The Urban Arterial Traffic Flow Forecasting Based on BP Neural Network

Predictive analytics of the traffic flow is paid more attention by the traffic engineering experts and relevant departments. However, how to forecast traffic volume still is an important problem affecting the traffic theoretical and practical analysis. Firstly, this paper set up a three layers BP neural network basing on the actual situation to introduce the modeling process of the neural network in detail, and forecast the short-term traffic volume by the means of rolling forecast. Secondly, taking the Hailar Street in Hohhot for example, two groups of test data from the same time of different days and sequent time of the same day were trained and forecast. In addition, predicting results and actual results were compared, and the correlations between test data and predicting result was analyzed and disclosed. Finally, the conclusion shows the error is acceptable and BP Neural Network constructed is practical when prediction accuracy is not very high.