Journal of Transport and Land Use

Spatial-temporal deep learning model based on Similarity Principle for dock shared bicycles ridership prediction

Jiahui Zhao

Southeast University

Zhibin Li

Southeast University

Pan Liu

Southeast University

Mingye Zhang

Southeast University

DOI: https://doi.org/10.5198/jtlu.2024.2348

Keywords: Keywords: Traffic demand prediction, Similarity-based Principle, Spatio-temporal Graph Convolutional Neural Network model; activity-based geographic information; prediction of bicycle sharing ridership.

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Abstract

Demand prediction plays a critical role in traffic research. The key challenge of traffic demand prediction lies in modeling the complex spatial dependencies and temporal dynamics. However, there is no mature and widely accepted concept to support the solution of the above challenge. Essentially,  a prediction model combined with similar objects in temporal and spatial dimensions could obtain better performance. This paper proposes a concept called the Similarity-based Principle (SP), which is applied to improve the prediction performance of deep learning models in complex traffic scenarios. For the temporal components, the long-term temporal dynamics in contemporaneous historical data for ridership are extracted by the Stacked Autoencoder (SAE) method. For the spatial components, the activity-based spatial geographic information (ABG-information) is used to capture the spatial correlation of the traffic network, which is reflected in the daily activities of humans. Specifically, the SP is applied to a Spatio-temporal Graph Convolutional Neural Network (STGCNN) model. In the case study, the  Similarity-based Principle Spatio-temporal Graph Convolutional Neural Network (SP-STGCNN) model predicts demand for bicycle sharing in San Francisco. The results show that the SP effectively improves the model's performance. The prediction accuracy is enhanced by up to 10.34% compared with STGCNN. For spatial relationships, the model using the geographic information attribute performs better than that using the road information attribute and the distance attribute. It is proved that the construction of the Spatio-temporal model-based similarity principle can improve the performance.

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Author Biography

Zhibin Li, Southeast University

 

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References

Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., … & Farhan, L. (2021). Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions. Journal of Big Data, 8(1), 53. https://doi.org/10.1186/s40537-021-00444-8

Bai, S., Jiao, J., Chen, Y., & Guo, J. (2021). The relationship between E-scooter travels and daily leisure activities in Austin, Texas. Transportation Research Part D: Transport and Environment, 95, 102844. https://doi.org/10.1016/j.trd.2021.102844

Bao, Y.-X., Shi, Q., Shen, Q.-Q., & Cao, Y. (2021). Spatial-temporal 3D residual correlation network for urban traffic status prediction. Symmetry, 14(1), 33. https://doi.org/10.3390/sym14010033

Bogaerts, T., Masegosa, A. D., Angarita-Zapata, J. S., Onieva, E., & Hellinckx, P. (2020). A graph CNN-LSTM neural network for short and long-term traffic forecasting based on trajectory data. Transportation Research Part C: Emerging Technologies, 112, 62–77. https://doi.org/10.1016/j.trc.2020.01.010

Chai, D., Wang, L., & Yang, Q. (2018). Bike flow prediction with multi-graph convolutional networks. Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, 397–400. https://doi.org/10.1145/3274895.3274896

Chen, D., Wang, H., & Zhong, M. (2020). A short-term traffic flow prediction model based on AutoEncoder and GRU. Paper presented at the12th International Conference on Advanced Computational Intelligence, March 14–16, Dali, Yunnan, China.

Chen, L., Thakuriah, P., & Ampountolas, K. (2021). Short-term prediction of demand for ride-hailing services: A deep learning approach. Journal of Big Data Analytics in Transportation, 3(2), 175–195. https://doi.org/10.1007/s42421-021-00041-4

Chen, Z., Wu, H., O’Connor, N. E., & Liu, M. (2021). A comparative study of using spatial-temporal graph convolutional networks for predicting availability in bike sharing schemes. ArXiv: 2104.10644 [Cs, Eess]. http://arxiv.org/abs/2104.10644

Chen, Z., Zhao, B., Wang, Y., Duan, Z., & Zhao, X. (2020). Multitask learning and GCN-based taxi demand prediction for a traffic road network. Sensors, 20(13), 3776. https://doi.org/10.3390/s20133776

Cui, Z., Henrickson, K., Ke, R., & Wang, Y. (2020). Traffic graph convolutional recurrent neural network: A deep learning framework for network-scale traffic learning and forecasting. IEEE Transactions on Intelligent Transportation Systems, 21(11), 4883–4894. https://doi.org/10.1109/TITS.2019.2950416

Cui, Z., Ke, R., Pu, Z., Ma, X., & Wang, Y. (2020). Learning traffic as a graph: A gated graph wavelet recurrent neural network for network-scale traffic prediction. Transportation Research Part C: Emerging Technologies, 115, 102620. https://doi.org/10.1016/j.trc.2020.102620

Dauphin, Y. N., Fan, A., Auli, M., & Grangier, D. (2017). Language modeling with gated convolutional networks. Proceedings of the 34th International Conference on Machine Learning, 70, 933–941.

