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Deep learning for short-term origin–destination passenger flow prediction under partial observability in urban railway systems

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Abstract

Short-term origin–destination (OD) flow prediction is vital for operations planning, control, and management in urban railway systems. While the entry and exit passenger demand prediction problem has been studied in various studies, the OD passenger flow prediction problem receives much less attention. One key challenge for short-term OD flow prediction is the partial observability of the OD flow information due to trips having not been completed at a certain time interval. This paper develops a novel deep learning architecture for the OD flow prediction in urban railway systems and examines various mechanisms for data representation and for dealing with partial information. The deep learning framework consists of three main components, including multiple LSTM networks with an attention mechanism capturing short/long-term temporal dependencies, a temporally shifted graph matrix for spatiotemporal correlations, and a reconstruction mechanism for partial OD flow observations. The model is validated using smart card data from Hong Kong’s Mass Transit Railway (MTR) system and compared with state-of-the-art prediction models. Experiments are designed to examine the characteristics of the proposed approach and its various components. The results show the superior performance (accuracy and robustness) of the proposed model and also the importance of partial observations of OD flow information in improving prediction performance. In terms of data representation, predicting the deviation of OD flows performs consistently better than predicting OD flows directly.

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References

  1. Hao S, Lee DH, Zhao D (2019) Sequence to sequence learning with attention mechanism for short-term passenger flow prediction in large-scale metro system. Transp Res Part C Emerg Technol 107:287–300

    Article  Google Scholar 

  2. Liu Y, Liu Z, Jia R (2019) DeepPF: A deep learning based architecture for metro passenger flow prediction. Transp Res Part C Emerg Technol 101:18–34

    Article  Google Scholar 

  3. Ma X, Zhang J, Du B, Ding C, Sun L (2018) Parallel architecture of convolutional bi-directional LSTM neural networks for network-wide metro ridership prediction. IEEE Trans Intell Transp Syst 20(6):2278–2288

    Article  Google Scholar 

  4. Liu L, Qiu Z, Li G, Wang Q, Ouyang W, Lin L (2019) Contextualized spatial-temporal network for taxi origin-destination demand prediction. IEEE Trans Intell Transp Syst 20(10):3875–3887

    Article  Google Scholar 

  5. Wang Y, Yin H, Chen H, Wo T, Xu J, Zheng K (2019) Origin-destination matrix prediction via graph convolution: a new perspective of passenger demand modeling. In:Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 1227–1235

  6. Anvari S, Tuna S, Canci M, Turkay M (2016) Automated Box-Jenkins forecasting tool with an application for passenger demand in urban rail systems. J Adv Transp 50(1):25–49

    Article  Google Scholar 

  7. Moreira-Matias L, Gama J, Ferreira M, Mendes-Moreira J, Damas L (2013) Predicting taxi-passenger demand using streaming data. IEEE Trans Intell Transp Syst 14(3):1393–1402

    Article  Google Scholar 

  8. Zhang C, Song R, Sun Y (2011) Kalman filter-based short-term passenger flow forecasting on bus stop. J Transp Syst Eng Inf Technol 11(4):154–159

    Google Scholar 

  9. Jiao P, Li R, Sun T, Hou Z, Ibrahim A (2016) Three revised kalman filtering models for short-term rail transit passenger flow prediction. Math Probl Eng

  10. Sun Y, Leng B, Guan W (2015) A novel wavelet-SVM short-time passenger flow prediction in Beijing subway system. Neurocomputing 166:109–121

    Article  Google Scholar 

  11. Ding C, Wang D, Ma X, Li H (2016) Predicting short-term subway ridership and prioritizing its influential factors using gradient boosting decision trees. Sustainability 8(11):1100

    Article  Google Scholar 

  12. Roos J, Bonnevay S, Gavin G (2016) Short-term urban rail passenger flow forecasting: A dynamic bayesian network approach. In: 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA), 1034–1039

  13. Tsai T-H, Lee C-K, Wei C-H (2009) Neural network based temporal feature models for short-term railway passenger demand forecasting. Expert Syst Appl 36(2):3728–3736

    Article  Google Scholar 

  14. Zhao S-Z, Ni T-H, Wang Y, Gao X-T (2011) A new approach to the prediction of passenger flow in a transit system. Comput Math Appl 61(8):1968–1974

    Article  MathSciNet  Google Scholar 

  15. Wei Y, Chen MC (2012) Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks. Transp Res Part C Emerg Technol 21(1):148–162

    Article  Google Scholar 

  16. Guo J, Huang W, Williams BM (2014) Adaptive Kalman filter approach for stochastic short-term traffic flow rate prediction and uncertainty quantification. Transp Res Part C Emerg Technol 43:50–64

    Article  Google Scholar 

  17. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780

    Article  Google Scholar 

  18. Duan Y, Lv Y, Wang F-Y (2016) Travel time prediction with LSTM neural network. In IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), 1053–1058

  19. Ma X, Tao Z, Wang Y, Yu H, Wang Y (2015) Long short-term memory neural network for traffic speed prediction using remote microwave sensor data. Transp Res Part C Emerg Technol 54:187–197

