IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i19p11052-d650700.html
   My bibliography  Save this article

Impacts of Autonomous Vehicles on Traffic Flow Characteristics under Mixed Traffic Environment: Future Perspectives

Author

Listed:
  • Mohammed Al-Turki

    (Department of Civil & Environmental Engineering, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia)

  • Nedal T. Ratrout

    (Department of Civil & Environmental Engineering, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia)

  • Syed Masiur Rahman

    (Research Institute, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia)

  • Imran Reza

    (Department of Civil & Architectural Engineering, University of Wyoming, Laramie, WY 82071, USA)

Abstract

Vehicle automation and communication technologies are considered promising approaches to improve operational driving behavior. The expected gradual implementation of autonomous vehicles (AVs) shortly will cause unique impacts on the traffic flow characteristics. This paper focuses on reviewing the expected impacts under a mixed traffic environment of AVs and regular vehicles (RVs) considering different AV characteristics. The paper includes a policy implication discussion for possible actual future practice and research interests. The AV implementation has positive impacts on the traffic flow, such as improved traffic capacity and stability. However, the impact depends on the factors including penetration rate of the AVs, characteristics, and operational settings of the AVs, traffic volume level, and human driving behavior. The critical penetration rate, which has a high potential to improve traffic characteristics, was higher than 40%. AV’s intelligent control of operational driving is a function of its operational settings, mainly car-following modeling. Different adjustments of these settings may improve some traffic flow parameters and may deteriorate others. The position and distribution of AVs and the type of their leading or following vehicles may play a role in maximizing their impacts.

Suggested Citation

  • Mohammed Al-Turki & Nedal T. Ratrout & Syed Masiur Rahman & Imran Reza, 2021. "Impacts of Autonomous Vehicles on Traffic Flow Characteristics under Mixed Traffic Environment: Future Perspectives," Sustainability, MDPI, vol. 13(19), pages 1-22, October.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:19:p:11052-:d:650700
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/19/11052/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/13/19/11052/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wang, Jian & Peeta, Srinivas & He, Xiaozheng, 2019. "Multiclass traffic assignment model for mixed traffic flow of human-driven vehicles and connected and autonomous vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 126(C), pages 139-168.
    2. Jin, Shuang & Sun, Di-Hua & Zhao, Min & Li, Yang & Chen, Jin, 2020. "Modeling and stability analysis of mixed traffic with conventional and connected automated vehicles from cyber physical perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 551(C).
    3. Jamil Hamadneh & Domokos Esztergár-Kiss, 2021. "The Influence of Introducing Autonomous Vehicles on Conventional Transport Modes and Travel Time," Energies, MDPI, vol. 14(14), pages 1-28, July.
    4. VanderWerf, Joel & Shladover, Steven & Miller, Mark A., 2004. "Conceptual Development and Performance Assessment for the Deployment Staging of Advanced Vehicle Control and Safety Systems," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt8hg3b55r, Institute of Transportation Studies, UC Berkeley.
    5. Zhou, Y.J. & Zhu, H.B. & Guo, M.M. & Zhou, J.L., 2020. "Impact of CACC vehicles’ cooperative driving strategy on mixed four-lane highway traffic flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    6. Aggelos Soteropoulos & Martin Berger & Francesco Ciari, 2019. "Impacts of automated vehicles on travel behaviour and land use: an international review of modelling studies," Transport Reviews, Taylor & Francis Journals, vol. 39(1), pages 29-49, January.
    7. 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.
    8. Rajagopal, 2014. "The Human Factors," Palgrave Macmillan Books, in: Architecting Enterprise, chapter 9, pages 225-249, Palgrave Macmillan.
    9. Jason Hawkins & Khandker Nurul Habib, 2019. "Integrated models of land use and transportation for the autonomous vehicle revolution," Transport Reviews, Taylor & Francis Journals, vol. 39(1), pages 66-83, January.
    10. Dimitris Milakis, 2019. "Long-term implications of automated vehicles: an introduction," Transport Reviews, Taylor & Francis Journals, vol. 39(1), pages 1-8, January.
    11. Araz Taeihagh & Hazel Si Min Lim, 2019. "Governing autonomous vehicles: emerging responses for safety, liability, privacy, cybersecurity, and industry risks," Transport Reviews, Taylor & Francis Journals, vol. 39(1), pages 103-128, January.
    12. Chen, Danjue & Ahn, Soyoung & Chitturi, Madhav & Noyce, David A., 2017. "Towards vehicle automation: Roadway capacity formulation for traffic mixed with regular and automated vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 100(C), pages 196-221.
    13. Yu Lin & Hongfei Jia & Bo Zou & Hongzhi Miao & Ruiyi Wu & Jingjing Tian & Guanfeng Wang, 2021. "Multiobjective Environmentally Sustainable Optimal Design of Dedicated Connected Autonomous Vehicle Lanes," Sustainability, MDPI, vol. 13(6), pages 1-21, March.
    14. Guang Yu & Shuo Liu & Qiangqiang Shangguan, 2021. "Optimization and Evaluation of Platooning Car-Following Models in a Connected Vehicle Environment," Sustainability, MDPI, vol. 13(6), pages 1-20, March.
    15. Fábio Duarte & Carlo Ratti, 2018. "The Impact of Autonomous Vehicles on Cities: A Review," Journal of Urban Technology, Taylor & Francis Journals, vol. 25(4), pages 3-18, October.
    16. Naveen Eluru & Charisma F. Choudhury, 2019. "Impact of shared and autonomous vehicles on travel behavior," Transportation, Springer, vol. 46(6), pages 1971-1974, December.
    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. 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.
    2. Zibin Wei & Tao Peng & Sijia Wei, 2022. "A Robust Adaptive Traffic Signal Control Algorithm Using Q-Learning under Mixed Traffic Flow," Sustainability, MDPI, vol. 14(10), pages 1-16, May.
    3. Junyan Han & Xiaoyuan Wang & Gang Wang, 2022. "Modeling the Car-Following Behavior with Consideration of Driver, Vehicle, and Environment Factors: A Historical Review," Sustainability, MDPI, vol. 14(13), pages 1-27, July.
    4. 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.

