IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v561y2021ics0378437120306385.html
   My bibliography  Save this article

Linear stability analysis of heterogeneous traffic flow considering degradations of connected automated vehicles and reaction time

Author

Listed:
  • Yao, Zhihong
  • Xu, Taorang
  • Jiang, Yangsheng
  • Hu, Rong

Abstract

With the development of connected automated vehicles (CAVs) technologies, traffic flow on the road is converting into the heterogeneous traffic flow, which consists of CAVs and human-driven vehicles (HDVs). Considering the function of CAVs would degrade when following HDVs in heterogeneous traffic flow. Moreover, the reaction time of degraded CAVs would be different with CAVs or HDVs. This paper deduces and analyzes the linear stability of such heterogeneous traffic flow based on CAVs degradations and reaction time diversity. Firstly, considering the degradations of CAVs, the vehicle types and their ratios in heterogeneous traffic flow are analyzed. Secondly, the car-following model and the reaction time of three types of vehicles are discussed. Then, the linear stability condition of heterogeneous traffic flow is proposed. Finally, the impact factors on the stability condition are illustrated through numerical analysis. The results show that the high penetration rate of CAVs and the short reaction time can improve the linear stability of heterogeneous traffic flow. Besides, when the penetration rate of CAVs increases to 65%, the stability of heterogeneous traffic flow is not affected by speed. Furthermore, the analysis shows that CAVs degradations also has a negative effect on linear stability.

