IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v593y2022ics0378437122000735.html
   My bibliography  Save this article

Understanding the mechanism of lane changing process and dynamics using microscopic traffic data

Author

Listed:
  • Chauhan, Prajwal
  • Kanagaraj, Venkatesan
  • Asaithambi, Gowri

Abstract

The lane changing manoeuvre is a fundamental driver behaviour that determines vehicle distribution across lanes. Lane-changing behaviour has a significant effect on traffic flow and may cause traffic oscillations, relaxation, moving bottleneck, and capacity drop/breakdown. This study aims to identify the lane change window and classify the lane change behaviour of vehicles using NGSIM data. Determination of the starting and ending points (time window) of lane change operation has a significant impact on the lane change duration and dynamics. In this study, a new approach is proposed to identify the time window of lane change operation using the absolute value of the derivative of the cumulative lateral speed which reduces unintended drifts of vehicles in lateral direction. Then, the lane changes are classified into free, forced and cooperative using Hidas definition (Hidas, 2005) which does not closely replicate the field conditions. This study proposes three different methods for the classification of lane changes based on microscopic traffic variables and vehicle kinematics in the vicinity of lane changing process and these methods are verified quantitatively. Statistical tests are performed to check if the types of lane changes are statistically different. Finally, two types of classification are considered such as free lane change and constraint lane change, and a log-normal distribution is fitted for the lane change duration of these two types.

Suggested Citation

  • Chauhan, Prajwal & Kanagaraj, Venkatesan & Asaithambi, Gowri, 2022. "Understanding the mechanism of lane changing process and dynamics using microscopic traffic data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).
  • Handle: RePEc:eee:phsmap:v:593:y:2022:i:c:s0378437122000735
    DOI: 10.1016/j.physa.2022.126981
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437122000735
    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.2022.126981?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. 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.
    2. 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.
    3. Daniel (Jian) Sun & Lily Elefteriadou, 2014. "A Driver Behavior-Based Lane-Changing Model for Urban Arterial Streets," Transportation Science, INFORMS, vol. 48(2), pages 184-205, May.
    4. 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)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wang, Lichao & Yang, Min & Li, Ye & Hou, Yiqi, 2022. "A model of lane-changing intention induced by deceleration frequency in an automatic driving environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    2. Min Zhang & Yuhan Nie & Chi Zhang & Bo Wang & Shengyu Xi, 2024. "Analysis of the Duration of Mandatory Lane Changes for Heavy-Duty Trucks at Interchanges," Sustainability, MDPI, vol. 16(14), pages 1-21, July.

    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. 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).
    2. 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.
    3. 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.
    4. Zheng, Zuduo, 2014. "Recent developments and research needs in modeling lane changing," Transportation Research Part B: Methodological, Elsevier, vol. 60(C), pages 16-32.
    5. 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.
    6. 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.
    7. 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.
    8. Mingmin Guo & Zheng Wu & Huibing Zhu, 2018. "Empirical study of lane-changing behavior on three Chinese freeways," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-22, January.
    9. 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.
    10. 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.
    11. Feng, Shumin & Li, Jinyang & Ding, Ning & Nie, Cen, 2015. "Traffic paradox on a road segment based on a cellular automaton: Impact of lane-changing behavior," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 428(C), pages 90-102.
    12. 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.
    13. Yeo, Hwasoo, 2008. "Asymmetric Microscopic Driving Behavior Theory," University of California Transportation Center, Working Papers qt1tn1m968, University of California Transportation Center.
    14. 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.
    15. 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.
    16. 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.
    17. Jorge A. Laval & Ludovic Leclercq, 2010. "Continuum Approximation for Congestion Dynamics Along Freeway Corridors," Transportation Science, INFORMS, vol. 44(1), pages 87-97, February.
    18. 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.
    19. Khelfa, Basma & Ba, Ibrahima & Tordeux, Antoine, 2023. "Predicting highway lane-changing maneuvers: A benchmark analysis of machine and ensemble learning algorithms," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 612(C).
    20. Jin, Wen-Long & Gan, Qi-Jian & Lebacque, Jean-Patrick, 2015. "A kinematic wave theory of capacity drop," Transportation Research Part B: Methodological, Elsevier, vol. 81(P1), pages 316-329.

    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:593:y:2022:i:c:s0378437122000735. 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.