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Published in: Transportation 6/2023

01-08-2022

Predictability of short-term passengers’ origin and destination demands in urban rail transit

Authors: Fang Yang, Chunyan Shuai, Qian Qian, Wencong Wang, Mingwei He, Min He, Jaeyoung Lee

Published in: Transportation | Issue 6/2023

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Abstract

Accurate prediction of short-term passengers’ origin and destination (OD) demands is key to efficient operation and management of urban rail transit (URT), especially in the case of congestion or an incident. However, short-term OD demand forecasting is more challenging than passenger flow forecasting, due to its uncertainty and high dimensions. So far, most OD prediction models capture the spatio-temporal dependencies of OD flow by means of training models on historical data, but what characteristics and laws influence the performance of OD prediction are still unknown. In this paper, we propose temporal Pearson correlation coefficients and approximate entropy, as well as spatial correlations, as indicators to reflect the inherent time–space correlations and complexity of the OD flow. Then, by analyzing automatic fare collection data of the Beijing and Shanghai URT system, this paper deeply discusses the relationships between the spatio-temporal correlations and complexity of the OD flow and the predictive performances of different models with regard to different intervals. Finally, this paper proposes the predictable problem of travel demands and points out that the spatial correlations of the OD matrix are more important than the temporal correlations and complexity in the short-term prediction of travel demands. In particular, the number of principal components of the OD flow can be a key indicator to measure the forecasting performance of a model. A reasonable interval is very important for short-term OD forecasting, and in the Beijing URT system, 30 min is a preferable choice for workdays and 50 min for weekends. All these findings are beneficial to guide users to build a suitable model or improve the existing model to obtain better prediction performances.

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Literature
go back to reference Cunningham, J.P., Ghahramani, Z.: Linear dimensionality reduction: survey, insights, and generalizations. J. Mach. Learn. Res. 16(1), 2859–2900 (2015) Cunningham, J.P., Ghahramani, Z.: Linear dimensionality reduction: survey, insights, and generalizations. J. Mach. Learn. Res. 16(1), 2859–2900 (2015)
go back to reference Feng, J., Lin, Z.Q., Xia, T., Sun, F.: A sequential convolution network for population flow prediction with explicitly correlation modelling. In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence. (2020). Feng, J., Lin, Z.Q., Xia, T., Sun, F.: A sequential convolution network for population flow prediction with explicitly correlation modelling. In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence. (2020).
go back to reference Luo, X.L., Li, D.Y., Yang, Y., Zhang, S.R.: Spatiotemporal traffic flow prediction with KNN and LSTM. J. Adv. Transp. 2019, 1–10 (2019) Luo, X.L., Li, D.Y., Yang, Y., Zhang, S.R.: Spatiotemporal traffic flow prediction with KNN and LSTM. J. Adv. Transp. 2019, 1–10 (2019)
go back to reference Smith, B.L., Demetsky, M.J.: Traffic flow forecasting comparison of modeling approches. J. Transp. Eng.-Asce. 123(4), 261–266 (1997)CrossRef Smith, B.L., Demetsky, M.J.: Traffic flow forecasting comparison of modeling approches. J. Transp. Eng.-Asce. 123(4), 261–266 (1997)CrossRef
go back to reference Starczewski, J., Grzesica, D., Jirsa, V.: Modelling bicycle demand using autoregressive and moving average models. IOP Conference Series: Materials Science and Engineering. 471: 062038(2019) Starczewski, J., Grzesica, D., Jirsa, V.: Modelling bicycle demand using autoregressive and moving average models. IOP Conference Series: Materials Science and Engineering. 471: 062038(2019)
go back to reference Toqué, F., Côme, E., El Mahrsi, M.K., Oukhellou, L.: Forecasting dynamic public transport origin-destination matrices with long-short term memory recurrent neural networks. In: International Conference on Intelligent Transportation Systems. (2016). https://doi.org/10.1109/ITSC.2016.7795689 Toqué, F., Côme, E., El Mahrsi, M.K., Oukhellou, L.: Forecasting dynamic public transport origin-destination matrices with long-short term memory recurrent neural networks. In: International Conference on Intelligent Transportation Systems. (2016). https://​doi.​org/​10.​1109/​ITSC.​2016.​7795689
go back to reference Wang, Y.D., Yin, H.Z., Chen, H.X., Wo, T.Y, Xu, J., Zheng, K.: Origin-destination matrix prediction via graph convolution: a new perspective of passenger demand modeling. In: Knowledge Discovery and Data Mining. (2019). Wang, Y.D., Yin, H.Z., Chen, H.X., Wo, T.Y, Xu, J., Zheng, K.: Origin-destination matrix prediction via graph convolution: a new perspective of passenger demand modeling. In: Knowledge Discovery and Data Mining. (2019).
go back to reference Woo, S., Tak, S., Yeo, H.: Data-driven prediction methodology of origin-destination demand in large network for real-time service. Transp. Res. Rec. J. Transp. Res. Board. 2567, 47–56 (2016)CrossRef Woo, S., Tak, S., Yeo, H.: Data-driven prediction methodology of origin-destination demand in large network for real-time service. Transp. Res. Rec. J. Transp. Res. Board. 2567, 47–56 (2016)CrossRef
go back to reference Yu, B., Yin, B., Zhu, Z.: Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting. In: Twenty-Seventh International Joint Conference on Artificial Intelligence IJCAI-18. (2018) Yu, B., Yin, B., Zhu, Z.: Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting. In: Twenty-Seventh International Joint Conference on Artificial Intelligence IJCAI-18. (2018)
go back to reference Yuan, H., Li, G.L.: A survey of traffic prediction: from spatio-temporal data to intelligent transportation. Data Sci. Eng. 6(1), 63–85 (2021)CrossRef Yuan, H., Li, G.L.: A survey of traffic prediction: from spatio-temporal data to intelligent transportation. Data Sci. Eng. 6(1), 63–85 (2021)CrossRef
go back to reference Zhang, J.L., Feng, C., Wang, Z.J.: Short-term origin-destination forecasting in urban rail transit based on attraction degree. IEEE Access. 7133452–133462 (2019b) Zhang, J.L., Feng, C., Wang, Z.J.: Short-term origin-destination forecasting in urban rail transit based on attraction degree. IEEE Access. 7133452–133462 (2019b)
Metadata
Title
Predictability of short-term passengers’ origin and destination demands in urban rail transit
Authors
Fang Yang
Chunyan Shuai
Qian Qian
Wencong Wang
Mingwei He
Min He
Jaeyoung Lee
Publication date
01-08-2022
Publisher
Springer US
Published in
Transportation / Issue 6/2023
Print ISSN: 0049-4488
Electronic ISSN: 1572-9435
DOI
https://doi.org/10.1007/s11116-022-10313-9

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