IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i10p5690-d557636.html
   My bibliography  Save this article

Analysis and Prediction of Pedestrians’ Violation Behavior at the Intersection Based on a Markov Chain

Author

Listed:
  • Chengyuan Mao

    (Road and Traffic Engineering Institute, Zhejiang Normal University, Jinhua 321004, China)

  • Lewen Bao

    (Road and Traffic Engineering Institute, Zhejiang Normal University, Jinhua 321004, China)

  • Shengde Yang

    (Road and Traffic Engineering Institute, Zhejiang Normal University, Jinhua 321004, China)

  • Wenjiao Xu

    (Road and Traffic Engineering Institute, Zhejiang Normal University, Jinhua 321004, China)

  • Qin Wang

    (Road and Traffic Engineering Institute, Zhejiang Normal University, Jinhua 321004, China)

Abstract

Pedestrian violations pose a danger to themselves and other road users. Most previous studies predict pedestrian violation behaviors based only on pedestrians’ demographic characteristics. In practice, in addition to demographic characteristics, other factors may also impact pedestrian violation behaviors. Therefore, this study aims to predict pedestrian crossing violations based on pedestrian attributes, traffic conditions, road geometry, and environmental conditions. Data on the pedestrian crossing, both in compliance and in violation, were collected from 10 signalized intersections in the city of Jinhua, China. We propose an illegal pedestrian crossing behavior prediction approach that consists of a logistic regression model and a Markov Chain model. The former calculates the likelihood that the first pedestrian who decides to cross the intersection illegally within each signal cycle, while the latter computes the probability that the subsequent pedestrians who decides to follow the violation. The proposed approach was validated using data gathered from an additional signalized intersection in Jinhua city. The results show that the proposed approach has a robust ability in pedestrian violation behavior prediction. The findings can provide theoretical references for pedestrian signal timing, crossing facility optimization, and warning system design.