Do, L. N. N., Vu, H. L., Vo, B. Q., Liu, Z., & Phung, D. (2019). An effective spatial-temporal attention based neural network for traffic flow prediction. Transportation Research Part C: Emerging Technologies, 108, 12–28. https://doi.org/10.1016/j.trc.2019.09.008

Du, B., Hu, X., Sun, L., Liu, J., Qiao, Y., & Lv, W. (2021). Traffic demand prediction based on dynamic transition convolutional neural network. IEEE Transactions on Intelligent Transportation Systems, 22(2), 1237–1247. https://doi.org/10.1109/TITS.2020.2966498

El-Assi, W., Salah Mahmoud, M. & Nurul Habib, K. (2017). Effects of built environment and weather on bike sharing demand: A station level analysis of commercial bike sharing in Toronto. Transportation 44, 589–613. https://doi.org/10.1007/s11116-015-9669-z

Fang, S., Zhang, Q., Meng, G., Xiang, S., & Pan, C. (2019). GSTNet: Global spatial-temporal network for traffic flow prediction. Proceedings of the 28th International Joint Conference on Artificial Intelligence, 2286–2293. https://doi.org/10.24963/ijcai.2019/317

Feng, J., Chen, X., Gao, R., Zeng, M., & Li, Y. (2018). DeepTP: An end-to-end neural network for mobile cellular traffic prediction. IEEE Network, 32(6), 108–115. https://doi.org/10.1109/MNET.2018.1800127

Feng, S., Chen, H., Du, C., Li, J., & Jing, N. (2018). A hierarchical demand prediction method with station clustering for bike sharing system. 2018 IEEE Third International Conference on Data Science in Cyberspace (DSC), 829–836. https://doi.org/10.1109/DSC.2018.00133

Geng, X., Li, Y., Wang, L., Zhang, L., Yang, Q., Ye, J., & Liu, Y. (2019). Spatiotemporal multi-graph convolution network for ride-hailing demand forecasting. Proceedings of the AAAI Conference on Artificial Intelligence, 33, 3656–3663. https://doi.org/10.1609/aaai.v33i01.33013656

Guo, S., Lin, Y., Wan, H., Li, X., & Cong, G. (2021). Learning dynamics and heterogeneity of spatial-temporal graph data for traffic forecasting. IEEE Transactions on Knowledge and Data Engineering, 34, 5415–5428. https://doi.org/10.1109/TKDE.2021.3056502

Guo, Y., Yang, L., & Chen, Y. (2022). Bike share usage and the built environment: A review. Frontiers in Public Health, 10, 848169. https://doi.org/10.3389/fpubh.2022.848169

Hinton, G. E. (2006). Reducing the dimensionality of data with neural networks. Science, 313(5786), 504–507. https://doi.org/10.1126/science.1127647

Hulot, P., Aloise, D., & Jena, S. D. (2018). Towards station-level demand prediction for effective rebalancing in bike-sharing systems. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 378–386. https://doi.org/10.1145/3219819.3219873

Jiang, J., Lin, F., Fan, J., Lv, H., & Wu, J. (2019). A destination prediction network based on spatiotemporal data for bike-sharing. Complexity, 2019, 1–14. https://doi.org/10.1155/2019/7643905

Jiao, J., Bai, S., & Choi, S. J. (2021). Understanding e-scooter incidents patterns in street network perspective: A case study of Travis County, Texas. Sustainability. 13(19), 10583. https://doi.org/10.3390/su131910583

Joshi, M., & Hadi, T. H. (2015). A review of network traffic analysis and prediction techniques. ArXiv:1507.05722 [Cs]. http://arxiv.org/abs/1507.05722

Li, Y., Yu, R., Shahabi, C., & Liu, Y. (2017). Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. ArXiv Preprint arXiv:1707.01926.

