Video data based Traffic State Prediction at Intersection

In this article, a method of traffic state prediction at intersection based video data is proposed. The method inherits the basic assumption of modified cell transmission model (MCTM) and depends on back propagation neural network (BPNN). The training set of the neural network consists of traffic data from the video. In order to verify the good generalization of the prediction method, novel data is used as the verification set. The experimental results exhibit that the model has virtuous generalization. Especially, the model is suitable for short-term traffic prediction at intersections. The prediction results of the method serve to construct the traffic state prediction model (TSPM) of the urban traffic network. Moreover, making route arrangement and traffic guidance strategy also require them.