Prediction of Public Bus Passenger Flow Using Spatial–Temporal Hybrid Model of Deep Learning
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 148, Issue 4
Abstract
Passenger flow predictions are of great significance to bus scheduling and route optimization. In this paper, a novel algorithm, namely the Spatial–Temporal Graph Sequence with Attention Network (STGSAN), was proposed to predict transit passenger flow. The algorithm mainly focused on the following three aspects: (1) a graph attention network (GAT) was used to capture the spatial correlation of various bus stops; (2) to make full use of the historical and real-time data, a bidirectional long short-term memory and attention mechanism was conducted to extract the temporal correlation of historical ridership at bus stations; and (3) external factors that affect passenger choices were taken into account. We conducted an experiment using field data collected in Urumqi, China. After comparison with five other models, the proposed model was proven to have excellent performance prediction.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This work was jointly supported by the National Natural Science Foundation of China, which provided support for the projects entitled “Connected vehicle big data driven expressway multi-objective coordinated control fusing deep learning and traffic flow model” (Award No. 71901070).
References
Bahdanau, D., K. H. Cho, and Y. Bengio. 2015. “Neural machine translation by jointly learning to align and translate.” In Proc., 3rd Int. Conf. on Learning Representations, ICLR 2015, 1–15. New York: ACM Digital Library.
Bai, L., L. Yao, S. S. Kanhere, X. Wang, and Q. Z. Sheng. 2019. “StG2seq: Spatial-temporal graph to sequence model for multi-step passenger demand forecasting.” Appl. Soft Comput. 58 (Sep): 669–680.
Bai, Y., Z. Sun, B. Zeng, J. Deng, and C. Li. 2017. “A multi-pattern deep fusion model for short-term bus passenger flow forecasting.” Appl. Soft Comput. 58 (Sep): 669–680. https://doi.org/10.1016/j.asoc.2017.05.011.
Cui, Z., R. Ke, Z. Pu, X. Ma, and Y. Wang. 2020. “Learning traffic as a graph: A gated graph wavelet recurrent neural network for network-scale traffic prediction.” Transp. Res. Part C: Emerging Technol. 115 (Dec): 102620. https://doi.org/10.1016/j.trc.2020.102620.
Han, Y., S. Wang, Y. Ren, C. Wang, P. Gao, and G. Chen. 2019. “Predicting station-level short-term passenger flow in a citywide metro network using spatiotemporal graph convolutional neural networks.” ISPRS Int. J. Geo-Inf. 8 (6): 1–25. https://doi.org/10.3390/ijgi8060243.
Huang, L., T. Chen, Y. Wang, and H. Yuan. 2015. “Forecasting daily pedestrian flows in the Tiananmen square based on historical data and weather conditions.” Transp. Res. Part C: Emerging Technol. 85: 591–608.
Ke, J., H. Zheng, H. Yang, and X. Chen. 2017. “Short-term forecasting of passenger demand under on-demand ride services: A spatio-temporal deep learning approach.” Transp. Res. Part C: Emerging Technol. 85 (Dec): 591–608. https://doi.org/10.1016/j.trc.2017.10.016.
Kerner, B., S. Ralf, and G. Herrtwich. 2001. “Traffic flow forecasting.” Automatisierungstechnik 49 (11): 505–511. https://doi.org/10.1524/auto.2001.49.11.505.
Li, L., J. Xu, S. Thomas Ng, J. Zhang, and Y. Yang. 2020. “Attention-based graph neural network enabled method to predict short-term metro passenger flow.” In Proc., 5th Int. Conf. on Universal Village (UV). New York: IEEE. https://doi.org/10.1109/UV50937.2020.9426223.
Li, Y., X. Wang, S. Sun, X. Ma, and G. Lu. 2017. “Forecasting short-term subway passenger flow under special events scenarios using multiscale radial basis function networks.” Transp. Res. Part C: Emerging Technol. 77 (Apr): 306–328. https://doi.org/10.1016/j.trc.2017.02.005.
Li, Y., R. Yu, C. Shahabi, and Y. Liu. 2018. “Diffusion convolutional recurrent neural network: Data-driven traffic forecasting.” In Proc., 6th Int. Conf. on Learning Representations, ICLR 2018, 1–16. New York: ACM Digital Library.
Liang, Y., S. Ke, J. Zhang, X. Yi, and Y. Zheng. 2018. “Geoman: Multi-level attention networks for geo-sensory time series prediction.” In Proc., IJCAI Int. Joint Conf. on Artificial Intelligence 2018, 3428–3434. Tokyo: Multimedia Tools and Applications.
Liu, D., Z. Wu, and S. Sun. 2020. “Study on subway passenger flow prediction based on deep recurrent neural network.” Multimedia Tools Appl. 6 (11): 335–343. https://doi.org/10.1007/s11042-020-09088-x.
