Traffic Flow Prediction Model Based on Drivers’ Cognition of Road Network
Songjiang Li, Wen An, and Peng Wang
School of Computer Science and Technology, Changchun University of Science and Technology
No.7186 Weixing Road, Chaoyang, Changchun, Jilin 130022, China
The traditional traffic flow prediction method is based on data modeling, when emergencies occur, it is impossible to accurately analyze the changes in traffic characteristics. This paper proposes a traffic flow prediction model (BAT-GCN) which is based on drivers’ cognition of the road network. Firstly, drivers can judge the capacity of different paths by analyzing the travel time in the road network, which bases on the drivers’ cognition of road network space. Secondly, under the condition that the known road information is obtained, people through game decision-making for different road sections to establish the probability model of path selection; Finally, drivers obtain the probability distribution of different paths in the regional road network and build the prediction model by combining the spatiotemporal directed graph convolution neural network. The experimental results show that the BAT-GCN model reduces the prediction error compared with other baseline models in the peak period.
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