    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. Devon McAslan & Farah Najar Arevalo & David A. King & Thaddeus R. Miller, 2021. "Pilot project purgatory? Assessing automated vehicle pilot projects in U.S. cities," Palgrave Communications, Palgrave Macmillan, vol. 8(1), pages 1-16, December.
    2. 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).
    3. Nadafianshahamabadi, Razieh & Tayarani, Mohammad & Rowangould, Gregory, 2021. "A closer look at urban development under the emergence of autonomous vehicles: Traffic, land use and air quality impacts," Journal of Transport Geography, Elsevier, vol. 94(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. Gu, Yewen & Goez, Julio C. & Mario, Guajardo & Wallace, Stein W., 2019. "Autonomous vessels: State of the art and potential opportunities in logistics," Discussion Papers 2019/6, Norwegian School of Economics, Department of Business and Management Science.
    6. 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.
    7. Wali, Behram & Santi, Paolo & Ratti, Carlo, 2023. "Are californians willing to use shared automated vehicles (SAV) & renounce existing vehicles? An empirical analysis of factors determining SAV use & household vehicle ownership," Technological Forecasting and Social Change, Elsevier, vol. 195(C).
    8. 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.
    9. Bridgelall, Raj & Stubbing, Edward, 2021. "Forecasting the effects of autonomous vehicles on land use," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    10. Liliana Andrei & Oana Luca & Florian Gaman, 2022. "Insights from User Preferences on Automated Vehicles: Influence of Socio-Demographic Factors on Value of Time in Romania Case," Sustainability, MDPI, vol. 14(17), pages 1-22, August.
    11. Asmussen, Katherine E. & Mondal, Aupal & Bhat, Chandra R., 2022. "Adoption of partially automated vehicle technology features and impacts on vehicle miles of travel (VMT)," Transportation Research Part A: Policy and Practice, Elsevier, vol. 158(C), pages 156-179.
    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. Shelly Etzioni & Jamil Hamadneh & Arnór B. Elvarsson & Domokos Esztergár-Kiss & Milena Djukanovic & Stelios N. Neophytou & Jaka Sodnik & Amalia Polydoropoulou & Ioannis Tsouros & Cristina Pronello & N, 2020. "Modeling Cross-National Differences in Automated Vehicle Acceptance," Sustainability, MDPI, vol. 12(22), pages 1-22, November.
    14. Charles David A. Icasiano & Araz Taeihagh, 2021. "Governance of the Risks of Ridesharing in Southeast Asia: An In-Depth Analysis," Sustainability, MDPI, vol. 13(11), pages 1-32, June.
    15. Tengilimoglu, Oguz & Carsten, Oliver & Wadud, Zia, 2023. "Implications of automated vehicles for physical road environment: A comprehensive review," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 169(C).
    16. Sun, Mingmei, 2023. "A day-to-day dynamic model for mixed traffic flow of autonomous vehicles and inertial human-driven vehicles," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 173(C).
    17. Ziakopoulos, Apostolos & Oikonomou, Maria G. & Vlahogianni, Eleni I. & Yannis, George, 2021. "Quantifying the implementation impacts of a point to point automated urban shuttle service in a large-scale network," Transport Policy, Elsevier, vol. 114(C), pages 233-244.
    18. 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).
    19. Liu, Zhaocai & Chen, Zhibin & He, Yi & Song, Ziqi, 2021. "Network user equilibrium problems with infrastructure-enabled autonomy," Transportation Research Part B: Methodological, Elsevier, vol. 154(C), pages 207-241.
    20. Guo, Yuntao & Souders, Dustin & Labi, Samuel & Peeta, Srinivas & Benedyk, Irina & Li, Yujie, 2021. "Paving the way for autonomous Vehicles: Understanding autonomous vehicle adoption and vehicle fuel choice under user heterogeneity," Transportation Research Part A: Policy and Practice, Elsevier, vol. 154(C), pages 364-398.

    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:13:y:2021:i:19:p:11052-:d:650700. 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.