Short-Term Traffic State Prediction Based on the Spatiotemporal Features of Critical Road Sections.
Recently, short-term traffic prediction under conditions with corrupted or missing data has become a popular topic. Since a road section has predictive power regarding the adjacent roads at a specific location, this paper proposes a novel hybrid convolutional long short-term memory neural network model based on critical road sections (CRS-ConvLSTM NN) to predict the traffic evolution of global networks. The critical road sections that have the most powerful impact on the subnetwork are identified by a spatiotemporal correlation algorithm. Subsequently, the traffic speed of the critical road sections is used as the input to the ConvLSTM to predict the future traffic states of the entire network. The experimental results from a Beijing traffic network indicate that the CRS-ConvLSTM outperforms prevailing deep learning (DL) approaches for cases that consider critical road sections and the results validate the capability and generalizability of the model when predicting with different numbers of critical road sections.
['Yang|Gang|G|', 'Wang|Yunpeng|Y|', 'Yu|Haiyang|H|', 'Ren|Yilong|Y|', 'Xie|Jindong|J|']
critical road sections short-term traffic prediction spatiotemporal correlation deep learning structural missing data