    Article  Google Scholar 

  20. Xu J, Rahmatizadeh R, Bölöni L, Turgut D (2017) Real-time prediction of taxi demand using recurrent neural networks. IEEE Trans Intell Transp Syst 19(8):2572–2581

    Article  Google Scholar 

  21. Yao H, Tang X, Wei H, Zheng G, Li Z (2019) Revisiting spatial-temporal similarity: a deep learning framework for traffic prediction. Proc AAAI Conf Artif Intell 33:5668–5675

    Google Scholar 

  22. LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324

    Article  Google Scholar 

  23. Ke J, Zheng H, Yang H, Chen X (2017) Short-term forecasting of passenger demand under on-demand ride services: A spatio-temporal deep learning approach. Transp Res Part C Emerg Technol 85:591–608

    Article  Google Scholar 

  24. Yao H, Wu F, Ke J, Tang X, Jia Y, Lu S, Gong P, Li Z, Ye J, Chuxing D (2018) Deep multi-view spatial-temporal network for taxi demand prediction. In: 32nd AAAI Conference on Artificial Intelligence, AAAI 2018, 2588–2595

  25. Du B, Peng H, Wang S, Bhuiyan MZA, Wang L, Gong Q, Li J (2019) Deep irregular convolutional residual LSTM for urban traffic passenger flows prediction. IEEE Trans Intell Transp Syst 21(3):972–985

    Article  Google Scholar 

  26. Chu KF, Lam AY, Li VO (2019) Deep multi-scale convolutional LSTM network for travel demand and origin-destination predictions. IEEE Trans Intell Transp Syst 21(8):3219–3232

    Article  Google Scholar 

  27. Ke J, Qin X, Yang H, Zheng Z, Zhu Z, Ye J (2021) Predicting origin-destination ride-sourcing demand with a spatio-temporal encoder-decoder residual multi-graph convolutional network. Transp Res Part C Emerg Technol 122:102858

    Article  Google Scholar 

  28. Noursalehi P, Koutsopoulos HN, Zhao J (2021) Dynamic origin-destination prediction in urban rail systems: a multi-resolution spatio-temporal deep learning approach. IEEE Trans Intell Transp Syst

  29. Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. In International Conference on Learning Representations (ICLR), 2017

  30. Yu B, Yin H, Zhu Z (2018) Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI-18), 2018

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

  32. 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. Proc AAAI Conf Artif Intell 33:3656–3663

    Google Scholar 

  33. Guo S, Lin Y, Feng N, Song C, Wan H (2019) Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. Proc AAAI Conf Artif Intell 33:922–929

    Google Scholar 

  34. Noursalehi P, Koutsopoulos HN, Zhao J (2018) Real time transit demand prediction capturing station interactions and impact of special events. Transp Res Part C Emerg Technol 97:277–300

    Article  Google Scholar 

  35. Paszke A, Gross S, Chintala S, Chanan G, Yang E, DeVito Z, Lin Z, Desmaison A, Antiga L, Lerer A (2017) Automatic differentiation in pytorch

  36. Luong MT, Pham H, Manning CD (2015) Effective approaches to attention-based neural machine translation. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, 2015

  37. Zhang K, Liu Z, Zheng L (2019) Short-term prediction of passenger demand in multi-zone level: Temporal convolutional neural network with multi-task learning. IEEE Trans Intell Transp Syst 21(4):1480–1490

    Article  Google Scholar 

  38. Kamarianakis Y, Prastacos P (2003) Forecasting traffic flow conditions in an urban network: Comparison of multivariate and univariate approaches. Transp Res Record 1857(1):74–84

    Article  Google Scholar 

  39. Zhang Z, Li M, Lin X, Wang Y, He F (2019) Multistep speed prediction on traffic networks: a deep learning approach considering spatio-temporal dependencies. Transp Res Part C Emerg Technol 105:297–322

    Article  Google Scholar 

  40. Gers FA, Eck D, Schmidhuber J (2002) Applying LSTM to time series predictable through time-window approaches. In Neural Nets WIRN Vietri-01, 193–200

  41. Rodrigues F, Markou I, Pereira FC (2019) Combining time-series and textual data for taxi demand prediction in event areas: A deep learning approach. Information Fusion 49:120–129

    Article  Google Scholar 

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Acknowledgements

The authors would like to thank MTR, Hong Kong for providing the data, the Massachusetts Green High-Performance Computing Center (MGHPCC) for support on computing. The research is supported by the Monash Faculty of Engineering travel grant and Monash seed funding.

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Authors and Affiliations

  1. Department of Civil Engineering, Monash University, 23 College Walk, Clayton, VIC, 3168, Australia

    Wenhua Jiang & Zhenliang Ma

  2. Department of Civil and Environmental Engineering, Northeastern University, 400 Snell Engineering, Boston, MA, 02115, United States

    Haris N. Koutsopoulos

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  1. Wenhua Jiang
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  2. Zhenliang Ma
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  3. Haris N. Koutsopoulos
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Correspondence to Zhenliang Ma.

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Jiang, W., Ma, Z. & Koutsopoulos, H.N. Deep learning for short-term origin–destination passenger flow prediction under partial observability in urban railway systems. Neural Comput & Applic 34, 4813–4830 (2022). https://doi.org/10.1007/s00521-021-06669-1

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