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Dynamic Relational Graph Convolutional Network for Metro Passenger Flow Forecasting

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Abstract

Due to the complex network structure and time-varying spatial relationships inherent in metro station systems, coupled with the influence of historical time series, station-level passenger flow forecasting tasks are challenging to solve by traditional prediction models. Therefore, we propose a dynamic relational graph convolutional network that includes a spatial-temporal framework to forecast passenger flow. Regarding spatial convolution, apart from physical and similarity graphs, we introduce accessibility graphs, which utilize the origin–destination (OD) matrix and the OD path consumption time, into graph convolutional networks. Then, dynamic graph convolution by the long short-term memory (LSTM) model is implemented with similarity graphs and accessibility graphs. Within the temporal convolution, the temporal convolutional network (TCN) module is implemented to convolve historical passenger flow to acquire passenger flow in the near future. Moreover, an automatic fare collection (AFC) dataset of the Chongqing metro system is adopted to validate the effectiveness of our proposed model with the baselines. The experimental results confirm that our model outperforms the baselines and show the effectiveness of the dynamic multirelationship and the TCN module.

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Data available on request from the authors.

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References

  1. Zhou Y, Yang H, Wang Y, Yan X (2021) Integrated line configuration and frequency determination with passenger path assignment in urban rail transit networks. Transp Res B Methodol 145:134–151

    Article  Google Scholar 

  2. Yin J, D’Ariano A, Wang Y, Yang L, Tang T (2021) Timetable coordination in a rail transit network with time-dependent passenger demand. Eur J Oper Res 295(1):183–202

    Article  Google Scholar 

  3. Zhang Y, D’Ariano A, He B, Peng Q (2019) Microscopic optimization model and algorithm for integrating train timetabling and track maintenance task scheduling. Transp Res B Methodol 127:237–278

    Article  Google Scholar 

  4. Wang Y, D’Ariano A, Yin J, Meng L, Tang T, Ning B (2018) Passenger demand oriented train scheduling and rolling stock circulation planning for an urban rail transit line. Transp Res B Methodol 118:193–227

    Article  Google Scholar 

  5. Wick F, Kerzel U, Hahn M, Wolf M, Singhal T, Stemmer D, Ernst J, Feindt M (2021) Demand forecasting of individual probability density functions with machine learning. In: Operations Research Forum, vol. 2, pp. 1–39. Springer

  6. Zhao J, Qu Q, Zhang F, Xu C, Liu S (2017) Spatio-temporal analysis of passenger travel patterns in massive smart card data. IEEE Trans Intell Transp Syst 18(11):3135–3146

    Article  Google Scholar 

  7. Li H, Wang Y, Xu X, Qin L, Zhang H (2019) Short-term passenger flow prediction under passenger flow control using a dynamic radial basis function network. Appl Soft Comput 83:105620

    Article  Google Scholar 

  8. Lei Y, Lu G, Zhang H, He B, Fang J (2022) Optimizing total passenger waiting time in an urban rail network: a passenger flow guidance strategy based on a multi-agent simulation approach. Simul Model Pract Theory 117:102510

    Article  Google Scholar 

  9. Zhang H, He B, Lu G, Zhu Y (2022) A simulation and machine learning based optimization method for integrated pedestrian facilities planning and staff assignment problem in the multi-mode rail transit transfer station. Simul Model Pract Theory 115:102449

    Article  Google Scholar 

  10. Cacchiani V, Qi J, Yang L (2020) Robust optimization models for integrated train stop planning and timetabling with passenger demand uncertainty. Transp Res B Methodol 136:1–29

    Article  Google Scholar 

  11. Zemkoho A (2023) A basic time series forecasting course with python. In: Operations Research Forum, vol. 4, pp. 1–43. Springer

  12. Lee S, Fambro DB (1999) Application of subset autoregressive integrated moving average model for short-term freeway traffic volume forecasting. Transp Res Rec 1678(1):179–188

    Article  Google Scholar 

  13. Ding C, Duan J, Zhang Y, Wu X, Yu G (2017) Using an ARIMA-GARCH modeling approach to improve subway short-term ridership forecasting accounting for dynamic volatility. IEEE Trans Intell Transp Syst 19(4):1054–1064

    Article  Google Scholar 

  14. Liu Y, Wu H (2017) Prediction of road traffic congestion based on random forest. In: 2017 10th International Symposium on Computational Intelligence and Design (ISCID), vol. 2, pp. 361–364. IEEE

  15. Wen K, Zhao G, He B, Ma J, Zhang H (2021) A decomposition-based forecasting method with transfer learning for railway short-term passenger flow in holidays. Expert Syst Appl 116102

  16. Castro-Neto M, Jeong Y-S, Jeong M-K, Han LD (2009) Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions. Expert Syst Appl 36(3):6164–6173