Liu, G., Z. Yin, Y. Jia, and Y. Xie. 2017. “Passenger flow estimation based on convolutional neural network in public transportation system.” Knowledge-Based Syst. 123 (May): 102–115. https://doi.org/10.1016/j.knosys.2017.02.016.
Liu, X. 2018. “A model of short-term forecast of passenger flow of buses based on SVM-KNN under rainy conditions.” J. Transp. Info. Saf. 36 (5).
Lu, Y., H. Ding, S. Ji, N. N. Sze, and Z. He. 2021. “Dual attentive graph neural network for metro passenger flow prediction.” Neural Comput. Appl. 33 (1): 1–15. https://doi.org/10.1007/s00521-021-05966-z.
Ma, J., J. Gu, Q. Zhou, Q. Wang, and M. Sun. 2020. “Dynamic-static-based spatiotemporal multi-graph neural networks for passenger flow prediction.” In Proc., 26th Int. Conf. on Parallel and Distributed Systems. New York: IEEE. https://doi.org/10.1109/ICPADS51040.2020.00095.
Ma, X., Z. Tao, Y. Wang, H. Yu, and Y. Wang. 2015. “Long short-term memory neural network for traffic speed prediction using remote microwave sensor data.” Transp. Res. Part C: Emerging Technol. 54 (May): 187–197. https://doi.org/10.1016/j.trc.2015.03.014.
Mi, G., Z. Liqin, and L. Miao. 2015. “Subway station passenger flow forecast based on mixed kernel support vector machine optimized by golden section chaotic particle swarm optimization.” Comput. Eng. Appl. 51 (14): 231–235.
Qu, L., W. Li, W. Li, D. Ma, and Y. Wang. 2019. “Daily long-term traffic flow forecasting based on a deep neural network.” Expert Syst. Appl. 121 (May): 304–312. https://doi.org/10.1016/j.eswa.2018.12.031.
Su, B., and Z. Wen. 2020. “Traffic flow prediction via spatial temporal neural network ‘ResLS-C.’” In Proc., 2020 8th Int. Conf. on Advanced Cloud and Big Data, CBD 2020, 119–124. New York: IEEE.
Tong, M., and H. Xue. 2008. “Highway traffic volume forecasting based on seasonal ARIMA model.” J. Highway Transp. Res. Dev. 3 (2): 109–112. https://doi.org/10.1061/JHTRCQ.0000255.
Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin. 2017. “Attention is all you need.” In Advances in neural information processing systems 2017, 5999–6009. Red Hook, NY: Curran Associates.
Veličković, P., A. Casanova, P. Liò, G. Cucurull, A. Romero, and Y. Bengio. 2018. “Graph attention networks.” Preprint, submitted October 30, 2017. https://arxiv.org/abs/1710.10903.
Yao, H., X. Tang, H. Wei, G. Zheng, and Z. Li. 2019. “Revisiting spatial-temporal similarity: A deep learning framework for traffic prediction.” In Proc., 33rd AAAI Conf. on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conf., 5668–5674. Palo Alto, CA: Association for the Advancement of Artificial Intelligence.
Yu, B., H. Yin, and Z. Zhu. 2018. “Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting.” In Proc., IJCAI Int. Joint Conf. on Artificial Intelligence, 3634–3640. New York: ACM Digital Library.
Zhang, H., J. He, J. Bao, Q. Hong, and X. Shi. 2020. “A hybrid spatiotemporal deep learning model for short-term metro passenger flow prediction.” J. Adv. Transp. 2020 (May): 30. https://doi.org/10.1155/2020/4656435.
Zhang, Z., and T. Liang. 2019. “Short-term forecasting of passenger flow on the metro platform using an improved Kalman filtering method.” In Proc., 19th COTA Int. Conf. of Transportation Professionals, 2789–2801. Reston, VA: ASCE.
Zhao, L., Y. Song, C. Zhang, Y. Liu, P. Wang, T. Lin, M. Deng, and H. Li. 2020. “T-GCN: A temporal graph convolutional network for traffic prediction.” IEEE Trans. Intell. Transp. Syst. 21 (9): 3848–3858. https://doi.org/10.1109/TITS.2019.2935152.
Zheng, C., X. Fan, C. Wang, and J. Qi. 2020. “GMAN: A graph multi-attention network for traffic prediction.” In Proc., AAAI Conf. on Artificial Intelligence, 1234–1241. Palo Alto, CA: Association for the Advancement of Artificial Intelligence.
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History
Received: Jun 29, 2021
Accepted: Dec 8, 2021
Published online: Jan 28, 2022
Published in print: Apr 1, 2022
Discussion open until: Jun 28, 2022
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