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Research Article

How spatial features affect urban rail transit prediction accuracy: a deep learning based passenger flow prediction method

ORCID Icon, , , , &
Received 19 Oct 2021, Accepted 01 Nov 2023, Published online: 12 Nov 2023

References

  • Abdulazim, T., Abdelgawad, H., Habib, K. N., & Abdulhai, B. (2015). Framework for automating travel activity inference using land use data: The case of foursquare in the greater Toronto and Hamilton Area, Ontario, Canada. Transportation Research Record: Journal of the Transportation Research Board, 2526(1), 136–142. https://doi.org/10.3141/2526-15
  • Cervero, R., & Kockelman, K. (1997). Travel demand and the 3Ds: Density, diversity, and design. Transportation Research Part D: Transport and Environment, 2(3), 199–219. https://doi.org/10.1016/S1361-9209(97)00009-6
  • Chakraborty, A., & Mishra, S. (2013). Land use and transit ridership connections: Implications for state-level planning agencies. Land Use Policy, 30(1), 458–469. https://doi.org/10.1016/j.landusepol.2012.04.017
  • Chang, X., Feng, Z., Wu, J., Sun, H., Wang, G., & Bao, X. (2022). Understanding and predicting the short-term passenger flow of station-free shared bikes: A spatiotemporal deep learning approach. IEEE Intelligent Transportation Systems Magazine, 14(4), 73–85. https://doi.org/10.1109/MITS.2021.3049362
  • Chang, X., Wu, J., Sun, H., Correia, G. H., de, A., & Chen, J. (2021). Relocating operational and damaged bikes in free-floating systems: A data-driven modeling framework for level of service enhancement. Transportation Research Part A: Policy and Practice, 153, 235–260. https://doi.org/10.1016/j.tra.2021.09.010
  • Chen, E., Ye, Z., Wang, C., & Zhang, W. (2019). Discovering the spatio-temporal impacts of built environment on metro ridership using smart card data. Cities, 95, 102359. https://doi.org/10.1016/j.cities.2019.05.028
  • Cheng, L., Jin, T., Wang, K., Lee, Y., & Witlox, F. (2022). Promoting the integrated use of bikeshare and metro: A focus on the nonlinearity of built environment effects. Multimodal Transportation, 1(1), 100004. https://doi.org/10.1016/j.multra.2022.100004
  • Cho, K., van Merrienboer, B., Bahdanau, D., & Bengio, Y. (2014). On the properties of neural machine translation: Encoder-decoder approaches. ArXiv:1409.1259 [Cs, Stat]. http://arxiv.org/abs/1409.1259
  • Dong, H., Jia, L., Sun, X., Li, C., & Qin, Y. (2009). Road traffic flow prediction with a time-oriented ARIMA model [Paper presentation]. 2009 Fifth International Joint Conference on INC, IMS and IDC, pp. 1649–1652. https://doi.org/10.1109/NCM.2009.224
  • Gan, Z., Yang, M., Feng, T., & Timmermans, H. J. P. (2020). Examining the relationship between built environment and metro ridership at station-to-station level. Transportation Research Part D: Transport and Environment, 82, 102332. https://doi.org/10.1016/j.trd.2020.102332
  • Goodfellow, L., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press. http://www.deeplearningbook.org
  • Han, Y., Wang, S., Ren, Y., Wang, C., Gao, P., & Chen, G. (2019). Predicting station-level short-term passenger flow in a citywide metro network using spatiotemporal graph convolutional neural networks. ISPRS International Journal of Geo-Information, 8(6), 243. https://doi.org/10.3390/ijgi8060243
  • Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
  • Jiao, P., Li, R., Sun, T., Hou, Z., & Ibrahim, A. (2016). Three revised Kalman filtering models for short-term rail transit passenger flow prediction. Mathematical Problems in Engineering, 2016, e9717582–10. https://doi.org/10.1155/2016/9717582
  • Kang, D., Lv, Y., & Chen, Y. (2017). Short-term traffic flow prediction with LSTM recurrent neural network. In 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), pp. 1–6. https://doi.org/10.1109/itsc.2017.8317872
  • Kepaptsoglou, K., Stathopoulos, A., & Karlaftis, M. G. (2017). Ridership estimation of a new LRT system: Direct demand model approach. Journal of Transport Geography, 58, 146–156. https://doi.org/10.1016/j.jtrangeo.2016.12.004
  • Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. Learning.
  • Kipf, T. N., & Welling, M. (2017). Semi-supervised classification with graph convolutional networks. ArXiv:1609.02907 [Cs, Stat]. http://arxiv.org/abs/1609.02907
  • Li, Z., Xu, H., Gao, X., Wang, Z., & Xu, W. (2022). Fusion attention mechanism bidirectional LSTM for short-term traffic flow prediction. Journal of Intelligent Transportation Systems, 0(0), 1–14. https://doi.org/10.1080/15472450.2022.2142049
