Traffic flow state prediction based on space-time correlation of vehicle trajectory using the deep hybrid model

Abstract

In order to alleviate the increasingly serious traffic congestion problem in China, realize intelligent traffic control, and provide accurate and real-time traffic flow prediction data for traffic flow guidance and traffic travel, this paper designs a GPS-based vehicle trajectory fusion optimization deep model BN-LSTM-CNN which makes full use of the temporal and spatial correlation characteristics of dynamic traffic flow to improve the accuracy of short-term traffic flow prediction. The parameters of the historical GPS dynamic trajectory of the traffic network link are converted into a two-dimensional matrix image of time and space relationship. First, the spatial features are input to the CNN network, and the spatial dependence relationship between the links is mined, then the traffic flow time series modeling is performed with a four-layer ConvLSTM network, and the BN normalization layer is added to normalize the activation value of the previous layer on each batch, so that the model can obtain higher training accuracy and quickly complete the prediction of the traffic flow state in a certain period of time in the future. The experimental results show that the prediction model is fast to optimize, the prediction error is the smallest compared with other methods, and it can meet the real-time requirements of urban traffic control.