Li, Y., Yu, R., Shahabi, C., & Liu, Y. (2018). Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. ArXiv:1707.01926 [Cs, Stat]. http://arxiv.org/abs/1707.01926

Li, Y., & Zheng, Y. (2020). Citywide bike usage prediction in a bike-sharing system. IEEE Transactions on Knowledge and Data Engineering, 32(6), 1079–1091. https://doi.org/10.1109/TKDE.2019.2898831

Li, Y., Zheng, Y., Zhang, H., & Chen, L. (2015). Traffic prediction in a bike-sharing system. Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems, 1–10. https://doi.org/10.1145/2820783.2820837

Li, Y., Zhu, Z., Kong, D., Xu, M., & Zhao, Y. (2019). Learning heterogeneous spatial-temporal representation for bike-sharing demand prediction. Proceedings of the AAAI Conference on Artificial Intelligence, 33, 1004–1011. https://doi.org/10.1609/aaai.v33i01.33011004

Lin, L., He, Z., & Peeta, S. (2018). Predicting station-level hourly demand in a large-scale bike-sharing network: A graph convolutional neural network approach. Transportation Research Part C: Emerging Technologies, 97, 258–276. https://doi.org/10.1016/j.trc.2018.10.011

Liu, J., Si, Y., Niu, Y., & Zhang, R. (2022). Projection quantile correlation and its use in high-dimensional grouped variable screening. Computational Statistics & Data Analysis, 167, 107369. https://doi.org/10.1016/j.csda.2021.107369

Liu, L., Qiu, Z., Li, G., Wang, Q., Ouyang, W., & Lin, L. (2019). Contextualized spatial-temporal network for taxi origin-destination demand prediction. ArXiv:1905.06335 [Cs, Stat]. http://arxiv.org/abs/1905.06335

Ma, Y., Zhao, X., Zhang, C., Zhang, J., & Qin, X. (2021). Outlier detection from multiple data sources. Information Sciences, 580, 819–837. https://doi.org/10.1016/j.ins.2021.09.053

Maleki Vishkaei, B., Mahdavi, I., Mahdavi-Amiri, N., & Khorram, E. (2020). Balancing public bicycle sharing system using inventory critical levels in queuing network. Computers & Industrial Engineering, 141, 106277. https://doi.org/10.1016/j.cie.2020.106277

Molnar, C. (2020). Interpretable machine learning. Morrisville, NC: Lulu.com.

Pan, X., Shi, J., Luo, P., Wang, X., & Tang, X. (2018). Spatial as deep: Spatial CNN for traffic scene understanding. Washington, DC: Association for the Advancement of Artificial Intelligence.

Pan, Z., Liang, Y., Wang, W., Yu, Y., Zheng, Y., & Zhang, J. (2019). Urban traffic prediction from spatio-temporal data using deep meta learning. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 1720–1730. https://doi.org/10.1145/3292500.3330884

Ramakrishnan, N., & Soni, T. (2018). Network traffic prediction using recurrent neural networks. 17th IEEE International Conference on Machine Learning and Applications (ICMLA), 187–193. https://doi.org/10.1109/ICMLA.2018.00035

Sarker, I. H. (2021). Deep learning: A comprehensive overview on techniques, taxonomy, applications and research directions. SN Computer Science, 2(6), 420. https://doi.org/10.1007/s42979-021-00815-1

Saxena, D., & Cao, J. (2019). D-GAN: Deep generative adversarial nets for spatio- temporal prediction. ArXiv. Retrieved from https://arxiv.org/pdf/1907.08556.pdf

Shi, X., Qi, H., Shen, Y., Wu, G., & Yin, B. (2020). A spatial-temporal attention approach for traffic prediction. IEEE Transactions on Intelligent Transportation Systems, 22(8), 4909–4918. https://doi.org/10.1109/TITS.2020.2983651

Sun, J., Zhang, J., Li, Q., Yi, X., Liang, Y., & Zheng, Y. (2022). Predicting citywide crowd flows in irregular regions using multi-view graph convolutional networks. IEEE Transactions on Knowledge and Data Engineering, 34(5), 2348–2359. https://doi.org/10.1109/TKDE.2020.3008774

Taye, M. M. (2023). Understanding of machine learning with deep learning: Architectures, workflow, applications and future directions. Computers, 12(5), 91. https://doi.org/10.3390/computers12050091

Triguero, I., Figueredo, G. P., Mesgarpour, M., Garibaldi, J. M., & John, R. I. (2017). Vehicle incident hot spots identification: An approach for big data. 2017 IEEE Trustcom/BigDataSE/ICESS, 901–908. https://doi.org/10.1109/Trustcom/BigDataSE/ICESS.2017.329

Wang, W., Zhao, X., Gong, Z., Chen, Z., Zhang, N., & Wei, W. (2021). An attention-based deep learning framework for trip destination prediction of sharing bike. IEEE Transactions on Intelligent Transportation Systems, 22(7), 4601–4610. https://doi.org/10.1109/TITS.2020.3008935

Wang, X., Guan, X., Cao, J., Zhang, N., & Wu, H. (2020). Forecast network-wide traffic states for multiple steps ahead: A deep learning approach considering dynamic non-local spatial correlation and non-stationary temporal dependency. Transportation Research Part C: Emerging Technologies, 119, 102763. https://doi.org/10.1016/j.trc.2020.102763

Wu, Z., Pan, S., Long, G., Jiang, J., & Zhang, C. (2019). Graph WaveNet for deep spatial-temporal graph modeling. Proceedings of the 28th International Joint Conference on Artificial Intelligence, 1907–1913.

