IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v16y2019i22p4373-d285202.html
   My bibliography  Save this article

Traffic Simulation Analysis on Running Speed in a Connected Vehicles Environment

Author

Listed:
  • Bin Yu

    (School of Transportation, Southeast University, Nanjing 211189, China)

  • Miyi Wu

    (School of Transportation, Southeast University, Nanjing 211189, China)

  • Shuyi Wang

    (School of Transportation, Southeast University, Nanjing 211189, China)

  • Wen Zhou

    (School of Transportation, Southeast University, Nanjing 211189, China)

Abstract

Connected vehicles (CVs) exchange a variety of information instantly with surrounding vehicles and traffic facilities, which could smooth traffic flow significantly. The objective of this paper is to analyze the effect of CVs on running speed. This study compared the delay time, travel time, and running speed in the normal and the connected states, respectively, through VISSIM (a traffic simulation software developed by PTV company in German). The optimization speed model was established to simulate the decision-makings of CVs in MATLAB, considering the parameters of vehicle distance, average speed, and acceleration, etc. After the simulation, the vehicle information including speed, travel time, and delay time under the normal and the connected states were compared and evaluated, and the influence of different CV rates on the results was analyzed. In a two-lane arterial road, running speed in the connected state increase by 4 km/h, and the total travel time and delay time decrease by 5.34% and 16.76%, respectively, compared to those in the normal state. The optimal CV market penetration rate related to running speed and delay time is 60%. This simulation-based study applies user-defined lane change and lateral behavior rules, and takes different CV rates into consideration, which is more reliable and practical to estimate the impact of CV on road traffic characteristics.

Suggested Citation

  • Bin Yu & Miyi Wu & Shuyi Wang & Wen Zhou, 2019. "Traffic Simulation Analysis on Running Speed in a Connected Vehicles Environment," IJERPH, MDPI, vol. 16(22), pages 1-15, November.
    • Handle: RePEc:gam:jijerp:v:16:y:2019:i:22:p:4373-:d:285202
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/16/22/4373/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/16/22/4373/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. Tian, Junfang & Li, Guangyu & Treiber, Martin & Jiang, Rui & Jia, Ning & Ma, Shoufeng, 2016. "Cellular automaton model simulating spatiotemporal patterns, phase transitions and concave growth pattern of oscillations in traffic flow," Transportation Research Part B: Methodological, Elsevier, vol. 93(PA), pages 560-575.
    4. Chen, T. Donna & Kockelman, Kara M. & Hanna, Josiah P., 2016. "Operations of a shared, autonomous, electric vehicle fleet: Implications of vehicle & charging infrastructure decisions," Transportation Research Part A: Policy and Practice, Elsevier, vol. 94(C), pages 243-254.
    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. 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. 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.
    3. 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).
    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. 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.
    6. 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.
    7. 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.
    8. Mourad, Abood & Puchinger, Jakob & Chu, Chengbin, 2019. "A survey of models and algorithms for optimizing shared mobility," Transportation Research Part B: Methodological, Elsevier, vol. 123(C), pages 323-346.
    9. 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.
    10. Tomoko Sakiyama & Ikuo Arizono, 2019. "Reversible Transitions in a Cellular Automata-Based Traffic Model with Driver Memory," Complexity, Hindawi, vol. 2019, pages 1-8, December.
    11. 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).
    12. Yantao Huang & Kara M. Kockelman, 2020. "What will autonomous trucking do to U.S. trade flows? Application of the random-utility-based multi-regional input–output model," Transportation, Springer, vol. 47(5), pages 2529-2556, October.
    13. Li, Dun & Huang, Youlin & Qian, Lixian, 2022. "Potential adoption of robotaxi service: The roles of perceived benefits to multiple stakeholders and environmental awareness," Transport Policy, Elsevier, vol. 126(C), pages 120-135.
    14. Xin-Wei Li & Hong-Zhi Miao, 2023. "How to Incorporate Autonomous Vehicles into the Carbon Neutrality Framework of China: Legal and Policy Perspectives," Sustainability, MDPI, vol. 15(7), pages 1-24, March.
    15. Treiber, Martin & Kesting, Arne, 2018. "The Intelligent Driver Model with stochasticity – New insights into traffic flow oscillations," Transportation Research Part B: Methodological, Elsevier, vol. 117(PB), pages 613-623.
    16. Yuan, Zijian & Wang, Tao & Zhang, Jing & Li, Shubin, 2022. "Influences of dynamic safe headway on car-following behavior," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 591(C).
    17. Li, Yongfu & Zhao, Hang & Zhang, Li & Zhang, Chao, 2018. "An extended car-following model incorporating the effects of lateral gap and gradient," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 177-189.
    18. Meyer, Jonas & Becker, Henrik & Bösch, Patrick M. & Axhausen, Kay W., 2017. "Autonomous vehicles: The next jump in accessibilities?," Research in Transportation Economics, Elsevier, vol. 62(C), pages 80-91.
    19. Liu, Zhiyong & Li, Ruimin & Dai, Jingchen, 2022. "Effects and feasibility of shared mobility with shared autonomous vehicles: An investigation based on data-driven modeling approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 156(C), pages 206-226.
    20. Hyland, Michael & Mahmassani, Hani S., 2020. "Operational benefits and challenges of shared-ride automated mobility-on-demand services," Transportation Research Part A: Policy and Practice, Elsevier, vol. 134(C), pages 251-270.

    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:jijerp:v:16:y:2019:i:22:p:4373-:d:285202. 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.