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.

1. [1] X. Cheng, R. Zhang, J. Zhou et al., “DeepTransport: Learning Spatial-Temporal Dependency for Traffic Condition Forecasting,” 2018 Int. Joint Conf. on Neural Networks (IJCNN), pp. 1-8, 2018.
2. [2] J. Ke, H. Zheng, H. Yang et al., “Short-term forecasting of passenger demand under on-demand ride services: A spatio-temporal deep learning approach,” Transportation Research Part C: Emerging Technologies, Vol.85, pp. 591-608, 2017.
3. [3] H. Yu, Z. Wu, S. Wang et al., “Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks,” Sensors, Vol.17, No.7, Article No.1501, 2017.
4. [4] B. Yang, S. Sun, J. Li et al., “Traffic Flow Prediction Using LSTM with Feature Enhancement,” Neurocomputing, Vol.332, pp. 320-327, 2019.
5. [5] D. Zhu, H. Du, Y. Sun et al., “Research on Path Planning Model Based on Short-Term Traffic Flow Prediction in Intelligent Transportation System,” Sensors, Vol.18, No.12, Article No.4275, 2018.
6. [6] T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” 5th Int. Conf. on Learning Representations (ICLR), 2017.
7. [7] Y. Li, R. Yu, C. Shahabi, and Y. Liu, “Diffusion convolutional recurrent neural network: Data-driven traffic forecasting,” 6th Int. Conf. on Learning Representations (ICLR), 2018.
8. [8] S. Gao, “Optimal adaptive routing and traffic assignment in stochastic time-dependent networks,” Ph.D. Thesis, Massachusetts Institute of Technology, 2005.
9. [9] S. Gao and H. Huang, “Real-time traveler information for optimal adaptive routing in stochastic time-dependent networks,” Transportation Research Part C: Emerging Technologies, Vol.21, No.1, pp. 196-213, 2012.
10. [10] J. Zhang, Y. Zheng, J. Sun, and D. Qi, “Flow Prediction in Spatio-Temporal Networks Based on Multitask Deep Learning,” IEEE Trans. on Knowledge and Data Engineering, Vol.32, No.3, pp. 468-478, 2020.
11. [11] J. Bruna, W. Zaremba, A. Szlam et al., “Spectral Networks and Locally Connected Networks on Graphs,” Int. Conf. on Learning Representations (ICLR), 2014.