IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i4p3138-d1062406.html
   My bibliography  Save this article

Evaluating Efficiency and Safety of Mixed Traffic with Connected and Autonomous Vehicles in Adverse Weather

Author

Listed:
  • Guangyang Hou

    (School of Civil Engineering and Environmental Science, University of Oklahoma, Norman, OK 80523, USA)

Abstract

Connected and autonomous vehicles (CAVs) are expected to significantly improve traffic efficiency and safety. However, the overall impacts of CAVs on mixed traffic have not been clearly studied because most previous research focused on one subset of the performance of mixed traffic. This study aims to provide complete information for the policymakers to make better decisions on future CAV implementation strategies with a comprehensive evaluation of the overall performance of mixed traffic. With this purpose, this study develops an integrated framework to evaluate the efficiency and safety of mixed traffic with CAVs under adverse weather conditions, which is composed of a traffic simulation, multi-vehicle crash model, single-vehicle crash model, and performance assessment. For the first time, a unified performance index is introduced to reflect the overall efficiency and safety performance of mixed traffic. The proposed framework is demonstrated with an evaluation of the performance of mixed traffic on a highway segment. Traffic efficiency and safety under different weather conditions are investigated. The impact of reaction time of human-driving vehicles (HDVs) and CAVs are also studied. Simulation results show that the overall traffic performance in terms of traffic efficiency, multi-vehicle safety, and single-vehicle safety increases with the increase in the market penetration rate (MPR). In addition, it is found that CAVs have a greater impact on improving overall traffic performance under rainy and snowy weather than in clear weather. Moreover, a shorter reaction time of HDVs and CAVs can lead to better overall traffic performance.

Suggested Citation

  • Guangyang Hou, 2023. "Evaluating Efficiency and Safety of Mixed Traffic with Connected and Autonomous Vehicles in Adverse Weather," Sustainability, MDPI, vol. 15(4), pages 1-19, February.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:4:p:3138-:d:1062406
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/4/3138/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/4/3138/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Ye, Lanhang & Yamamoto, Toshiyuki, 2019. "Evaluating the impact of connected and autonomous vehicles on traffic safety," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 526(C).
    3. Ye, Lanhang & Yamamoto, Toshiyuki, 2018. "Modeling connected and autonomous vehicles in heterogeneous traffic flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 269-277.
    4. Jiang, Yangsheng & Wang, Sichen & Yao, Zhihong & Zhao, Bin & Wang, Yi, 2021. "A cellular automata model for mixed traffic flow considering the driving behavior of connected automated vehicle platoons," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 582(C).
    5. Zhu, H.B. & Zhou, Y.J. & Wu, W.J., 2020. "Modeling traffic flow mixed with automated vehicles considering drivers ’ character difference," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).
    6. Chen Chen & Xiaohua Zhao & Hao Liu & Guichao Ren & Yunlong Zhang & Xiaoming Liu, 2019. "Assessing the Influence of Adverse Weather on Traffic Flow Characteristics Using a Driving Simulator and VISSIM," Sustainability, MDPI, vol. 11(3), pages 1-16, February.
    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. Oana Luca & Liliana Andrei & Cristina Iacoboaea & Florian Gaman, 2023. "Unveiling the Hidden Effects of Automated Vehicles on “Do No Significant Harm” Components," Sustainability, MDPI, vol. 15(14), pages 1-26, July.
    2. Xiaoyuan Feng & Yue Chen & Hongbo Li & Tian Ma & Yilong Ren, 2023. "Gated Recurrent Graph Convolutional Attention Network for Traffic Flow Prediction," Sustainability, MDPI, vol. 15(9), pages 1-13, May.

