Traffic Flow Prediction: A Spatiotemporal Convolution Model Considering Traffic Accident Influence Coefficient Matrix


Traffic Planning
Traffic Flow Prediction
Graph Convolution Network
Time Convolution Network
Traffic Accident Influence Coefficient Matrix
The Spatial-Temporal Correlation

How to Cite

Liu, X., Lv, Z., Li, J., Ma, Z., Chu, B., Xia, F., & Liu, Y. (2023). Traffic Flow Prediction: A Spatiotemporal Convolution Model Considering Traffic Accident Influence Coefficient Matrix. Annals of Applied Sciences.


Accurate and timely traffic flow prediction plays a vital role in traffic planning. But the work is more complicated for the road where the traffic accident occurred and the roads around it. This is because the influence of traffic accidents not only acts on the traffic flow of the current road but also quickly spreads to the surrounding road. Traffic accidents differ from other external factors, and their influence on surrounding traffic flow is distinct. Therefore, this work proposes an influence coefficient matrix to express the degree of influence between any two roads to quickly capture the impact of traffic accidents on different road traffic flows. Moreover, this work proposes a hybrid network model based on graph and temporal convolution. To address the spatial dependence between traffic flow data and roads, we selected a graph convolutional network that can be used to analyze the complicated non-Euclidean spatial data in order to extract the spatial dependence. Taking into consideration the temporal dependence of traffic flow data, the temporal convolution model is chosen in this work to model the temporal dependence of the data. Compared to traditional statistical models, single deep learning models, and complex spatiotemporal convolutional models, our model’s performance has been improved by 30% to 50%.


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