IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v616y2023ics037843712300122x.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S037843712300122X
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2023.128567?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    3. Wu, Weitiao & Liu, Ronghui & Jin, Wenzhou, 2017. "Modelling bus bunching and holding control with vehicle overtaking and distributed passenger boarding behaviour," Transportation Research Part B: Methodological, Elsevier, vol. 104(C), pages 175-197.
    4. Andres, Matthias & Nair, Rahul, 2017. "A predictive-control framework to address bus bunching," Transportation Research Part B: Methodological, Elsevier, vol. 104(C), pages 123-148.
    5. Liu, Tao & Ceder, Avishai (Avi), 2015. "Analysis of a new public-transport-service concept: Customized bus in China," Transport Policy, Elsevier, vol. 39(C), pages 63-76.
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Li, Shukai & Liu, Ronghui & Yang, Lixing & Gao, Ziyou, 2019. "Robust dynamic bus controls considering delay disturbances and passenger demand uncertainty," Transportation Research Part B: Methodological, Elsevier, vol. 123(C), pages 88-109.
    2. Dai, Zhuang & Liu, Xiaoyue Cathy & Chen, Zhuo & Guo, Renyong & Ma, Xiaolei, 2019. "A predictive headway-based bus-holding strategy with dynamic control point selection: A cooperative game theory approach," Transportation Research Part B: Methodological, Elsevier, vol. 125(C), pages 29-51.
    3. Sirmatel, Isik Ilber & Geroliminis, Nikolas, 2018. "Mixed logical dynamical modeling and hybrid model predictive control of public transport operations," Transportation Research Part B: Methodological, Elsevier, vol. 114(C), pages 325-345.
    4. Wang, Chao & Ma, Changxi & Xu, Xuecai(Daniel), 2020. "Multi-objective optimization of real-time customized bus routes based on two-stage method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).
    5. Gong, Manlin & Hu, Yucong & Chen, Zhiwei & Li, Xiaopeng, 2021. "Transfer-based customized modular bus system design with passenger-route assignment optimization," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 153(C).
    6. Wu, Weitiao & Liu, Ronghui & Jin, Wenzhou & Ma, Changxi, 2019. "Stochastic bus schedule coordination considering demand assignment and rerouting of passengers," Transportation Research Part B: Methodological, Elsevier, vol. 121(C), pages 275-303.
    7. S. Sajikumar & D. Bijulal, 2022. "Zero bunching solution for a local public transport system with multiple-origins bus operation," Public Transport, Springer, vol. 14(3), pages 655-681, October.
    8. Zhou, Chang & Tian, Qiong & Wang, David Z.W., 2022. "A novel control strategy in mitigating bus bunching: Utilizing real-time information," Transport Policy, Elsevier, vol. 123(C), pages 1-13.
    9. Mohammad Sadrani & Ahmad Reza Jafarian-Moghaddam & Mohsen Aboutalebi Esfahani & Amir Masoud Rahimi, 2023. "Designing limited-stop bus services for minimizing operator and user costs under crowding conditions," Public Transport, Springer, vol. 15(1), pages 97-128, March.
    10. Varga, Balázs & Tettamanti, Tamás & Kulcsár, Balázs, 2019. "Energy-aware predictive control for electrified bus networks," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    11. Jenelius, Erik, 2018. "Public transport experienced service reliability: Integrating travel time and travel conditions," Transportation Research Part A: Policy and Practice, Elsevier, vol. 117(C), pages 275-291.
    12. Dong Liu & Feng Xiao & Jian Luo & Fan Yang, 2023. "Deep Reinforcement Learning-Based Holding Control for Bus Bunching under Stochastic Travel Time and Demand," Sustainability, MDPI, vol. 15(14), pages 1-18, July.
    13. Sadrani, Mohammad & Tirachini, Alejandro & Antoniou, Constantinos, 2022. "Vehicle dispatching plan for minimizing passenger waiting time in a corridor with buses of different sizes: Model formulation and solution approaches," European Journal of Operational Research, Elsevier, vol. 299(1), pages 263-282.
    14. Wu, Weitiao & Liu, Ronghui & Jin, Wenzhou & Ma, Changxi, 2019. "Simulation-based robust optimization of limited-stop bus service with vehicle overtaking and dynamics: A response surface methodology," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 130(C), pages 61-81.
    15. Liping Ge & Stefan Voß & Lin Xie, 2022. "Robustness and disturbances in public transport," Public Transport, Springer, vol. 14(1), pages 191-261, March.
    16. 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).
    17. Wu, Weitiao & Liu, Ronghui & Jin, Wenzhou, 2017. "Modelling bus bunching and holding control with vehicle overtaking and distributed passenger boarding behaviour," Transportation Research Part B: Methodological, Elsevier, vol. 104(C), pages 175-197.
    18. 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.
    19. Lee, Enoch & Cen, Xuekai & Lo, Hong K., 2022. "Scheduling zonal-based flexible bus service under dynamic stochastic demand and Time-dependent travel time," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 168(C).
    20. Xing, Jiping & Wu, Wei & Cheng, Qixiu & Liu, Ronghui, 2022. "Traffic state estimation of urban road networks by multi-source data fusion: Review and new insights," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 595(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:616:y:2023:i:c:s037843712300122x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.