    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. Li, Xia & Xiao, Yuewen & Zhao, Xiaodong & Ma, Xinwei & Wang, Xintong, 2023. "Modeling mixed traffic flows of human-driving vehicles and connected and autonomous vehicles considering human drivers’ cognitive characteristics and driving behavior interaction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    2. Wu, Yuanyuan & Wang, David Z.W. & Zhu, Feng, 2022. "Influence of CAVs platooning on intersection capacity under mixed traffic," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(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. 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.
    5. Abebe Dress Beza & Mohammad Maghrour Zefreh & Adam Torok, 2022. "Impacts of Different Types of Automated Vehicles on Traffic Flow Characteristics and Emissions: A Microscopic Traffic Simulation of Different Freeway Segments," Energies, MDPI, vol. 15(18), pages 1-19, September.
    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. Tanimoto, Jun & Futamata, Masanori & Tanaka, Masaki, 2020. "Automated vehicle control systems need to solve social dilemmas to be disseminated," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    8. Wang, Baojie & Li, Wei & Wen, Haosong & Hu, Xiaojian, 2021. "Modeling impacts of driving automation system on mixed traffic flow at off-ramp freeway facilities," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 573(C).
    9. Manivasakan, Hesavar & Kalra, Riddhi & O'Hern, Steve & Fang, Yihai & Xi, Yinfei & Zheng, Nan, 2021. "Infrastructure requirement for autonomous vehicle integration for future urban and suburban roads – Current practice and a case study of Melbourne, Australia," Transportation Research Part A: Policy and Practice, Elsevier, vol. 152(C), pages 36-53.
    10. Muhammad Azam & Sitti Asmah Hassan & Othman Che Puan, 2022. "Autonomous Vehicles in Mixed Traffic Conditions—A Bibliometric Analysis," Sustainability, MDPI, vol. 14(17), pages 1-34, August.
    11. Pernestål Brenden , Anna & Kristoffersson , Ida, 2018. "Effects of driverless vehicles: A review of simulations," Working papers in Transport Economics 2018:11, CTS - Centre for Transport Studies Stockholm (KTH and VTI).
    12. Zhang, Fang & Lu, Jian & Hu, Xiaojian & Meng, Qiang, 2023. "Integrated deployment of dedicated lane and roadside unit considering uncertain road capacity under the mixed-autonomy traffic environment," Transportation Research Part B: Methodological, Elsevier, vol. 174(C).
    13. Raffaele Mauro & Andrea Pompigna, 2022. "A Statistically Based Model for the Characterization of Vehicle Interactions and Vehicle Platoons Formation on Two-Lane Roads," Sustainability, MDPI, vol. 14(8), pages 1-22, April.
    14. Abdul Rashid Mussah & Yaw Adu-Gyamfi, 2022. "Machine Learning Framework for Real-Time Assessment of Traffic Safety Utilizing Connected Vehicle Data," Sustainability, MDPI, vol. 14(22), pages 1-16, November.
    15. Li, Huamin & Zhang, Shun, 2022. "Lane change behavior with uncertainty and fuzziness for human driving vehicles and its simulation in mixed traffic," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).
    16. Kantapich Preedakorn & David Butler & Jörn Mehnen, 2023. "Challenges for the Adoption of Electric Vehicles in Thailand: Potential Impacts, Barriers, and Public Policy Recommendations," Sustainability, MDPI, vol. 15(12), pages 1-21, June.
    17. Zhai, Cong & Zhang, Ronghui & Peng, Tao & Zhong, Changfu & Xu, Hongguo, 2023. "Heterogeneous lattice hydrodynamic model and jamming transition mixed with connected vehicles and human-driven vehicles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 623(C).
    18. Yuan, Naitong & Ma, Minghui & Liang, Shidong & Wang, Wenjie & Zhang, Hu, 2022. "Optimal control method of freeway based on tollbooths lane configuration and variable speed limit control," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
    19. Jacek Oskarbski & Konrad Biszko, 2022. "Estimation of Vehicle Energy Consumption at Intersections Using Microscopic Traffic Models," Energies, MDPI, vol. 16(1), pages 1-35, December.
    20. Kun Zhang & Yu Xue & Hao-Jie Luo & Qiang Zhang & Yuan Tang & Bing-Ling Cen, 2023. "Cyber-attacks on the optimal velocity and its variation by bifurcation analyses," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 96(12), pages 1-19, December.

    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:15:y:2023:i:4:p:3138-:d:1062406. 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.