IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v15y2023i7p222-d1176200.html
   My bibliography  Save this article

Bus Travel Time Prediction Based on the Similarity in Drivers’ Driving Styles

Author

Listed:
  • Zhenzhong Yin

    (School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China)

  • Bin Zhang

    (Software College, Northeastern University, Shenyang 110819, China)

Abstract

Providing accurate and real-time bus travel time information is crucial for both passengers and public transportation managers. However, in the traditional bus travel time prediction model, due to the lack of consideration of the influence of different bus drivers’ driving styles on the bus travel time, the prediction result is not ideal. In the traditional bus travel time prediction model, the historical travel data of all drivers in the entire bus line are usually used for training and prediction. Due to great differences in individual driving styles, the eigenvalues of drivers’ driving parameters are widely distributed. Therefore, the prediction accuracy of the model trained by this dataset is low. At the same time, the training time of the model is too long due to the large sample size, making it difficult to provide a timely prediction in practical applications. However, if only the historical dataset of a single driver is used for training and prediction, the amount of training data is too small, and it is also difficult to accurately predict travel time. To solve these problems, this paper proposes a method to predict bus travel times based on the similarity of drivers’ driving styles. Firstly, the historical travel time data of different drivers are clustered, and then the corresponding types of drivers’ historical data are used to predict the travel time, so as to improve the accuracy and speed of the travel time prediction. We evaluated our approach using a real-world bus trajectory dataset collected in Shenyang, China. The experimental results show that the accuracy of the proposed method is 13.4% higher than that of the traditional method.

Suggested Citation

  • Zhenzhong Yin & Bin Zhang, 2023. "Bus Travel Time Prediction Based on the Similarity in Drivers’ Driving Styles," Future Internet, MDPI, vol. 15(7), pages 1-18, June.
  • Handle: RePEc:gam:jftint:v:15:y:2023:i:7:p:222-:d:1176200
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/15/7/222/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1999-5903/15/7/222/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Cats, Oded, 2014. "Regularity-driven bus operation: Principles, implementation and business models," Transport Policy, Elsevier, vol. 36(C), pages 223-230.
    2. Daganzo, Carlos F., 2009. "A headway-based approach to eliminate bus bunching: Systematic analysis and comparisons," Transportation Research Part B: Methodological, Elsevier, vol. 43(10), pages 913-921, December.
    3. Chow, Andy H.F. & Li, Shuai & Zhong, Renxin, 2017. "Multi-objective optimal control formulations for bus service reliability with traffic signals," Transportation Research Part B: Methodological, Elsevier, vol. 103(C), pages 248-268.
    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. Gkiotsalitis, K. & Cats, O., 2021. "At-stop control measures in public transport: Literature review and research agenda," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 145(C).
    2. Varga, Balázs & Tettamanti, Tamás & Kulcsár, Balázs & Qu, Xiaobo, 2020. "Public transport trajectory planning with probabilistic guarantees," Transportation Research Part B: Methodological, Elsevier, vol. 139(C), pages 81-101.
    3. 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.
    4. 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.
    5. Xuemei Zhou & Yehan Wang & Xiangfeng Ji & Caitlin Cottrill, 2019. "Coordinated Control Strategy for Multi-Line Bus Bunching in Common Corridors," Sustainability, MDPI, vol. 11(22), pages 1-23, November.
    6. Pandey, Ayush & Lehe, Lewis J., 2024. "Congestive mode-switching and economies of scale on a bus route," Transportation Research Part B: Methodological, Elsevier, vol. 183(C).
    7. Martínez-Estupiñan, Yerly & Delgado, Felipe & Muñoz, Juan Carlos & Watkins, Kari E., 2023. "Improving the performance of headway control tools by using individual driving speed data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 174(C).
    8. Anderson, Paul & Daganzo, Carlos F., 2020. "Effect of transit signal priority on bus service reliability," Transportation Research Part B: Methodological, Elsevier, vol. 132(C), pages 2-14.
    9. Wang, Shuaian & Meng, Qiang, 2012. "Liner ship route schedule design with sea contingency time and port time uncertainty," Transportation Research Part B: Methodological, Elsevier, vol. 46(5), pages 615-633.
    10. 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.
    11. 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.
    12. Wei Wu & Wanjing Ma & Kejun Long & Heping Zhou & Yi Zhang, 2016. "Designing Sustainable Public Transportation: Integrated Optimization of Bus Speed and Holding Time in a Connected Vehicle Environment," Sustainability, MDPI, vol. 8(11), pages 1-15, November.
    13. Zhang, Wei & (Ato) Xu, Wangtu, 2017. "Simulation-based robust optimization for the schedule of single-direction bus transit route: The design of experiment," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 106(C), pages 203-230.
    14. Ibarra-Rojas, Omar J. & Rios-Solis, Yasmin A., 2012. "Synchronization of bus timetabling," Transportation Research Part B: Methodological, Elsevier, vol. 46(5), pages 599-614.
    15. Fatemeh Enayatollahi & Ahmed Osman Idris & M. A. Amiri Atashgah, 2019. "Modelling bus bunching under variable transit demand using cellular automata," Public Transport, Springer, vol. 11(2), pages 269-298, August.
    16. 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.
    17. 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.
    18. Su, Z.C. & Chow, Andy H.F. & Fang, C.L. & Liang, E.M. & Zhong, R.X., 2023. "Hierarchical control for stochastic network traffic with reinforcement learning," Transportation Research Part B: Methodological, Elsevier, vol. 167(C), pages 196-216.
    19. Gkiotsalitis, K. & Cats, O. & Liu, T. & Bult, J.M., 2023. "An exact optimization method for coordinating the arrival times of urban rail lines at a common corridor," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 178(C).
    20. Cats, Oded, 2014. "Regularity-driven bus operation: Principles, implementation and business models," Transport Policy, Elsevier, vol. 36(C), pages 223-230.

    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:gam:jftint:v:15:y:2023:i:7:p:222-:d:1176200. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.