Deep Spatio-Temporal Residual Networks for Connected Urban Vehicular Traffic Prediction

Recent advancement of connected vehicles technologies combined with machine learning (MA) methods has shown great potential for the improvement of efficiency of Intelligent Transportation System. In this work, considering the spatio-temporal correlations under vehicle distribution on urban road network, neural network based deep learning solution is adopted to obtain vehicle driving characteristics and predict future traffic conditions. First, to address the huge challenge brought by complex traffic environment, we present a fine-grained regional-level forecast structure for the prediction of traffic flow at each road. After that, a residual network based deep learning traffic prediction algorithm called DST-RGTP is proposed for the performance enhancement of vehicle regulation in the entire traffic system. Finally, we use the real traffic data of Beijing and open-source road network data on Openstreetmap to test the proposed method. Simulation results verify the accuracy of prediction approach DST-RGTP, which can help to improve the urban traffic management efficiency.