    Article  Google Scholar 

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

    Article  Google Scholar 

  18. 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 

  19. Jing Y, Hu H, Guo S, Wang X, Chen F (2021) Short-term prediction of urban rail transit passenger flow in external passenger transport hub based on LSTM-LGB-DRS. IEEE Trans Intell Transp Syst 22(7):4611–4621

    Article  Google Scholar 

  20. Bruna J, Zaremba W, Szlam A, Lecun Y (2014) Spectral networks and locally connected networks on graphs. In: International Conference on Learning Representations (ICLR2014), CBLS, April 2014, p

  21. Wang J, Deng W, Guo Y (2014) New Bayesian combination method for short-term traffic flow forecasting. Transp Res Part C Emerg Technol 43:79–94

    Article  Google Scholar 

  22. Polson NG, Sokolov VO (2017) Deep learning for short-term traffic flow prediction. Transp Res Part C Emerg Technol 79:1–17

    Article  Google Scholar 

  23. Julio N, Giesen R, Lizana P (2016) Real-time prediction of bus travel speeds using traffic shockwaves and machine learning algorithms. Res Transp Econ 59:250–257

    Article  Google Scholar 

  24. Jepsen TS, Jensen CS, Nielsen TD (2020) Relational fusion networks: graph convolutional networks for road networks. IEEE Trans Intell Transp Syst PP(99)1–12

  25. He Y, Li L, Zhu X, Tsui KL (2022) Multi-graph convolutional-recurrent neural network (MGC-RNN) for short-term forecasting of transit passenger flow. IEEE Trans Intell Transp Syst 23(10):18155–18174

    Article  Google Scholar 

  26. Tang L, Zhao Y, Cabrera J, Ma J, Tsui KL (2018) Forecasting short-term passenger flow: an empirical study on Shenzhen Metro. IEEE Trans Intell Transp Syst 20(10):3613–3622

    Article  Google Scholar 

  27. Li F, Feng J, Yan H, Jin G, Yang F, Sun F, Jin D, Li Y (2021) Dynamic graph convolutional recurrent network for traffic prediction: benchmark and solution. ACM Trans Knowl Discov Data (TKDD)

  28. Chen L, Shao W, Lv M, Chen W, Zhang Y, Yang C (2022) AARGNN: an attentive attributed recurrent graph neural network for traffic flow prediction considering multiple dynamic factors. IEEE Trans Intell Transp Syst 23(10):17201–17211

    Article  Google Scholar 

  29. Wang Y, Zheng J, Du Y, Huang C, Li P (2022) Traffic-GGNN: predicting traffic flow via attentional spatial-temporal gated graph neural networks. IEEE Trans Intell Transp Syst 23(10):18423–18432

    Article  Google Scholar 

  30. Zhang Y, Wang S, Chen B, Cao J, Huang Z (2021) TrafficGAN: network-scale deep traffic prediction with generative adversarial nets. IEEE Trans Intell Transp Syst 22(1):219–230

    Article  Google Scholar 

  31. Zhang X, Huang C, Xu Y, Xia L, Dai P, Bo L, Zhang J, Zheng Y (2021) Traffic flow forecasting with spatial-temporal graph diffusion network. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 15008–15015

  32. Li T, Wang B, Zhou M, Zhang L, Zhao X (2018) Short-term load forecasting using optimized LSTM networks based on EMD. In: 2018 10th International Conference on Communications, Circuits and Systems (ICCCAS), pp. 84–88

  33. Liu J, Guan W (2004) A summary of traffic flow forecasting methods. Journal of Highway and Transportation Research And Development 21(3):82–85

    Google Scholar 

  34. Bai S, Kolter JZ, Koltun V (2018) An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271

  35. Yu D, Liu Y, Yu X (2016) A data grouping CNN algorithm for short-term traffic flow forecasting. In: Web Technologies and Applications: 18th Asia-Pacific Web Conference, APWeb 2016, Suzhou, China, September 23-25, 2016. Proceedings, Part I, pp. 92–103. Springer

  36. Zheng Z, Yang Y, Liu J, Dai H-N, Zhang Y (2019) Deep and embedded learning approach for traffic flow prediction in urban informatics. IEEE Trans Intell Transp Syst 20(10):3927–3939

    Article  Google Scholar 

  37. Guo S, Lin Y, Li S, Chen Z, Wan H (2019) Deep spatial-temporal 3D convolutional neural networks for traffic data forecasting. IEEE Trans Intell Transp Syst 20(10):3913–3926

    Article  Google Scholar 

  38. Guo S, Lin Y, Feng N, Song C, Wan H (2019) Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 33:922–929

    Article  Google Scholar 

  39. Li D, Lasenby J (2021) Spatiotemporal attention-based graph convolution network for segment-level traffic prediction. IEEE Trans Intell Transp Syst 23(7):8337–8345

