Abstract
Accurate and real-time passenger flow prediction is of great significance for realizing intelligent transportation systems. However, due to the complexity and unstable change of traffic network passenger flow data, passenger flow prediction remains a challenging problem in transportation research field. Moreover, the core problem is how to obtain the spatial and temporal characteristics efficiently. In this paper, we propose an Enhanced Self-node Weights Based Spatial-Temporal Graph Convolutional Networks (EST-GCN) model to capture the spatial and temporal characteristics. Specifically, in order to capture the spatial characteristics, we optimize the ability of Graph Convolutional of Network (GCN) in extracting the spatial characteristics of rail transit networks based on the difference maximization of aggregated information, hoping to solve the problem that GCN cannot fit peak value accurately. As for temporal characteristics, we leverage the Gate Recurrent Unit (GRU) model to obtain dynamic changes of passenger flow data to capture them. The EST-GCN model is a combination of these two models. Based on the Shanghai dataset, we use the proposed EST-GCN model for simulation experiments, and compare our proposed method with other mainstream passenger flow prediction algorithms. The experimental results demonstrate the superiority of our algorithm.
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References
Gao, C., Fan, Y., Jiang, S., Deng, Y., Liu, J., Li, X.: Dynamic robustness analysis of a two-layer rail transit network model. IEEE Trans. Intell. Transp. Syst. 1–16 (2021). https://doi.org/10.1109/TITS.2021.3058185
Williams, B.M., Hoel, L.A.: Modeling and forecasting vehicular traffic flow as a seasonal arima process: theoretical basis and empirical results. J. Transp. Eng. 129(6), 664–672 (2003)
Ye, J., Zhao, J., Xu, C.: How to build a graph-based deep learning architecture in traffic domain: a survey. IEEE Trans. Intell. Transp. Syst. 6, 1–21 (2020). https://doi.org/10.1109/TITS.2020.3043250
Yao, Z.S., Shao, C.F.: Research on methods of short-term traffic forecasting based on support vector regression. J. Beijing Jiaotong Univ. 30(3), 19–22 (2006)
Deng, Y., Gao, C., Li, X.: Assessing temporal-spatial characteristics of urban travel behaviors from multiday smart-card data. Phys. A 576, 126058 (2021)
Wang, H., Gao, C., Liu, J.: Medication combination prediction using temporal attention mechanism and simple graph convolution. IEEE J. Biomed. Inform. 1–1 (2021). https://doi.org/10.1109/JBHI.2021.3082548
Yu, B., Yin, H., Zhu, Z.: Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting. In: 27th International Joint Conference on Artificial Intelligence, pp. 3634–3640. IJCAI, Sweden (2018)
Zhao, L., Deng, M., Li, H.: T-GCN: a temporal graph convolutional network for traffic prediction. IEEE Trans. Intell. Transp. Syst. 21(9), 3848–3858 (2020)
He, Y., Zhao, Y., Wang, H., Tsui, K.L.: GC-LSTM: a deep spatiotemporal model for passenger flow forecasting of high-speed rail network. In: 23rd International Conference on Intelligent Transportation Systems, pp. 1–6. IEEE, Rhodes (2020)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: 5th International Conference on Learning Representations, pp. 1–5. OpenReview, Toulon (2017)
Defferrard, M., Bresson, X., Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral filtering. In: 29th Annual Conference on Neural Information Processing Systems, pp. 3844–3853. NIPS, Barcelona (2016)
Estrach, J.B., Zaremba, W., Szlam, A., LeCun, Y.: Spectral networks and deep locally connected networks on graphs. In: 2nd International Conference on Learning Representations, pp. 1–14. OpenReview, Banff (2014)
Fu, R., Zhang, Z., Li, L.: Using LSTM and GRU neural network methods for traffic flow prediction. In: 31st Youth Academic Annual Conference of Chinese Association of Automation, pp. 324–328. IEEE, Wuhan (2016)
Cao, W., Gao, J., Ming, Z., Cai, S.: Some tricks in parameter selection for extreme learning machine. In: IOP Conference Series: Materials Science and Engineering, p. 012002. IOP, Hawaii (2017)
Cao, W., Gao, J., Ming, Z., Cai, S., Zheng, H.: Impact of probability distribution selection on rvfl performance. In: 2nd International Conference on Smart Computing and Communication, pp. 114–124. Springer, Shenzhen (2017)
Smola, A.J., Schölkopf, B.: A tutorial on support vector regression. Statist. Comput. 14(3), 199–222 (2004)
Guo, S., Lin, Y., Feng, N., Song, C., Wan, H.: Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. In: 33rd AAAI Conference on Artificial Intelligence, pp. 922–929. AAAI, Honolulu (2019)
Acknowledgement
This work was supported by the National Key R&D Program of China (No. 2019YFB2102300), National Natural Science Foundation of China (Nos. 61976181, 11931015, 61762020), Key Technology Research and Development Program of Science and Technology Scientific and Technological Innovation Team of Shaanxi Province (No. 2020TD-013) and the Science and Technology Support Program of Guizhou (No. QKHZC2021YB531).
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Liu, H., Zhang, F., Fan, Y., Zhu, J., Wang, Z., Gao, C. (2021). Enhanced Self-node Weights Based Graph Convolutional Networks for Passenger Flow Prediction. In: Qiu, H., Zhang, C., Fei, Z., Qiu, M., Kung, SY. (eds) Knowledge Science, Engineering and Management. KSEM 2021. Lecture Notes in Computer Science(), vol 12817. Springer, Cham. https://doi.org/10.1007/978-3-030-82153-1_22
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