IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i4p1639-d1339951.html
   My bibliography  Save this article

Evaluation of Spatiotemporal Characteristics of Lane-Changing at the Freeway Weaving Area from Trajectory Data

Author

Listed:
  • Pengying Ouyang

    (Department of Transportation Engineering, School of Transportation, Southeast University, Southeast University Road #2, Nanjing 211189, China)

  • Bo Yang

    (School of Internet, Anhui University, Feixi Road #3, Hefei 230039, China)

Abstract

Intensive lane-changing (LC) events are one of the great causes that make freeway weaving areas become bottlenecks. This study proposes an approach using vehicle trajectory data to investigate the spatiotemporal distributions of the number of LC events, void occupancies, and throughput variations at the freeway weaving area. Firstly, all LC events are extracted from the cleaned dataset and classified into four types according to the LC vehicles’ origin–destination lanes and LC directions. Secondly, the time and space void occupancies are calculated using the kinematic theory. Thirdly, the throughput variations are identified with the oblique N-curve method. Finally, the spatial and temporal distributions of the LC events, void occupancies, and throughput variations are plotted to analyze their characteristics and relationships. The spatial distributions of different types of LC events indicate that most LC events occur at the surrounding area of the on-ramp entrance. Spatial distributions of time void occupancies show that the time void in the original lanes is quite small while that in the target lanes is much larger. Furthermore, the time void occupancies amplify downstream when considering vehicles traveling on the road. By comparing the temporal distributions of LC events, void occupancies, and throughput variations, there is a lag effect between the large value occurrences of space void occupancy and throughput reduction and that of the LC events, which can conclude a causal relationship between LC events and the occurrences of the space void occupancies and throughput reductions.

