IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v604y2022ics0378437122005799.html
   My bibliography  Save this article

A model of lane-changing intention induced by deceleration frequency in an automatic driving environment

Author

Listed:
  • Wang, Lichao
  • Yang, Min
  • Li, Ye
  • Hou, Yiqi

Abstract

Achieving high-level, anthropomorphic lane-changing control for autonomous vehicles is an important method for promoting the safe and efficient operation of autonomous vehicles. According to existing lane-changing models, the important phenomenon that frequent acceleration and deceleration cause a poor riding experience and thus induce the intention of lane-changing needs to be given more attention. In this study, a discretionary lane-changing control method for autonomous vehicles is designed based on anti-interference ability (AIA, where AIA is the number of decelerations per unit time of an autonomous vehicle) to extract the traffic flow variation characteristics of autonomous vehicles with AIA and to determine methods to improve the driving efficiency and stability of autonomous vehicles. AIA is introduced to the lane-changing decision-making process of autonomous vehicles to simulate the phenomenon of lane-changing intention caused by poor riding experience. A control model that can evaluate the lane-changing conditions is then proposed. NetLogo software was selected to construct a one-way, two-lane scenario for autonomous vehicle operation, and the impacts of the proposed decision-making method in each scenario were tested. The experimental results show that autonomous vehicles are more likely to produce frequent deceleration behaviors under large-traffic and small-space operating conditions. A traffic flow composed of autonomous vehicles that use AIA to induce lane-changing intentions has a more obvious phase transition process. An autonomous vehicle whose AIA induces lane-changing intention can improve the speed and stability for a certain traffic volume (a saturation of less than approximately 0.45). Although lane-changing behavior breaks the stable state of two-lane traffic flow, it will also successfully suppress the aggravation of traffic congestion. Lane-changing has an impact on the local oscillation of traffic flow, and a stable traffic flow operation state and reduction in the AIA clearing frequency are mutually reinforcing.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:phsmap:v:604:y:2022:i:c:s0378437122005799
    DOI: 10.1016/j.physa.2022.127905
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437122005799
    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.2022.127905?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. Shinji Kukida & Jun Tanimoto & Aya Hagishima, 2011. "Analysis Of The Influence Of Lane Changing On Traffic-Flow Dynamics Based On The Cellular Automaton Model," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 22(03), pages 271-281.
    2. Keller, Hartmut, 1994. "The German part in European research programmes PROMETHEUS and DRIVE/ATT," Transportation Research Part A: Policy and Practice, Elsevier, vol. 28(6), pages 483-493, November.
    3. Lyu, Zelin & Hu, Xiaojian & Zhang, Fang & Liu, Tenghui & Cui, Zhiwei, 2022. "Heterogeneous traffic flow characteristics on the highway with a climbing lane under different truck percentages: The framework of Kerner’s three-phase traffic theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 587(C).
    4. 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.
    5. Zeng, Jun-Wei & Qian, Yong-Sheng & Yu, Sen-Bin & Wei, Xu-Ting, 2019. "Research on critical characteristics of highway traffic flow based on three phase traffic theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 530(C).
    6. Kerner, Boris S., 2004. "Three-phase traffic theory and highway capacity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 333(C), pages 379-440.
    7. Xiong, Bang-Kai & Jiang, Rui & Tian, Jun-Fang, 2019. "Improving two-dimensional intelligent driver models to overcome overly high deceleration in car-following," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    8. Guo, Yingshi & Zhang, Hongjia & Wang, Chang & Sun, Qinyu & Li, Wanmin, 2021. "Driver lane change intention recognition in the connected environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 575(C).
    9. Sharma, Anshuman & Zheng, Zuduo & Bhaskar, Ashish & Haque, Md. Mazharul, 2019. "Modelling car-following behaviour of connected vehicles with a focus on driver compliance," Transportation Research Part B: Methodological, Elsevier, vol. 126(C), pages 256-279.
    10. Vranken, Tim & Schreckenberg, Michael, 2022. "Modelling multi-lane heterogeneous traffic flow with human-driven, automated, and communicating automated vehicles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 589(C).
    11. Lv, Wei & Song, Wei-guo & Liu, Xiao-dong & Ma, Jian, 2013. "A microscopic lane changing process model for multilane traffic," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(5), pages 1142-1152.
    12. Zheng, Zuduo, 2014. "Recent developments and research needs in modeling lane changing," Transportation Research Part B: Methodological, Elsevier, vol. 60(C), pages 16-32.
    13. Xi-Qun Chen & Wei-Jun Xie & Jing Shi & Qi-Xin Shi, 2010. "Perturbation And Stability Analysis Of The Multi-Anticipative Intelligent Driver Model," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 21(05), pages 647-668.
    14. 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).
    15. Li, Xiang & Sun, Jian-Qiao, 2017. "Studies of vehicle lane-changing dynamics and its effect on traffic efficiency, safety and environmental impact," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 467(C), pages 41-58.
    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, Bingtong & Li, Zhibin & Wang, Shunchao & Li, Meng & Ji, Ang, 2022. "Modeling bounded rationality in discretionary lane change with the quantal response equilibrium of game theory," Transportation Research Part B: Methodological, Elsevier, vol. 164(C), pages 145-161.