Xiao, G., Wang, R., Zhang, C., & Ni, A. (2021). Demand prediction for a public bike sharing program based on spatio-temporal graph convolutional networks. Multimedia Tools and Applications, 80(15), 22907–22925. https://doi.org/10.1007/s11042-020-08803-y

Xu, C., Ji, J., & Liu, P. (2018). The station-free sharing bike demand forecasting with a deep learning approach and large-scale datasets. Transportation Research Part C: Emerging Technologies, 95, 47–60. https://doi.org/10.1016/j.trc.2018.07.013

Yang, S. (2013). On feature selection for traffic congestion prediction. Transportation Research Part C: Emerging Technologies, 26, 160–169. https://doi.org/10.1016/j.trc.2012.08.005

Yao, H., Tang, X., Wei, H., Zheng, G., & Li, Z. (2018). Revisiting spatial-temporal similarity: A deep learning framework for traffic prediction. ArXiv:1803.01254 [Cs]. http://arxiv.org/abs/1803.01254

Yao, H., Tang, X., Wei, H., Zheng, G., & Li, Z. (2019). Revisiting spatial-temporal similarity: A deep learning framework for traffic prediction. Proceedings of the AAAI Conference on Artificial Intelligence, 33, 5668–5675. https://doi.org/10.1609/aaai.v33i01.33015668

Yao, H., Wu, F., Ke, J., Tang, X., Jia, Y., Lu, S., … & Li, Z. (2019). Deep multi-view spatial-temporal network for taxi demand prediction. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1), 2588–2595.

Yao, W., & Qian, S. (2021). From Twitter to traffic predictor: Next-day morning traffic prediction using social media data. Transportation Research Part C: Emerging Technologies, 124, 102938. https://doi.org/10.1016/j.trc.2020.102938

Yin, X., Wu, G., Wei, J., Shen, Y., Qi, H., & Yin, B. (2021). Multi-stage attention spatial-temporal graph networks for traffic prediction. Neurocomputing, 428, 42–53. https://doi.org/10.1016/j.neucom.2020.11.038

Yu, B., Yin, H., & Zhu, Z. (2018). Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. Proceedings of the 27th International Joint Conference on Artificial Intelligence, 3634–3640. https://doi.org/10.24963/ijcai.2018/505

Yuan, H., Li, G., Bao, Z., & Feng, L. (2021). An effective joint prediction model for travel demands and traffic flows. 2021 IEEE 37th International Conference on Data Engineering (ICDE), 348–359. https://doi.org/10.1109/ICDE51399.2021.00037

Zhang, D., Xiao, F., Shen, M., & Zhong, S. (2021). DNEAT: A novel dynamic node-edge attention network for origin-destination demand prediction. Transportation Research Part C: Emerging Technologies, 122, 102851. https://doi.org/10.1016/j.trc.2020.102851

Zhang, K., Liu, Z., & Zheng, L. (2020). Short-term prediction of passenger demand in multi-zone level: Temporal convolutional neural network with multi-task learning. IEEE Transactions on Intelligent Transportation Systems, 21(4), 1480–1490. https://doi.org/10.1109/TITS.2019.2909571

Zhao, J., Bi, H., & Chen, K. (2022). Identifying land-use types and intensity using traffic volume and traffic event data based on ensemble learning approaches. Paper presented at the Transportation Research Board 101st Annual Meeting, January 9–13, Washington DC.

Zhao, J., Fan, W., & Zhai, X. (2020). Identification of land-use characteristics using bicycle sharing data: A deep learning approach. Journal of Transport Geography, 82, 102562. https://doi.org/10.1016/j.jtrangeo.2019.102562

Zhao, L., Song, Y., Zhang, C., Liu, Y., Wang, P., Lin, T., … & Li, H. (2020). T-GCN: A temporal graph convolutional network for traffic prediction. IEEE Transactions on Intelligent Transportation Systems, 21(9), 3848–3858. https://doi.org/10.1109/TITS.2019.2935152

Zheng, C., Fan, X., Wang, C, & Qi, J. (2020). GMAN: A graph multi-attention network for traffic prediction. Proceedings of the AAAI Conference on Artificial Intelligence, 34(1), 1234-1241.

Zi, W., Xiong, W., Chen, H., & Chen, L. (2021). TAGCN: Station-level demand prediction for bike-sharing system via a temporal attention graph convolution network. Information Sciences, 561, 274–285. https://doi.org/10.1016/j.ins.2021.01.065