IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i9p5193-d801965.html
   My bibliography  Save this article

Autonomous Vehicles for Enhancing Expressway Capacity: A Dynamic Perspective

Author

Listed:
  • Cong-Jian Liu

    (School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Fang-Kai Wang

    (Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai 200092, China)

  • Zhuang-Zhuang Wang

    (School of Naval Architecture Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)

  • Tao Wang

    (School of Naval Architecture Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)

  • Ze-Hao Jiang

    (School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

Abstract

With rapidly developing communication and autonomous-driving technology, traffic flow on road networks will change from homogeneous human-driven vehicle (HDV) traffic flow to heterogeneous mixed traffic flow (MTF) comprising HDVs, autonomous vehicles (AVs), and connective-and-autonomous vehicles (CAVs). To understand the changes in the MTF of transportation engineering, we investigated the reserved capacity (RC) and right-of-way (ROW) reallocation policy that should be utilized under MTF scenarios. We established an MTF-based theoretical model to calculate the expressway segment capacity, theoretically analyzed the influence of the market penetration rate (MPR) on capacity and validated the model through numerical analysis. The results showed that the MPR of AVs and CAVs can enhance the MTF RC that is within 0–200% and that the platooning rate of CAVs positively influences the MTF RC. CAV popularization does not necessarily lead to a rapid increase in the transportation system efficiency when the MPR is <40% but significantly improves the efficiency of existing urban transportation facilities. When the MPR is >40%, the greatest enhancement is 4800 pcu/h/lane in terms of RC. A ROW reallocation policy that equips CAV-dedicated lanes according to the MPR of AVs and CAVs can enhance the capacity of expressway systems by 500 pcu/h/lane in terms of RC.

Suggested Citation

  • Cong-Jian Liu & Fang-Kai Wang & Zhuang-Zhuang Wang & Tao Wang & Ze-Hao Jiang, 2022. "Autonomous Vehicles for Enhancing Expressway Capacity: A Dynamic Perspective," Sustainability, MDPI, vol. 14(9), pages 1-14, April.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:9:p:5193-:d:801965
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/9/5193/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/14/9/5193/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wang, Yacan & Geng, Kexin & May, Anthony D. & Zhou, Huiyu, 2022. "The impact of traffic demand management policy mix on commuter travel choices," Transport Policy, Elsevier, vol. 117(C), pages 74-87.
    2. Bahrami, Sina & Roorda, Matthew J., 2020. "Optimal traffic management policies for mixed human and automated traffic flows," Transportation Research Part A: Policy and Practice, Elsevier, vol. 135(C), pages 130-143.
    3. Zhao, Congyu & Wang, Kun & Dong, Xiucheng & Dong, Kangyin, 2022. "Is smart transportation associated with reduced carbon emissions? The case of China," Energy Economics, Elsevier, vol. 105(C).
    4. Ghiasi, Amir & Hussain, Omar & Qian, Zhen (Sean) & Li, Xiaopeng, 2017. "A mixed traffic capacity analysis and lane management model for connected automated vehicles: A Markov chain method," Transportation Research Part B: Methodological, Elsevier, vol. 106(C), pages 266-292.
    5. Ma, Zhenliang & Koutsopoulos, Haris N. & Liu, Tianyou & Basu, Abhishek Arunasis, 2020. "Behavioral response to promotion-based public transport demand management: Longitudinal analysis and implications for optimal promotion design," Transportation Research Part A: Policy and Practice, Elsevier, vol. 141(C), pages 356-372.
    6. Hani S. Mahmassani, 2016. "50th Anniversary Invited Article—Autonomous Vehicles and Connected Vehicle Systems: Flow and Operations Considerations," Transportation Science, INFORMS, vol. 50(4), pages 1140-1162, November.
    7. van den Berg, Vincent A.C. & Verhoef, Erik T., 2016. "Autonomous cars and dynamic bottleneck congestion: The effects on capacity, value of time and preference heterogeneity," Transportation Research Part B: Methodological, Elsevier, vol. 94(C), pages 43-60.
    8. Sala, Marcel & Soriguera, Francesc, 2021. "Capacity of a freeway lane with platoons of autonomous vehicles mixed with regular traffic," Transportation Research Part B: Methodological, Elsevier, vol. 147(C), pages 116-131.
    9. Yang, Yang & He, Kun & Wang, Yun-peng & Yuan, Zhen-zhou & Yin, Yong-hao & Guo, Man-ze, 2022. "Identification of dynamic traffic crash risk for cross-area freeways based on statistical and machine learning methods," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 595(C).
    10. Chen, Zhibin & He, Fang & Yin, Yafeng & Du, Yuchuan, 2017. "Optimal design of autonomous vehicle zones in transportation networks," Transportation Research Part B: Methodological, Elsevier, vol. 99(C), pages 44-61.
    11. Decker, Christopher & Chiambaretto, Paul, 2022. "Economic policy choices and trade-offs for Unmanned aircraft systems Traffic Management (UTM): Insights from Europe and the United States," Transportation Research Part A: Policy and Practice, Elsevier, vol. 157(C), pages 40-58.
    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. Qingyu Luo & Rui Du & Hongfei Jia & Lili Yang, 2022. "Research on the Deployment of Joint Dedicated Lanes for CAVs and Buses," Sustainability, MDPI, vol. 14(14), pages 1-20, July.