    Article  Google Scholar 

  40. 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, pp. 3634–3640

  41. Zhao L, Song Y, Zhang C, Liu Y, Wang P, Lin T, Deng M, Li H (2019) T-GCN: a temporal graph convolutional network for traffic prediction. IEEE Trans Intell Transp Syst 21(9):3848–3858

    Article  Google Scholar 

  42. Schlichtkrull M, Kipf TN, Bloem P, Van Den Berg R, Titov I, Welling M (2018) Modeling relational data with graph convolutional networks. In: The Semantic Web: 15th International Conference, ESWC 2018, Heraklion, Crete, Greece, June 3–7, 2018, Proceedings 15, pp. 593–607. Springer

  43. 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 33(01):3656–3663

  44. Lv M, Hong Z, Chen L, Chen T, Zhu T, Ji S (2020) Temporal multi-graph convolutional network for traffic flow prediction. IEEE Trans Intell Transp Syst 22(6):3337–3348

    Article  Google Scholar 

  45. Lee K, Rhee W (2022) DDP-GCN: multi-graph convolutional network for spatiotemporal traffic forecasting. Transp Res Part C Emerg Technol 134:103466

    Article  Google Scholar 

  46. Pareja A, Domeniconi G, Chen J, Ma T, Suzumura T, Kanezashi H, Kaler T, Schardl T, Leiserson C (2020) EvolveGCN: evolving graph convolutional networks for dynamic graphs 34(04), 5363–5370

  47. Li M, Zhu Z (2021) Spatial-temporal fusion graph neural networks for traffic flow forecasting 35(5), 4189–4196

  48. Guo K, Hu Y, Qian Z, Liu H, Zhang K, Sun Y, Gao J, Yin B (2020) Optimized graph convolution recurrent neural network for traffic prediction. IEEE Trans Intell Transp Syst 22(2):1138–1149

    Article  Google Scholar 

  49. Wang J, Zhang Y, Wei Y, Hu Y, Piao X, Yin B (2021) Metro passenger flow prediction via dynamic hypergraph convolution networks. IEEE Trans Intell Transp Syst 22(12):7891–7903

    Article  Google Scholar 

  50. Chen P, Fu X, Wang X (2021) A graph convolutional stacked bidirectional unidirectional-LSTM neural network for metro ridership prediction. IEEE Trans Intell Transp Syst 23(7):6950–6962

    Article  Google Scholar 

  51. Yin D, Jiang R, Deng J, Li Y, Xie Y, Wang Z, Zhou Y, Song X, Shang J (2022) MTMGNN: multi-time multi-graph neural network for metro passenger flow prediction. GeoInformatica 27:1–29

    Google Scholar 

  52. Shen J, Huang W, Zhu D, Liang J (2017) A novel similarity measure model for multivariate time series based on LMNN and DTW. Neural Process Lett 45:925–937

    Article  Google Scholar 

  53. 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, pp. 397–400

  54. Chen T, Guestrin C (2016) XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, pp. 785–794

  55. Baro J, Khouadjia M (2021) Passenger flow forecasting on transportation network: sensitivity analysis of the spatiotemporal features. In: 2021 International Conference on Data Mining Workshops (ICDMW), pp. 734–741. IEEE

  56. Wilcoxon F (1992) In: Kotz S, Johnson NL (eds.) Individual comparisons by ranking methods, pp. 196–202. Springer, New York, NY

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Funding

This work is supported by the National Key Research and Development Program of China (No. 2022YFB4300503), the National Natural Science Foundation of China (No. 61603317,52005082), and the National Natural Science Foundation of Sichuan Province (No. 2022NSFSC0465, 2022NSFSC0397). The first author is deeply grateful for the financial support from the China Scholarship Council (201707005054).

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Authors and Affiliations

  1. School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, 610031, Sichuan, China

    Bisheng He & Yongjun Zhu

  2. National United Engineering Laboratory of Integrated and Intelligent Transportation, Southwest Jiaotong University, 611756, Chengdu, China

    Bisheng He

  3. China Mobile (Hangzhou) Information Technology Co., Ltd, 311100, Hangzhou, China

    Yongjun Zhu

  4. Department of Engineering, Roma Tre University, Via della Vasca Navale 79, Rome, 00146, Italy

    Andrea D’Ariano

  5. Department of Economics and Management, China Railway Economic and Planning Research Institute, Beijing, 100038, China

    Keyu Wen

  6. School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, 611756, Sichuan, China

    Lufeng Chen

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Contributions

BH: conceptualization, methodology, writing—original draft. YZ: data curation, methodology, writing—original draft. AD’A: methodology, writing—review, editing, supervision. KW: methodology, validation, investigation. LC: methodology, validation, writing—original draft, investigation.

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Correspondence to Lufeng Chen.

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He, B., Zhu, Y., D’Ariano, A. et al. Dynamic Relational Graph Convolutional Network for Metro Passenger Flow Forecasting. Oper. Res. Forum 4, 85 (2023). https://doi.org/10.1007/s43069-023-00266-9

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