IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v664y2025ics0378437125001372.html
   My bibliography  Save this article

The elimination and absorption mechanism of oscillatory motion wave based on jam-absorption driving for mixed traffic flow in intelligent connected environment

Author

Listed:
  • Shen, Jin
  • Zhao, Jiandong
  • Yu, Zhixin
  • Zheng, Shiteng
  • Jiang, Rui

Abstract

With the emergence and development of connected and autonomous driving technologies, managing the behavior of connected autonomous vehicles (CAVs) within mixed traffic flow can mitigate traffic oscillation and enhance overall traffic performance. In recent years, the congestion absorption strategy known as jam-absorption driving (JAD) has been proposed, demonstrating efficacy in absorbing motion waves. Based on this premise, the paper addresses congestion phenomena on roadways by utilizing the real-time control system for JAD within a localized mixed traffic environment of CAVs. This system detects and locates the formation of mobile motion waves, estimates their starting and ending points, selects appropriate CAVs to serve as absorbing vehicles, and issues commands to activate them while choosing suitable control modes to facilitate the absorption of motion waves. Finally, simulations employing a stochastic car-following model are conducted to replicate experimental traffic phenomena and outcomes, testing the real-time absorption effects of varying absorption speeds on motion waves. The results indicate that the JAD system effectively eliminates both single and double motion waves observed during the experiments. In the analysis of individual motion wave, the application of the JAD system resulted in reductions in average speed, speed standard deviation, and speed variability, with speed variability decreasing by as much as 16.22 %. In terms of vehicle collision risk, compared to before the implementation of the JAD system, the platoon's TET and TIT values decreased by 15.45 % and 15.77 % respectively. Regarding energy consumption, the maximum reduction was 6.51 % at a speed of 12.5 m/s. As for emission, CO2 emission decreased by up to 25.05 %, NOx emission by 30.30 %, VOCx emission by 1.63 g/km, and PM emission by 31.53 %.These findings validate that the JAD system effectively mitigates motion waves and reduces traffic oscillations by experimental data, thereby enhancing the efficiency of mixed traffic flow and improving roadway safety.

