IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i12p5536-d1680053.html
   My bibliography  Save this article

Evaluating the Impact of AV Penetration and Behavior on Freeway Traffic Efficiency and Safety Using Microscopic Simulation

Author

Listed:
  • Taebum Eom

    (Eco-Friendly Smart Vehicle Research Center, Korea Advanced Institute of Science and Technology (KAIST), Jeju-si 63309, Republic of Korea)

  • Minju Park

    (Department of Big Data Application, Hannam University, Daejeon 34430, Republic of Korea
    SIANDIS Inc., Daejeon 34430, Republic of Korea)

Abstract

As autonomous vehicles (AVs) are gradually integrated into existing traffic systems, understanding their impact on freeway operations becomes essential for effective infrastructure planning and policy design. This study explores how AV penetration rates, behavior profiles, and freeway geometry interact to influence traffic performance and safety. Using microscopic simulations in VISSIM (a high-fidelity traffic simulation tool), four typical freeway segment types—basic sections, weaving zones, on-ramp merging areas, and AV-exclusive lanes—were modeled under diverse traffic demands and AV behavior settings. The findings indicate that, while AVs can improve flow stability in simple environments, their performance may deteriorate in complex merging scenarios without supportive design or behavior coordination. AV-exclusive lanes offer some mitigation when AV share is high. These results underscore that AV integration requires context-specific strategies and cannot be universally applied. Adaptive, behavior-aware traffic management is recommended to support a smooth transition toward mixed autonomy.

Suggested Citation

  • Taebum Eom & Minju Park, 2025. "Evaluating the Impact of AV Penetration and Behavior on Freeway Traffic Efficiency and Safety Using Microscopic Simulation," Sustainability, MDPI, vol. 17(12), pages 1-19, June.
  • Handle: RePEc:gam:jsusta:v:17:y:2025:i:12:p:5536-:d:1680053
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/12/5536/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/17/12/5536/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Xuan Fang & Hexuan Li & Tamás Tettamanti & Arno Eichberger & Martin Fellendorf, 2022. "Effects of Automated Vehicle Models at the Mixed Traffic Situation on a Motorway Scenario," Energies, MDPI, vol. 15(6), pages 1-15, March.
    2. 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.
    3. Ji, Yuanjin & Huang, Youpei & Yang, Maozhenning & Leng, Han & Ren, Lihui & Liu, Hongda & Chen, Yuejian, 2025. "Physics-informed deep learning for virtual rail train trajectory following control," Reliability Engineering and System Safety, Elsevier, vol. 261(C).
    4. Chen, Yuejian & Liu, Xuemei & Rao, Meng & Qin, Yong & Wang, Zhipeng & Ji, Yuanjin, 2025. "Explicit speed-integrated LSTM network for non-stationary gearbox vibration representation and fault detection under varying speed conditions," Reliability Engineering and System Safety, Elsevier, vol. 254(PA).
    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. 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).
    2. Zeynel Baran Yıldırım & Mustafa Özuysal, 2024. "Autonomous Vehicles and Urban Traffic Management for Sustainability: Impacts of Transition of Control and Dedicated Lanes," Sustainability, MDPI, vol. 16(19), pages 1-19, September.
    3. Arno Eichberger & Zsolt Szalay & Martin Fellendorf & Henry Liu, 2022. "Advances in Automated Driving Systems," Energies, MDPI, vol. 15(10), pages 1-5, May.
    4. 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).
    5. Zhou, Yongjie & Liang, Jun, 2025. "Platoon agglomeration strategy and analysis in CAV dedicated lanes under low CAV penetration," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 664(C).
    6. 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.
    7. 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).
    8. 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.
    9. Guan, Hao & Wang, Hua & Meng, Qiang & Mak, Chin Long, 2023. "Markov chain-based traffic analysis on platooning effect among mixed semi- and fully-autonomous vehicles in a freeway lane," Transportation Research Part B: Methodological, Elsevier, vol. 173(C), pages 176-202.
    10. Tan, Zhen & Liu, Fan & Chan, Hing Kai & Gao, H. Oliver, 2022. "Transportation systems management considering dynamic wireless charging electric vehicles: Review and prospects," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 163(C).
    11. Mateusz Malarczyk & Jules-Raymond Tapamo & Marcin Kaminski, 2022. "Application of Neural Data Processing in Autonomous Model Platform—A Complex Review of Solutions, Design and Implementation," Energies, MDPI, vol. 15(13), pages 1-22, June.
    12. Almlöf, Erik & Nybacka, Mikael & Pernestål, Anna & Jenelius, Erik, 2022. "Will leisure trips be more affected than work trips by autonomous technology? Modelling self-driving public transport and cars in Stockholm, Sweden," Transportation Research Part A: Policy and Practice, Elsevier, vol. 165(C), pages 1-19.
    13. Zhao, Peilin & Wong, Yiik Diew & Zhu, Feng, 2025. "Modeling and analysis of the platoon size of Connected Autonomous Vehicles in a mixed traffic environment," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 199(C).
    14. 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.
    15. Sreten Jevremović & Vladan Tubić & Filip Arnaut & Aleksandra Kolarski & Vladimir A. Srećković, 2025. "Moonlit Roads—Spatial and Temporal Patterns of Wildlife–Vehicle Collisions in Serbia," Sustainability, MDPI, vol. 17(14), pages 1-17, July.
    16. Hu, Xiaojian & Yu, Fengkai, 2025. "Objective description of heterogeneous traffic flow patterns of passenger cars and trucks on long downhill sections in Kerner's three-phase traffic theory framework," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 659(C).
    17. 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.
    18. Liu, Qingchao & Cai, Yingfeng & Jiang, Haobin & Lu, Jian & Chen, Long, 2018. "Traffic state prediction using ISOMAP manifold learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 532-541.
    19. Umberto Crisalli & Andrea Gemma & Marco Petrelli, 2023. "Investigating the Effects of Automated Vehicles on Large Urban Road Networks: Some Evidence from Rome," Sustainability, MDPI, vol. 15(13), pages 1-10, July.
    20. Andrea Gemma & Tina Onorato & Stefano Carrese, 2023. "Performances and Environmental Impacts of Connected and Autonomous Vehicles for Different Mixed-Traffic Scenarios," Sustainability, MDPI, vol. 15(13), pages 1-19, June.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:17:y:2025:i:12:p:5536-:d:1680053. 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.