Suggested Citation

  • Yao, Zhihong & Xu, Taorang & Jiang, Yangsheng & Hu, Rong, 2021. "Linear stability analysis of heterogeneous traffic flow considering degradations of connected automated vehicles and reaction time," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 561(C).
  • Handle: RePEc:eee:phsmap:v:561:y:2021:i:c:s0378437120306385
    DOI: 10.1016/j.physa.2020.125218
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437120306385
    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.2020.125218?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. Jia, Dongyao & Ngoduy, Dong, 2016. "Enhanced cooperative car-following traffic model with the combination of V2V and V2I communication," Transportation Research Part B: Methodological, Elsevier, vol. 90(C), pages 172-191.
    2. Treiber, Martin & Kesting, Arne & Helbing, Dirk, 2006. "Delays, inaccuracies and anticipation in microscopic traffic models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 360(1), pages 71-88.
    3. Ye, Lanhang & Yamamoto, Toshiyuki, 2018. "Impact of dedicated lanes for connected and autonomous vehicle on traffic flow throughput," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 588-597.
    4. Zhu, Wen-Xing & Zhang, H.M., 2018. "Analysis of mixed traffic flow with human-driving and autonomous cars based on car-following model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 274-285.
    5. Robert Herman & Elliott W. Montroll & Renfrey B. Potts & Richard W. Rothery, 1959. "Traffic Dynamics: Analysis of Stability in Car Following," Operations Research, INFORMS, vol. 7(1), pages 86-106, February.
    6. Yao, Zhihong & Hu, Rong & Wang, Yi & Jiang, Yangsheng & Ran, Bin & Chen, Yanru, 2019. "Stability analysis and the fundamental diagram for mixed connected automated and human-driven vehicles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 533(C).
    7. Sun, Jie & Zheng, Zuduo & Sun, Jian, 2018. "Stability analysis methods and their applicability to car-following models in conventional and connected environments," Transportation Research Part B: Methodological, Elsevier, vol. 109(C), pages 212-237.
    8. Davis, L.C., 2014. "Nonlinear dynamics of autonomous vehicles with limits on acceleration," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 405(C), pages 128-139.
    9. Chang, Xin & Li, Haijian & Rong, Jian & Zhao, Xiaohua & Li, An’ran, 2020. "Analysis on traffic stability and capacity for mixed traffic flow with platoons of intelligent connected vehicles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 557(C).
    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. Luo, Ruifa & Gu, Qiufan & Xu, Taorang & Hao, Huijun & Yao, Zhihong, 2022. "Analysis of linear internal stability for mixed traffic flow of connected and automated vehicles considering multiple influencing factors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 597(C).
    2. Chen, Yingda & Kong, Dewen & Sun, Lishan & Zhang, Tong & Song, Yongchang, 2022. "Fundamental diagram and stability analysis for heterogeneous traffic flow considering human-driven vehicle driver’s acceptance of cooperative adaptive cruise control vehicles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 589(C).
    3. Zong, Fang & Wang, Meng & Tang, Jinjun & Zeng, Meng, 2022. "Modeling AVs & RVs’ car-following behavior by considering impacts of multiple surrounding vehicles and driving characteristics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 589(C).
    4. Bouadi, Marouane & Jia, Bin & Jiang, Rui & Li, Xingang & Gao, Zi-You, 2022. "Stochastic factors and string stability of traffic flow: Analytical investigation and numerical study based on car-following models," Transportation Research Part B: Methodological, Elsevier, vol. 165(C), pages 96-122.
    5. Zhaoming Zhou & Jianbo Yuan & Shengmin Zhou & Qiong Long & Jianrong Cai & Lei Zhang, 2023. "Modeling and Analysis of Driving Behaviour for Heterogeneous Traffic Flow Considering Market Penetration under Capacity Constraints," Sustainability, MDPI, vol. 15(4), pages 1-21, February.
    6. Zhang, Futao & Qian, Yongsheng & Zeng, Junwei & Xu, Dejie & Li, Haijun, 2023. "Stability and safety analysis of mixed traffic flow considering network function degradation and platoon driving on the road with a slope," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    7. Chen, Jianzhong & Liang, Huan & Li, Jing & Xu, Zhaoxin, 2021. "A novel distributed cooperative approach for mixed platoon consisting of connected and automated vehicles and human-driven vehicles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 573(C).
    8. Montanino, Marcello & Punzo, Vincenzo, 2021. "On string stability of a mixed and heterogeneous traffic flow: A unifying modelling framework," Transportation Research Part B: Methodological, Elsevier, vol. 144(C), pages 133-154.
    9. Cui, Ziyu & Wang, Xiaoning & Ci, Yusheng & Yang, Changyun & Yao, Jia, 2023. "Modeling and analysis of car-following models incorporating multiple lead vehicles and acceleration information in heterogeneous traffic flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    10. Li, Chao & Zhao, Xiaomei & Xie, Dongfan, 2022. "Steady-state performance and dynamic performance of heterogeneous platoons under a connected environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 599(C).
    11. Xin Chang & Xingjian Zhang & Haichao Li & Chang Wang & Zhe Liu, 2022. "A Survey on Mixed Traffic Flow Characteristics in Connected Vehicle Environments," Sustainability, MDPI, vol. 14(13), pages 1-22, June.
    12. Du, Yu & Kouvelas, Anastasios & ShangGuan, Wei & Makridis, Michail A., 2022. "Dynamic capacity estimation of mixed traffic flows with application in adaptive traffic signal control," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).
    13. Zhang, Geng & Yin, Le & Pan, Dong-Bo & Zhang, Yu & Cui, Bo-Yuan & Jiang, Shan, 2020. "Research on multiple vehicles’ continuous self-delayed velocities on traffic flow with vehicle-to-vehicle communication," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
    14. Chen, Shuiwang & Hu, Lu & Yao, Zhihong & Zhu, Juanxiu & Zhao, Bin & Jiang, Yangsheng, 2022. "Efficient and environmentally friendly operation of intermittent dedicated lanes for connected autonomous vehicles in mixed traffic environments," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P2).
    15. Montanino, Marcello & Monteil, Julien & Punzo, Vincenzo, 2021. "From homogeneous to heterogeneous traffic flows: Lp String stability under uncertain model parameters," Transportation Research Part B: Methodological, Elsevier, vol. 146(C), pages 136-154.
    16. Ma, Ke & Wang, Hao & Ruan, Tiancheng, 2021. "Analysis of road capacity and pollutant emissions: Impacts of Connected and automated vehicle platoons on traffic flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 583(C).
    17. Zhou, Linjie & Ruan, Tiancheng & Ma, Ke & Dong, Changyin & Wang, Hao, 2021. "Impact of CAV platoon management on traffic flow considering degradation of control mode," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 581(C).
    18. Guo, Mengting & Bai, Yang & Li, Xia & Zhou, Wei & Wang, Chunyang & Ma, Xinwei & Gao, Huixin & Xiao, Yuewen, 2023. "Freeway capacity modeling and analysis for traffic mixed with human-driven and connected automated vehicles considering driver behavior characteristics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 623(C).
    19. Hamdar, Samer H. & Mahmassani, Hani S. & Treiber, Martin, 2015. "From behavioral psychology to acceleration modeling: Calibration, validation, and exploration of drivers’ cognitive and safety parameters in a risk-taking environment," Transportation Research Part B: Methodological, Elsevier, vol. 78(C), pages 32-53.
    20. Sun, Lu & Jafaripournimchahi, Ammar & Kornhauser, Alain & Hu, Wushen, 2020. "A new higher-order viscous continuum traffic flow model considering driver memory in the era of autonomous and connected vehicles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 547(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:561:y:2021:i:c:s0378437120306385. 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.