Suggested Citation

  • Shen, Jin & Zhao, Jiandong & Yu, Zhixin & Zheng, Shiteng & Jiang, Rui, 2025. "The elimination and absorption mechanism of oscillatory motion wave based on jam-absorption driving for mixed traffic flow in intelligent connected environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 664(C).
    • Handle: RePEc:eee:phsmap:v:664:y:2025:i:c:s0378437125001372
    DOI: 10.1016/j.physa.2025.130485
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437125001372
    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.2025.130485?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. Nishi, Ryosuke, 2020. "Theoretical conditions for restricting secondary jams in jam-absorption driving scenarios," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 542(C).
    2. Han, Youngjun & Chen, Danjue & Ahn, Soyoung, 2017. "Variable speed limit control at fixed freeway bottlenecks using connected vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 98(C), pages 113-134.
    3. Gao, Caihong & Wang, Ziyang & Wang, Shupei & Li, Ying, 2024. "Mitigating oscillations of mixed traffic flows at a signalized intersection: A multiagent trajectory optimization approach based on oscillation prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 635(C).
    4. Taniguchi, Yohei & Nishi, Ryosuke & Ezaki, Takahiro & Nishinari, Katsuhiro, 2015. "Jam-absorption driving with a car-following model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 433(C), pages 304-315.
    5. Bilbao-Ubillos, Javier, 2008. "The costs of urban congestion: Estimation of welfare losses arising from congestion on cross-town link roads," Transportation Research Part A: Policy and Practice, Elsevier, vol. 42(8), pages 1098-1108, October.
    6. Nishi, Ryosuke & Tomoeda, Akiyasu & Shimura, Kenichiro & Nishinari, Katsuhiro, 2013. "Theory of jam-absorption driving," Transportation Research Part B: Methodological, Elsevier, vol. 50(C), pages 116-129.
    7. Zhao, Jiandong & Shen, Jin & Yu, Zhixin & Gao, Yuhang & Jiang, Rui, 2024. "Exploration on relation between vehicle oscillation type and platoon oscillation evolution based on multi-scenario field experiment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 640(C).
    8. Nishi, Ryosuke & Watanabe, Takashi, 2022. "System-size dependence of a jam-absorption driving strategy to remove traffic jam caused by a sag under the presence of traffic instability," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 600(C).
    9. repec:plo:pone00:0094351 is not listed on IDEAS
    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. Nishi, Ryosuke & Watanabe, Takashi, 2022. "System-size dependence of a jam-absorption driving strategy to remove traffic jam caused by a sag under the presence of traffic instability," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 600(C).
    2. Nishi, Ryosuke, 2020. "Theoretical conditions for restricting secondary jams in jam-absorption driving scenarios," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 542(C).
    3. Zhou, Zhi & Li, Linheng & Qu, Xu & Ran, Bin, 2024. "PACC: A platoon-based adaptive cruise control strategy based on leader-following information topology to mitigate traffic oscillations under CAV environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 654(C).
    4. Ito, Kotaro & Nishi, Ryosuke, 2023. "Influence of self-disassembly of bridges on collective flow characteristics of swarm robots in a single-lane and periodic system with a gap," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 626(C).
    5. Gao, Caihong & Wang, Ziyang & Wang, Shupei & Li, Ying, 2024. "Mitigating oscillations of mixed traffic flows at a signalized intersection: A multiagent trajectory optimization approach based on oscillation prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 635(C).
    6. Lou, Haoli & Lyu, Hao & Cheng, Rongjun, 2024. "A time-varying driving style oriented model predictive control for smoothing mixed traffic flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 637(C).
    7. Jiang, Yangsheng & Cong, Hongwei & Wang, Yi & Wu, Yunxia & Li, Hongwu & Yao, Zhihong, 2023. "A new control strategy of CAVs platoon for mitigating traffic oscillation in a two-lane highway," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    8. Li, Li & Li, Xiaopeng, 2019. "Parsimonious trajectory design of connected automated traffic," Transportation Research Part B: Methodological, Elsevier, vol. 119(C), pages 1-21.
    9. Haizhen Li & Claudio Roncoli & Weiming Zhao & Yongfeng Ju, 2024. "Assessing the Impact of CAV Driving Strategies on Mixed Traffic on the Ring Road and Freeway," Sustainability, MDPI, vol. 16(8), pages 1-23, April.
    10. Jiang, Jiwan & Zhou, Yang & Wang, Xin & Ahn, Soyoung, 2024. "On dynamic fundamental diagrams: Implications for automated vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 189(C).
    11. Taniguchi, Yohei & Nishi, Ryosuke & Ezaki, Takahiro & Nishinari, Katsuhiro, 2015. "Jam-absorption driving with a car-following model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 433(C), pages 304-315.
    12. 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.
    13. Lu, Ruicheng & Ma, Minghui & Wang, Yansong & Lu, Jiaxuan & Liang, Shidong, 2023. "Dynamic areas strategy design for variable speed limiting at fixed freeway bottlenecks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 615(C).
    14. Saifuzzaman, Mohammad & Zheng, Zuduo & Haque, Md. Mazharul & Washington, Simon, 2017. "Understanding the mechanism of traffic hysteresis and traffic oscillations through the change in task difficulty level," Transportation Research Part B: Methodological, Elsevier, vol. 105(C), pages 523-538.
    15. Chen, Danjue & Laval, Jorge & Zheng, Zuduo & Ahn, Soyoung, 2012. "A behavioral car-following model that captures traffic oscillations," Transportation Research Part B: Methodological, Elsevier, vol. 46(6), pages 744-761.
    16. Bigazzi, Alexander Y. & Figliozzi, Miguel A., 2013. "Marginal costs of freeway traffic congestion with on-road pollution exposure externality," Transportation Research Part A: Policy and Practice, Elsevier, vol. 57(C), pages 12-24.
    17. Zhao, Jiandong & Liu, Meng & Shen, Jin, 2025. "Expressway traffic state recognition based on multi-source data fusion and multi-view fusion graph neural network under velocity feature mapping," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 660(C).
    18. Antunes, Jorge & Tan, Yong & Wanke, Peter & Jabbour, Charbel Jose Chiappetta, 2023. "Impact of R&D and innovation in Chinese road transportation sustainability performance: A novel trigonometric envelopment analysis for ideal solutions (TEA-IS)," Socio-Economic Planning Sciences, Elsevier, vol. 87(PA).
    19. Zhou, Yang & Ahn, Soyoung & Wang, Meng & Hoogendoorn, Serge, 2020. "Stabilizing mixed vehicular platoons with connected automated vehicles: An H-infinity approach," Transportation Research Part B: Methodological, Elsevier, vol. 132(C), pages 152-170.
    20. Zhao, Jiandong & Yu, Zhixin & Jiang, Rui & Wu, Di & Zheng, Shiteng, 2025. "Car following trajectory planning of CAVs: An improved APF model with considering the stochasticity of HDVs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 658(C).

    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:664:y:2025:i:c:s0378437125001372. 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.