Suggested Citation

  • Chengyuan Mao & Lewen Bao & Shengde Yang & Wenjiao Xu & Qin Wang, 2021. "Analysis and Prediction of Pedestrians’ Violation Behavior at the Intersection Based on a Markov Chain," Sustainability, MDPI, vol. 13(10), pages 1-15, May.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:10:p:5690-:d:557636
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/10/5690/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/13/10/5690/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yildirimoglu, Mehmet & Geroliminis, Nikolas, 2013. "Experienced travel time prediction for congested freeways," Transportation Research Part B: Methodological, Elsevier, vol. 53(C), pages 45-63.
    2. Ramezani, Mohsen & Geroliminis, Nikolas, 2012. "On the estimation of arterial route travel time distribution with Markov chains," Transportation Research Part B: Methodological, Elsevier, vol. 46(10), pages 1576-1590.
    3. Chiara Gruden & Irena Ištoka Otković & Matjaž Šraml, 2020. "Neural Networks Applied to Microsimulation: A Prediction Model for Pedestrian Crossing Time," Sustainability, MDPI, vol. 12(13), pages 1-22, July.
    4. Xi Yang & Wenjuan Fan & Shanlin Yang, 2020. "Identifying the Influencing Factors on Investors’ Investment Behavior: An Empirical Study Focusing on the Chinese P2P Lending Market," Sustainability, MDPI, vol. 12(13), pages 1-21, July.
    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. Guardiola, I.G. & Leon, T. & Mallor, F., 2014. "A functional approach to monitor and recognize patterns of daily traffic profiles," Transportation Research Part B: Methodological, Elsevier, vol. 65(C), pages 119-136.
    2. Hu, Zejing & Smirnova, M.N. & Zhang, Yongliang & Smirnov, N.N. & Zhu, Zuojin, 2021. "Estimation of travel time through a composite ring road by a viscoelastic traffic flow model," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 181(C), pages 501-521.
    3. Saif Eddin Jabari & Nikolaos M. Freris & Deepthi Mary Dilip, 2020. "Sparse Travel Time Estimation from Streaming Data," Transportation Science, INFORMS, vol. 54(1), pages 1-20, January.
    4. Büchel, Beda & Corman, Francesco, 2022. "Modeling conditional dependencies for bus travel time estimation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 592(C).
    5. Sjoerd van der Spoel & Chintan Amrit & Jos van Hillegersberg, 2017. "Predictive analytics for truck arrival time estimation: a field study at a European distribution centre," International Journal of Production Research, Taylor & Francis Journals, vol. 55(17), pages 5062-5078, September.
    6. Verbeeck, C. & Vansteenwegen, P. & Aghezzaf, E.-H., 2016. "Solving the stochastic time-dependent orienteering problem with time windows," European Journal of Operational Research, Elsevier, vol. 255(3), pages 699-718.
    7. Westgate, Bradford S. & Woodard, Dawn B. & Matteson, David S. & Henderson, Shane G., 2016. "Large-network travel time distribution estimation for ambulances," European Journal of Operational Research, Elsevier, vol. 252(1), pages 322-333.
    8. Wong, Wai & Shen, Shengyin & Zhao, Yan & Liu, Henry X., 2019. "On the estimation of connected vehicle penetration rate based on single-source connected vehicle data," Transportation Research Part B: Methodological, Elsevier, vol. 126(C), pages 169-191.
    9. Kaniz Fatima & Sara Moridpour & Tayebeh Saghapour, 2021. "Spatial and Temporal Distribution of Elderly Public Transport Mode Preference," Sustainability, MDPI, vol. 13(9), pages 1-15, April.
    10. Liu, Wei & Geroliminis, Nikolas, 2016. "Modeling the morning commute for urban networks with cruising-for-parking: An MFD approach," Transportation Research Part B: Methodological, Elsevier, vol. 93(PA), pages 470-494.
    11. Wenwei Zhang & Hui Zhao, 2021. "Modal choice analysis for a linear monocentric city with battery electric vehicles and park-charge-ride services," Transportation, Springer, vol. 48(4), pages 1895-1929, August.
    12. Yildirimoglu, Mehmet & Ramezani, Mohsen, 2020. "Demand management with limited cooperation among travellers: A doubly dynamic approach," Transportation Research Part B: Methodological, Elsevier, vol. 132(C), pages 267-284.
    13. Jenelius, Erik & Koutsopoulos, Haris N., 2013. "Travel time estimation for urban road networks using low frequency probe vehicle data," Transportation Research Part B: Methodological, Elsevier, vol. 53(C), pages 64-81.
    14. Irena Ištoka Otković & Barbara Karleuša & Aleksandra Deluka-Tibljaš & Sanja Šurdonja & Mario Marušić, 2021. "Combining Traffic Microsimulation Modeling and Multi-Criteria Analysis for Sustainable Spatial-Traffic Planning," Land, MDPI, vol. 10(7), pages 1-26, June.
    15. Zhaoqi Zang & Xiangdong Xu & Kai Qu & Ruiya Chen & Anthony Chen, 2022. "Travel time reliability in transportation networks: A review of methodological developments," Papers 2206.12696, arXiv.org, revised Jul 2022.
    16. Zhang, Kunpeng & Feng, Xiaoliang & Jia, Ning & Zhao, Liang & He, Zhengbing, 2022. "TSR-GAN: Generative Adversarial Networks for Traffic State Reconstruction with Time Space Diagrams," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 591(C).
    17. Yuan, Yun & Zhang, Zhao & Yang, Xianfeng Terry & Zhe, Shandian, 2021. "Macroscopic traffic flow modeling with physics regularized Gaussian process: A new insight into machine learning applications in transportation," Transportation Research Part B: Methodological, Elsevier, vol. 146(C), pages 88-110.
    18. Li, Weigang & Liu, Jian, 2023. "Analysis of the evolution of pedestrian crossing based on dynamic penalty mechanism," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 623(C).
    19. Kim, Jungyeol & Sarkar, Saswati & Venkatesh, Santosh S. & Ryerson, Megan Smirti & Starobinski, David, 2020. "An epidemiological diffusion framework for vehicular messaging in general transportation networks," Transportation Research Part B: Methodological, Elsevier, vol. 131(C), pages 160-190.
    20. Du, Bo & Wang, David Z.W., 2014. "Continuum modeling of park-and-ride services considering travel time reliability and heterogeneous commuters – A linear complementarity system approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 71(C), pages 58-81.

    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:jsusta:v:13:y:2021:i:10:p:5690-:d:557636. 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.