Suggested Citation

  • Pengying Ouyang & Bo Yang, 2024. "Evaluation of Spatiotemporal Characteristics of Lane-Changing at the Freeway Weaving Area from Trajectory Data," Sustainability, MDPI, vol. 16(4), pages 1-21, February.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:4:p:1639-:d:1339951
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/4/1639/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/4/1639/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chen, Danjue & Ahn, Soyoung, 2018. "Capacity-drop at extended bottlenecks: Merge, diverge, and weave," Transportation Research Part B: Methodological, Elsevier, vol. 108(C), pages 1-20.
    2. Wu, Jiaming & Kulcsár, Balázs & Ahn, Soyoung & Qu, Xiaobo, 2020. "Emergency vehicle lane pre-clearing: From microscopic cooperation to routing decision making," Transportation Research Part B: Methodological, Elsevier, vol. 141(C), pages 223-239.
    3. Han, Youngjun & Ahn, Soyoung, 2018. "Stochastic modeling of breakdown at freeway merge bottleneck and traffic control method using connected automated vehicle," Transportation Research Part B: Methodological, Elsevier, vol. 107(C), pages 146-166.
    4. Laval, Jorge A. & Leclercq, Ludovic, 2008. "Microscopic modeling of the relaxation phenomenon using a macroscopic lane-changing model," Transportation Research Part B: Methodological, Elsevier, vol. 42(6), pages 511-522, July.
    5. Junwei Zeng & Yongsheng Qian & Fan Yin & Leipeng Zhu & Dejie Xu, 2022. "A multi-value cellular automata model for multi-lane traffic flow under lagrange coordinate," Computational and Mathematical Organization Theory, Springer, vol. 28(2), pages 178-192, June.
    6. Ouyang, Pengying & Liu, Pan & Guo, Yanyong & Chen, Kequan, 2023. "Effects of configuration elements and traffic flow conditions on Lane-Changing rates at the weaving segments," Transportation Research Part A: Policy and Practice, Elsevier, vol. 171(C).
    7. Laval, Jorge A. & Daganzo, Carlos F., 2006. "Lane-changing in traffic streams," Transportation Research Part B: Methodological, Elsevier, vol. 40(3), pages 251-264, March.
    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. Tang, Qing & Hu, Xianbiao & Lu, Jiawei & Zhou, Xuesong, 2021. "Analytical characterization of multi-state effective discharge rates for bus-only lane conversion scheduling problem," Transportation Research Part B: Methodological, Elsevier, vol. 148(C), pages 106-131.
    2. Xu, Tu & Laval, Jorge, 2020. "Statistical inference for two-regime stochastic car-following models," Transportation Research Part B: Methodological, Elsevier, vol. 134(C), pages 210-228.
    3. He, Jia & He, Zhengbing & Fan, Bo & Chen, Yanyan, 2020. "Optimal location of lane-changing warning point in a two-lane road considering different traffic flows," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    4. Chen, Danjue & Ahn, Soyoung, 2018. "Capacity-drop at extended bottlenecks: Merge, diverge, and weave," Transportation Research Part B: Methodological, Elsevier, vol. 108(C), pages 1-20.
    5. Zhou, Hao & Toth, Christopher & Guensler, Randall & Laval, Jorge, 2022. "Hybrid modeling of lane changes near freeway diverges," Transportation Research Part B: Methodological, Elsevier, vol. 165(C), pages 1-14.
    6. Oh, Simon & Yeo, Hwasoo, 2015. "Impact of stop-and-go waves and lane changes on discharge rate in recovery flow," Transportation Research Part B: Methodological, Elsevier, vol. 77(C), pages 88-102.
    7. Ouyang, Pengying & Liu, Pan & Guo, Yanyong & Chen, Kequan, 2023. "Effects of configuration elements and traffic flow conditions on Lane-Changing rates at the weaving segments," Transportation Research Part A: Policy and Practice, Elsevier, vol. 171(C).
    8. Yeo, Hwasoo, 2008. "Asymmetric Microscopic Driving Behavior Theory," University of California Transportation Center, Working Papers qt1tn1m968, University of California Transportation Center.
    9. Shi, Kunsong & Wu, Yuankai & Shi, Haotian & Zhou, Yang & Ran, Bin, 2022. "An integrated car-following and lane changing vehicle trajectory prediction algorithm based on a deep neural network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 599(C).
    10. Chiabaut, Nicolas & Leclercq, Ludovic & Buisson, Christine, 2010. "From heterogeneous drivers to macroscopic patterns in congestion," Transportation Research Part B: Methodological, Elsevier, vol. 44(2), pages 299-308, February.
    11. Jin, Wen-Long, 2013. "A multi-commodity Lighthill–Whitham–Richards model of lane-changing traffic flow," Transportation Research Part B: Methodological, Elsevier, vol. 57(C), pages 361-377.
    12. Gong, Siyuan & Du, Lili, 2016. "Optimal location of advance warning for mandatory lane change near a two-lane highway off-ramp," Transportation Research Part B: Methodological, Elsevier, vol. 84(C), pages 1-30.
    13. Ronan Keane & H. Oliver Gao, 2021. "Fast Calibration of Car-Following Models to Trajectory Data Using the Adjoint Method," Transportation Science, INFORMS, vol. 55(3), pages 592-615, May.
    14. Yibing Wang & Long Wang & Xianghua Yu & Jingqiu Guo, 2023. "Capacity Drop at Freeway Ramp Merges with Its Replication in Macroscopic and Microscopic Traffic Simulations: A Tutorial Report," Sustainability, MDPI, vol. 15(3), pages 1-27, January.
    15. Leclercq, Ludovic & Laval, Jorge A. & Chiabaut, Nicolas, 2011. "Capacity drops at merges: An endogenous model," Transportation Research Part B: Methodological, Elsevier, vol. 45(9), pages 1302-1313.
    16. Anderson, Paul & Geroliminis, Nikolas, 2020. "Dynamic lane restrictions on congested arterials," Transportation Research Part A: Policy and Practice, Elsevier, vol. 135(C), pages 224-243.
    17. Laval, Jorge A. & Toth, Christopher S. & Zhou, Yi, 2014. "A parsimonious model for the formation of oscillations in car-following models," Transportation Research Part B: Methodological, Elsevier, vol. 70(C), pages 228-238.
    18. Jin, Wen-Long & Laval, Jorge, 2018. "Bounded acceleration traffic flow models: A unified approach," Transportation Research Part B: Methodological, Elsevier, vol. 111(C), pages 1-18.
    19. Zheng, Zuduo, 2014. "Recent developments and research needs in modeling lane changing," Transportation Research Part B: Methodological, Elsevier, vol. 60(C), pages 16-32.
    20. Han, Youngjun & Ahn, Soyoung, 2018. "Stochastic modeling of breakdown at freeway merge bottleneck and traffic control method using connected automated vehicle," Transportation Research Part B: Methodological, Elsevier, vol. 107(C), pages 146-166.

    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:16:y:2024:i:4:p:1639-:d:1339951. 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.