  • Liu, Y., Liu, Z., & Jia, R. (2019). DeepPF: A deep learning based architecture for metro passenger flow prediction. Transportation Research Part C: Emerging Technologies, 101, 18–34. https://doi.org/10.1016/j.trc.2019.01.027
  • Ma, X., Zhang, J., Du, B., Ding, C., & Sun, L. (2019). Parallel architecture of convolutional bi-directional LSTM neural networks for network-wide metro ridership prediction. IEEE Transactions on Intelligent Transportation Systems, 20(6), 2278–2288. https://doi.org/10.1109/TITS.2018.2867042
  • Nigam, A., & Srivastava, S. (2023). Hybrid deep learning models for traffic stream variables prediction during rainfall. Multimodal Transportation, 2(1), 100052. https://doi.org/10.1016/j.multra.2022.100052
  • Pan, H., Li, J., Shen, Q., & Shi, C. (2017). What determines rail transit passenger volume? Implications for transit oriented development planning. Transportation Research Part D: Transport and Environment, 57, 52–63. https://doi.org/10.1016/j.trd.2017.09.016
  • Roos, J., Gavin, G., & Bonnevay, S. (2017). A dynamic Bayesian network approach to forecast short-term urban rail passenger flows with incomplete data. Transportation Research Procedia, 26, 53–61. https://doi.org/10.1016/j.trpro.2017.07.008
  • Shi, X., Chen, Z., Wang, H., Yeung, D.-Y., Wong, W., & Woo, W. (2015). Convolutional LSTM network: A machine learning approach for precipitation nowcasting. In Advances in Neural Information Processing Systems, pp. 802–810.
  • Soczówka, P., Kłos, M. J., Żochowska, R., & Sobota, A. (2021). An analysis of the influence of travel time on access time in public transport. Scientific Journal of Silesian University of Technology. Series Transport, 111, 137–149. https://doi.org/10.20858/sjsutst.2021.111.12
  • Sun, Y., Leng, B., & Guan, W. (2015). A novel wavelet-SVM short-time passenger flow prediction in Beijing subway system. Neurocomputing, 166, 109–121. https://doi.org/10.1016/j.neucom.2015.03.085
  • Sung, H., & Oh, J.-T. (2011). Transit-oriented development in a high-density city: Identifying its association with transit ridership in Seoul, Korea. Cities, 28(1), 70–82. https://doi.org/10.1016/j.cities.2010.09.004
  • Tang, Q., Yang, M., & Yang, Y. (2019). ST-LSTM: A deep learning approach combined spatio-temporal features for short-term forecast in rail transit. Journal of Advanced Transportation, 2019, 1–8. https://doi.org/10.1155/2019/8392592
  • Taylor, B. D., Fink, C. N. Y. (2003). The factors influencing transit ridership: A review and analysis of the ridership literature. University of California Transportation Center Working Papers. https://escholarship.org/uc/item/3xk9j8m2
  • Wu, J., Liu, M., Sun, H., Li, T., Gao, Z., & Wang, D. Z. W. (2015). Equity-based timetable synchronization optimization in urban subway network. Transportation Research Part C: Emerging Technologies, 51, 1–18. https://doi.org/10.1016/j.trc.2014.11.001
  • Ye, J., Zhao, J., Ye, K., & Xu, C. (2020). Multi-STGCnet: A graph convolution based spatial-temporal framework for subway passenger flow forecasting [Paper presentation]. 2020 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. https://doi.org/10.1109/IJCNN48605.2020.9207049
  • Yuan, Z., Zhou, X., & Yang, T. (2018). Hetero-ConvLSTM: A deep learning approach to traffic accident prediction on heterogeneous spatio-temporal data [Paper presentation]. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 984–992. https://doi.org/10.1145/3219819.3219922
  • Zhang, J., Chen, F., Guo, Y., & Li, X. (2020). Multi-graph convolutional network for short-term passenger flow forecasting in urban rail transit. IET Intelligent Transport Systems, 14(10), 1210–1217. https://doi.org/10.1049/iet-its.2019.0873
  • Zhang, J., Chen, F., & Shen, Q. (2019). Cluster-based LSTM network for short-term passenger flow forecasting in urban rail transit. IEEE Access, 7, 147653–147671. https://doi.org/10.1109/ACCESS.2019.2941987
  • Zhang, J., Zheng, Y., & Qi, D. (2017). Deep spatio-temporal residual networks for citywide crowd flows prediction. ArXiv:1610.00081 [Cs]. http://arxiv.org/abs/1610.00081
  • Zhao, J., Deng, W., Song, Y., & Zhu, Y. (2013). What influences Metro station ridership in China? Insights from Nanjing. Cities, 35, 114–124. https://doi.org/10.1016/j.cities.2013.07.002
  • Zhao, J., Zhang, R., Sun, Q., Shi, J., Zhuo, F., & Li, Q. (2023). Adaptive graph convolutional network-based short-term passenger flow prediction for metro. Journal of Intelligent Transportation Systems. Advance online publication. https://doi.org/10.1080/15472450.2023.2209913
  • Zhao, L., Song, Y., Zhang, C., Liu, Y., Wang, P., Lin, T., Deng, M., & 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

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