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A separate modelling approach for short-term bus passenger flow prediction based on behavioural patterns: A hybrid decision tree method

Author

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  • Li, Peng
  • Wu, Weitiao
  • Pei, Xiangjing
Abstract
Accurate short-term passenger flow prediction plays an important role in transit planning and operation. Existing research is mostly based on a joint modelling approach in which transit demand is predicted in an aggregated manner taking the overall passenger flow as input. A critical problem for the joint modelling approach is that the complexity of passenger flow composition and the distinct behavioural response to influential factors are missing out. To address this challenge, this paper proposes a separate modelling approach for passenger flow prediction based on behavioural patterns. To this end, we develop a novel hybrid decision tree (HDT) model coupled with a decision tree model and time series model. The upper layer is a decision tree model, in which the dataset is divided according to passenger types and influential factors, while the lower layer is the time series model achieved by the recurrent neural network. Particularly, this research first undertakes passenger classification using smartcard data through cluster analysis, from which the correlation between the classified passenger flow and influential factors is obtained. The proposed method is tested in a real-life bus route in Guangzhou, China. We also investigate the impact of passenger classification schemes and the minimum amount of data contained by leaf nodes on the performance of the HDT model. Based on this, we recommend the best classification scheme and the optimal value of the minimum amount of data contained by leaf nodes. Comparisons show that our method outperforms other traditional methods in terms of both prediction accuracy and stability. In addition, our method could also provide the prediction of passenger flow composition, which provides more references for customized bus service design.

Suggested Citation

  • Li, Peng & Wu, Weitiao & Pei, Xiangjing, 2023. "A separate modelling approach for short-term bus passenger flow prediction based on behavioural patterns: A hybrid decision tree method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 616(C).
    • Handle: RePEc:eee:phsmap:v:616:y:2023:i:c:s037843712300122x
    DOI: 10.1016/j.physa.2023.128567
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    References listed on IDEAS

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    1. Wu, Weitiao & Liu, Ronghui & Jin, Wenzhou, 2016. "Designing robust schedule coordination scheme for transit networks with safety control margins," Transportation Research Part B: Methodological, Elsevier, vol. 93(PA), pages 495-519.
    2. Pengpeng Jiao & Ruimin Li & Tuo Sun & Zenghao Hou & Amir Ibrahim, 2016. "Three Revised Kalman Filtering Models for Short-Term Rail Transit Passenger Flow Prediction," Mathematical Problems in Engineering, Hindawi, vol. 2016, pages 1-10, March.
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    6. Wu, Weitiao & Li, Peng & Liu, Ronghui & Jin, Wenzhou & Yao, Baozhen & Xie, Yuanqi & Ma, Changxi, 2020. "Predicting peak load of bus routes with supply optimization and scaled Shepard interpolation: A newsvendor model," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 142(C).
    7. Sánchez-Martínez, G.E. & Koutsopoulos, H.N. & Wilson, N.H.M., 2016. "Real-time holding control for high-frequency transit with dynamics," Transportation Research Part B: Methodological, Elsevier, vol. 83(C), pages 1-19.
    8. Wu, Weitiao & Lin, Yue & Liu, Ronghui & Jin, Wenzhou, 2022. "The multi-depot electric vehicle scheduling problem with power grid characteristics," Transportation Research Part B: Methodological, Elsevier, vol. 155(C), pages 322-347.
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    Cited by:

    1. Ma, Changxi & Zhang, Bowen & Li, Shukai & Lu, Youpeng, 2024. "Urban rail transit passenger flow prediction with ResCNN-GRU based on self-attention mechanism," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 638(C).

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