    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, Pengbo & Tian, Lijun & Xiao, Feng & Zhu, Hongwei, 2022. "Can day-to-day dynamic model be solved analytically? New insights on portraying equilibrium and accommodating autonomous vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 166(C), pages 374-395.
    2. 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.
    3. 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).
    4. Luo, Qi & Saigal, Romesh & Chen, Zhibin & Yin, Yafeng, 2019. "Accelerating the adoption of automated vehicles by subsidies: A dynamic games approach," Transportation Research Part B: Methodological, Elsevier, vol. 129(C), pages 226-243.
    5. Chen, Shukai & Wang, Hua & Xiao, Ling & Meng, Qiang, 2022. "Random capacity for a single lane with mixed autonomous and human-driven vehicles: Bounds, mean gaps and probability distributions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 160(C).
    6. Jiang, Yangsheng & Sun, Siyuan & Zhu, Fangyi & Wu, Yunxia & Yao, Zhihong, 2023. "A mixed capacity analysis and lane management model considering platoon size and intensity of CAVs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 615(C).
    7. Li, Qing & Liao, Feixiong, 2020. "Incorporating vehicle self-relocations and traveler activity chains in a bi-level model of optimal deployment of shared autonomous vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 140(C), pages 151-175.
    8. 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).
    9. 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).
    10. Bahrami, Sina & Roorda, Matthew J., 2020. "Optimal traffic management policies for mixed human and automated traffic flows," Transportation Research Part A: Policy and Practice, Elsevier, vol. 135(C), pages 130-143.
    11. Talebian, Ahmadreza & Mishra, Sabyasachee, 2022. "Unfolding the state of the adoption of connected autonomous trucks by the commercial fleet owner industry," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 158(C).
    12. Liu, Peng & Xu, Shu-Xian & Ong, Ghim Ping & Tian, Qiong & Ma, Shoufeng, 2021. "Effect of autonomous vehicles on travel and urban characteristics," Transportation Research Part B: Methodological, Elsevier, vol. 153(C), pages 128-148.
    13. 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.
    14. Qingyu Luo & Rui Du & Hongfei Jia & Lili Yang, 2022. "Research on the Deployment of Joint Dedicated Lanes for CAVs and Buses," Sustainability, MDPI, vol. 14(14), pages 1-20, July.
    15. Yu, Xiaojuan & van den Berg, Vincent A.C. & Verhoef, Erik T. & Li, Zhi-Chun, 2022. "Will all autonomous cars cooperate? Brands’ strategic interactions under dynamic congestion," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 166(C).
    16. Mo, Dong & Chen, Xiqun (Michael) & Zhang, Junlin, 2022. "Modeling and Managing Mixed On-Demand Ride Services of Human-Driven Vehicles and Autonomous Vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 157(C), pages 80-119.
    17. Kassens-Noor, Eva & Dake, Dana & Decaminada, Travis & Kotval-K, Zeenat & Qu, Teresa & Wilson, Mark & Pentland, Brian, 2020. "Sociomobility of the 21st century: Autonomous vehicles, planning, and the future city," Transport Policy, Elsevier, vol. 99(C), pages 329-335.
    18. 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.
    19. 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).
    20. 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.

    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:14:y:2022:i:9:p:5193-:d:801965. 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.