    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. Zhou, Shirui & Ling, Shuai & Zhu, Chenqiang & Tian, Junfang, 2022. "Cellular automaton model with the multi-anticipative effect to reproduce the empirical findings of Kerner’s three-phase traffic theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 596(C).
    2. Shanchuan Yu & Yu Chen & Lang Song & Zhaoze Xuan & Yi Li, 2023. "Modelling and Mitigating Secondary Crash Risk for Serial Tunnels on Freeway via Lighting-Related Microscopic Traffic Model with Inter-Lane Dependency," IJERPH, MDPI, vol. 20(4), pages 1-29, February.
    3. Li Li & Dong Zhang, 2018. "Merging Vehicles and Lane Speed-Flow Relationship in a Work Zone," Sustainability, MDPI, vol. 10(7), pages 1-13, June.
    4. Jiang, Yangsheng & Tan, Li & Xiao, Guosheng & Wu, Yunxia & Yao, Zhihong, 2024. "Platoon-aware cooperative lane-changing strategy for connected automated vehicles in mixed traffic flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 640(C).
    5. 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.
    6. Mohammadian, Saeed & Zheng, Zuduo & Haque, Mazharul & Bhaskar, Ashish, 2023. "NET-RAT: Non-equilibrium traffic model based on risk allostasis theory," Transportation Research Part A: Policy and Practice, Elsevier, vol. 174(C).
    7. Kou, Yukang & Ma, Changxi, 2023. "Dual-objective intelligent vehicle lane changing trajectory planning based on polynomial optimization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 617(C).
    8. He, Jia & Huang, Hai-Jun & Yang, Hai & Tang, Tie-Qiao, 2017. "An electric vehicle driving behavior model in the traffic system with a wireless charging lane," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 481(C), pages 119-126.
    9. Guo, Yingshi & Zhang, Hongjia & Wang, Chang & Sun, Qinyu & Li, Wanmin, 2021. "Driver lane change intention recognition in the connected environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 575(C).
    10. Jing, Dian & Yao, Enjian & Chen, Rongsheng, 2023. "Moving characteristics analysis of mixed traffic flow of CAVs and HVs around accident zones," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 626(C).
    11. Hu, Xiaojian & Qiao, Longqi & Hao, Xiatong & Lin, Chenxi & Liu, Tenghui, 2022. "Research on the impact of entry points on urban arterial roads in the framework of Kerner’s three-phase traffic theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 605(C).
    12. Shang, Xue-Cheng & Li, Xin-Gang & Xie, Dong-Fan & Jia, Bin & Jiang, Rui & Liu, Feng, 2022. "A data-driven two-lane traffic flow model based on cellular automata," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 588(C).
    13. Tanimoto, Jun & An, Xie, 2019. "Improvement of traffic flux with introduction of a new lane-change protocol supported by Intelligent Traffic System," Chaos, Solitons & Fractals, Elsevier, vol. 122(C), pages 1-5.
    14. Huiming Duan & Xinping Xiao & Lingling Pei, 2017. "Forecasting the Short-Term Traffic Flow in the Intelligent Transportation System Based on an Inertia Nonhomogenous Discrete Gray Model," Complexity, Hindawi, vol. 2017, pages 1-16, July.
    15. Zhufei Huang & Zihan Zhang & Haijian Li & Lingqiao Qin & Jian Rong, 2019. "Determining Appropriate Lane-Changing Spacing for Off-Ramp Areas of Urban Expressways," Sustainability, MDPI, vol. 11(7), pages 1-15, April.
    16. Chen, Tianyi & Shi, Xiupeng & Wong, Yiik Diew, 2021. "A lane-changing risk profile analysis method based on time-series clustering," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    17. Hou, Lin & Pei, Yulong & He, Qingling, 2023. "A car following model in the context of heterogeneous traffic flow involving multilane following behavior," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 632(P1).
    18. Xizi Cao & Ye Tian & Yan Shen & Tongran Wu & Renfei Li & Xinyu Liu & Amanzheli Yeerken & Yangyang Cui & Yifeng Xue & Aiping Lian, 2021. "Emission Variations of Primary Air Pollutants from Highway Vehicles and Implications during the COVID-19 Pandemic in Beijing, China," IJERPH, MDPI, vol. 18(8), pages 1-12, April.
    19. 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 1-22, July.
    20. 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.

    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:604:y:2022:i:c:s0378437122005799. 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.