STNCN: A New Method for Traffic Flow Prediction Based on the Node Correlation

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Abstract
The prediction of traffic conditions plays a significant role in the intelligent transportation system. Since the traffic flow is able to directly reflect traffic situation, it is of great value to predict the traffic flow in reality. The traffic flow data has complex spatiotemporal characteristics. Meanwhile, a high node correlation between nodes in the traffic network. Therefore, the key point of accurate traffic flow prediction is to analyze the temporal and spatial correlation in the data, as well as integrate the node correlation. We are motivated to propose a spatial-temporal node correlation network (STNCN) for improving traffic flow prediction accuracy. First, a node correlation matrix is constructed to describe the pairwise correlation between nodes. We then propose a recurrent graph network to analyze node correlation and spatial correlation of traffic flow data. Lastly, a temporal convolutional network is applied to extract temporal features. We combine the outputs of the temporal convolutional networks of different periods into the fully connected layer to obtain final prediction results. The prediction results on real datasets demonstrate the effectiveness of the proposed method.
Funding Information
  • National Key Research and Development Plan Key Special Projects (2018YFB2100303)
  • National Natural Science Foundation of China (61802216)
  • Shandong Province colleges and universities youth innovation technology plan innovation team project (2020KJN011)
  • Program for Innovative Postdoctoral Talents in Shandong Province (40618030001)

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