IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i13p10146-d1179874.html
   My bibliography  Save this article

Performances and Environmental Impacts of Connected and Autonomous Vehicles for Different Mixed-Traffic Scenarios

Author

Listed:
  • Andrea Gemma

    (Department of Civil, Computer Science and Aeronautical Technologies Engineering, Roma Tre University, 00146 Rome, Italy)

  • Tina Onorato

    (Department of Civil, Computer Science and Aeronautical Technologies Engineering, Roma Tre University, 00146 Rome, Italy)

  • Stefano Carrese

    (Department of Civil, Computer Science and Aeronautical Technologies Engineering, Roma Tre University, 00146 Rome, Italy)

Abstract

As the transition towards connected and autonomous vehicles gradually happens, different phases with CAVs and human-driven vehicles sharing the same network will occur. This paper’s purpose is to increase the knowledge of these mixed situations, studying the impacts of an increasing number of CAVs within the vehicle fleet on road capacity, travel time savings and energy consumption, providing new insights into the debate that is still open. The methodology focused on a microsimulation-based approach on an urban motorway in the city of Rome. Some of the outcomes from simulations, run with the software PTV Vissim TM 21, were used to analyse variations in general performances of the transportation system, whereas the remaining results were fed into the emission model COPERT for assessing the impacts of CAV penetration on the energy consumption of the fleet. Results show how, in congested cases, appreciable improvements can be recorded in terms of road capacity, mean speeds, and environmental impacts, while in lower-congested situations, any enhancement in traffic fluidification counteracts the environmental performances of the whole system.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:13:p:10146-:d:1179874
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/13/10146/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/13/10146/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. McCall, Rod & McGee, Fintan & Mirnig, Alexander & Meschtscherjakov, Alexander & Louveton, Nicolas & Engel, Thomas & Tscheligi, Manfred, 2019. "A taxonomy of autonomous vehicle handover situations," Transportation Research Part A: Policy and Practice, Elsevier, vol. 124(C), pages 507-522.
    2. 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.
    3. Carrese, Stefano & Nigro, Marialisa & Patella, Sergio Maria & Toniolo, Eleonora, 2019. "A preliminary study of the potential impact of autonomous vehicles on residential location in Rome," Research in Transportation Economics, Elsevier, vol. 75(C), pages 55-61.
    4. Gipps, P. G., 1986. "A model for the structure of lane-changing decisions," Transportation Research Part B: Methodological, Elsevier, vol. 20(5), pages 403-414, October.
    5. Ding, Heng & Pan, Hao & Bai, Haijian & Zheng, Xiaoyan & Chen, Jin & Zhang, Weihua, 2022. "Driving strategy of connected and autonomous vehicles based on multiple preceding vehicles state estimation in mixed vehicular traffic," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 596(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. Mingmin Guo & Zheng Wu & Huibing Zhu, 2018. "Empirical study of lane-changing behavior on three Chinese freeways," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-22, January.
    2. He, Jia & He, Zhengbing & Fan, Bo & Chen, Yanyan, 2020. "Optimal location of lane-changing warning point in a two-lane road considering different traffic flows," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    3. 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.
    4. Arno Eichberger & Zsolt Szalay & Martin Fellendorf & Henry Liu, 2022. "Advances in Automated Driving Systems," Energies, MDPI, vol. 15(10), pages 1-5, May.
    5. Kanagaraj, Venkatesan & Treiber, Martin, 2018. "Self-driven particle model for mixed traffic and other disordered flows," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 1-11.
    6. Jin, Wen-Long, 2012. "A kinematic wave theory of multi-commodity network traffic flow," Transportation Research Part B: Methodological, Elsevier, vol. 46(8), pages 1000-1022.
    7. Sermpis, Dimitris, 2007. "The Influence of Geometry on Operational Performance of Signal-Controlled Junctions," Journal of the Transportation Research Forum, Transportation Research Forum, vol. 46(1).
    8. Ma, Yanli & Lv, Zhiliang & Zhang, Peng & Chan, Ching-Yao, 2021. "Impact of lane changing on adjacent vehicles considering multi-vehicle interaction in mixed traffic flow: A velocity estimating model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 566(C).
    9. Melo, Sandra & Macedo, Joaquim & Baptista, Patrícia, 2019. "Capacity-sharing in logistics solutions: A new pathway towards sustainability," Transport Policy, Elsevier, vol. 73(C), pages 143-151.
    10. Shi, Kunsong & Wu, Yuankai & Shi, Haotian & Zhou, Yang & Ran, Bin, 2022. "An integrated car-following and lane changing vehicle trajectory prediction algorithm based on a deep neural network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 599(C).
    11. Matteo Richiardi, 2003. "The New Italian Road Code and the Virtues of the ‘Shame Lane’," LABORatorio R. Revelli Working Papers Series 31, LABORatorio R. Revelli, Centre for Employment Studies.
    12. Rockwell International Science Center, 1992. "Potential Payoffs From Ivhs: A Framework For Analysis," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt7gf7j8n2, Institute of Transportation Studies, UC Berkeley.
    13. 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.
    14. 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.
    15. Ding, Heng & Zhang, Lang & Chen, Jin & Zheng, Xiaoyan & Pan, Hao & Zhang, Weihua, 2023. "MPC-based dynamic speed control of CAVs in multiple sections upstream of the bottleneck area within a mixed vehicular environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 613(C).
    16. Demeulenaere, Xavier, 2020. "How challenges of human reliability will hinder the deployment of semi-autonomous vehicles," Technological Forecasting and Social Change, Elsevier, vol. 157(C).
    17. Li, Linheng & Gan, Jing & Zhou, Kun & Qu, Xu & Ran, Bin, 2020. "A novel lane-changing model of connected and automated vehicles: Using the safety potential field theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 559(C).
    18. Jin, Wen-Long, 2013. "A multi-commodity Lighthill–Whitham–Richards model of lane-changing traffic flow," Transportation Research Part B: Methodological, Elsevier, vol. 57(C), pages 361-377.
    19. Khelfa, Basma & Ba, Ibrahima & Tordeux, Antoine, 2023. "Predicting highway lane-changing maneuvers: A benchmark analysis of machine and ensemble learning algorithms," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 612(C).
    20. Ma, Changxi & Li, Dong, 2023. "A review of vehicle lane change research," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 626(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:gam:jsusta:v:15:y:2023:i:13:p:10146-:d:1179874. 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.