, but these methods´ precision usually can´t meet with practical requirement. In this article, a neural network model is presented for forecasting crossroads traffic flow using backpropagation (BP) neural network. Through forecasting traffic flow at Hongqi crossroad in Ganzhou City, the result shows that this model has a considerable accuracy, which provides a new reliable and effective way of forecasting short term traffic flow of crossroads in urban road network.