Forecasting short-term passenger flow on a bus route: a splitting–integrating method based on passenger travel behavior
Abstract
Short-term passenger flow forecasting is the key to implement real-time dynamic dispatching of buses, which can meet the travel time requirement of passengers with different attributes. In practice, it is difficult to obtain passenger attribute information due to the restriction of bus information systems or other conditions. This article proposes a new perspective on identifying passenger attribute information, that is, the correlation between the bus card number and the travel time is used to analyse passenger travel behaviour. Then using the travel frequency as the splitting boundary, the passenger set is split into different types of subsets, which are predicted by different methods. The total forecast values are obtained by integration, so as to explore the effectiveness of the passenger attribute identification and splitting–integrating method. The result shows that: (1) compared with the forecasting method without considering the passenger travel behaviour, the performance of splitting–integrating method is better, and the passenger attribute identification method is effective; (2) the value of the splitting boundary will affect the size and consistency of the subset, and the optimal value can be sought according to forecast results; (3) different types of subsets should be treated by different forecasting models and combination paths.
Keyword : short-term passenger flow forecasting, bus passenger flow, passenger attribute, passenger travel behaviour, splitting–integrating method
How to Cite
Fang, X., Lin, M., Chen, W., & Pan, X. (2025). Forecasting short-term passenger flow on a bus route: a splitting–integrating method based on passenger travel behavior. Transport, 40(1), 12–23. https://doi.org/10.3846/transport.2025.20517
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Du, J.; Aultman-Hall, L. 2007. Increasing the accuracy of trip rate information from passive multi-day GPS travel datasets: automatic trip end identification issues, Transportation Research Part A: Policy and Practice 41(3): 220–232. https://doi.org/10.1016/j.tra.2006.05.001
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Lu, X. Q. 2016. Bus Passenger Flow Analysis and Prediction Based on Transport IC Card Big Data. MSc Thesis. Guangdong University of Technology, Guangzhou, Guangdong, China. (in Chinese).
Ma, H. H.; Guo, Q. R.; Ding, C. C. 2016. Eviews Statistical Analysis and Application. China: Beijing. (in Chinese).
Meng, P.-C.; Li, X.-Y.; Jia, H.-F.; Li, Y.-Z. 2018. Short-time rail transit passenger flow real-time prediction based on moving average, Journal of Jilin University (Engineering and Technology Edition) (2): 448–453. https://doi.org/10.13229/j.cnki.jdxbgxb20161256 (in Chinese).
Qiu, D.-G.; Yang, H.-Y. 2013. A short-term traffic flow forecast algorithm based on double seasonal time series, Journal of Sichuan University (Engineering Science Edition) (5): 64–68. https://doi.org/10.15961/j.jsuese.2013.05.001 (in Chinese).
Sfetsos, A.; Siriopoulos, C. 2004. Combinatorial time series forecasting based on clustering algorithms and neural networks, Neural Computing & Applications 13(1): 56–64. https://doi.org/10.1007/s00521-003-0391-y
Smith, B. L.; Williams, B. M.; Oswald, R. K. 2002. Comparison of parametric and nonparametric models for traffic flow forecasting, Transportation Research Part C: Emerging Technologies 10(4): 303–321. https://doi.org/10.1016/S0968-090X(02)00009-8
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Wang, Y.; Han, B.-M.; Zhang, Q.; Li, D.-W. 2015b. Forecasting of entering passenger flow volume in Beijing subway based on SARIMA model, Journal of Transportation Systems Engineering and Information Technology (6): 205–211. https://doi.org/10.16097/j.cnki.1009-6744.2015.06.031 (in Chinese).
Wang, P.; Wu, C.; Gao, X. 2016. Research on subway passenger flow combination prediction model based on RBF neural networks and LSSVM, in 2016 Chinese Control and Decision Conference (CCDC), 28–30 May 2016, Yinchuan, China, 6064–6068. https://doi.org/10.1109/CCDC.2016.7532085
Wang, W.; Cheng, H. 2016. Application of RBF neural network in the forecast of Shanghai railway short-term passenger flow, Intelligent Computer and Applications (6): 79–83. (in Chinese).
Williams, B. M.; Hoel, L. A. 2003. Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process: theoretical basis and empirical results, Journal of Transportation Engineering 129(6): 664–672. https://doi.org/10.1061/(ASCE)0733-947X(2003)129:6(664)
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Zhang, C.-H.; Song, R.; Sun, Y. 2011. Kalman filter-based short-term passenger flow forecasting on bus stop, Journal of Transportation Systems Engineering and Information Technology (4): 154–159. https://doi.org/10.16097/j.cnki.1009-6744.2011.04.019 (in Chinese).
Zhao, Z. 2018. Research on passenger flow prediction of urban rail transit based on combined model, Science and Technology & Innovation 4: 87–88. https://doi.org/10.15913/j.cnki.kjycx.2018.04.087 (in Chinese).
Zhong, M.; Sharma, S.; Lingras, P. 2006. Matching patterns for updating missing values of traffic counts, Transportation Planning and Technology 29(2): 141–156. https://doi.org/10.1080/03081060600753461
Zhu, X. X. 2017. Forecast of Short-Term Bus Passenger Flow Based on IC Card Data. MSc Thesis. Dalian University of Technology, Liaoning, China. (in Chinese).
Zuo, K. L. 2016. Research on Passenger Flow Forecasting Method and Application in Different Level of Time Based on IC Smart Card Data. MSc Thesis. Southeast University, Nanjing, Jiangsu, China. (in Chinese).