IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i14p5168-d864334.html
   My bibliography  Save this article

A Novel Coordination Mechanism for Connected and Automated Vehicles in the Multi-Intersection Road Network

Author

Listed:
  • Yuanhao Zhang

    (Department of Control and Systems Engineering, Nanjing University, Nanjing 210093, China)

  • Jiabao Zhao

    (Department of Control and Systems Engineering, Nanjing University, Nanjing 210093, China)

Abstract

In recent years, connected automated vehicles (CAVs) have attracted much attention, and the coordination strategy of CAVs in isolated intersections has been widely discussed. However, these algorithms for isolated intersections cannot be directly applied in a multi-intersection road network (MiRN). The coordination strategy in the MiRN requires further investigation. This paper proposes a two-tier strategy for CAV coordination in the MiRN. First, we analyze the coordination problem in isolated intersections and formulate it as a mixed-integer programming problem. Then, for the MiRN, we propose a consensus prediction method to estimate the travel time for CAVs with different paths. Finally, a novel coordination approach is given, showing how to determine the optimal path for CAVs. The experimental results demonstrate the efficiency of the proposed strategy under various traffic flow rates. Compared with the fixed signal time assignment method and the actuated signal time assignment method, our method reduces the average travel time by about 74–83% under different flow rates. We also evaluate the impact of parameters on the strategy’s performance and provide some suggestions for setting these parameters.

Suggested Citation

  • Yuanhao Zhang & Jiabao Zhao, 2022. "A Novel Coordination Mechanism for Connected and Automated Vehicles in the Multi-Intersection Road Network," Energies, MDPI, vol. 15(14), pages 1-16, July.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:14:p:5168-:d:864334
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/14/5168/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/15/14/5168/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jin Y. Yen, 1971. "Finding the K Shortest Loopless Paths in a Network," Management Science, INFORMS, vol. 17(11), pages 712-716, July.
    2. Boukouvala, Fani & Misener, Ruth & Floudas, Christodoulos A., 2016. "Global optimization advances in Mixed-Integer Nonlinear Programming, MINLP, and Constrained Derivative-Free Optimization, CDFO," European Journal of Operational Research, Elsevier, vol. 252(3), pages 701-727.
    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. Huili Zhang & Yinfeng Xu & Xingang Wen, 2015. "Optimal shortest path set problem in undirected graphs," Journal of Combinatorial Optimization, Springer, vol. 29(3), pages 511-530, April.
    2. Daria Dzyabura & Srikanth Jagabathula, 2018. "Offline Assortment Optimization in the Presence of an Online Channel," Management Science, INFORMS, vol. 64(6), pages 2767-2786, June.
    3. Li, Xin & Pan, Yanchun & Jiang, Shiqiang & Huang, Qiang & Chen, Zhimin & Zhang, Mingxia & Zhang, Zuoyao, 2021. "Locate vaccination stations considering travel distance, operational cost, and work schedule," Omega, Elsevier, vol. 101(C).
    4. Melchiori, Anna & Sgalambro, Antonino, 2020. "A branch and price algorithm to solve the Quickest Multicommodity k-splittable Flow Problem," European Journal of Operational Research, Elsevier, vol. 282(3), pages 846-857.
    5. Luss, Hanan & Wong, Richard T., 2005. "Graceful reassignment of excessively long communications paths in networks," European Journal of Operational Research, Elsevier, vol. 160(2), pages 395-415, January.
    6. Rinaldi, Marco & Viti, Francesco, 2017. "Exact and approximate route set generation for resilient partial observability in sensor location problems," Transportation Research Part B: Methodological, Elsevier, vol. 105(C), pages 86-119.
    7. Timothy M. Sweda & Irina S. Dolinskaya & Diego Klabjan, 2017. "Adaptive Routing and Recharging Policies for Electric Vehicles," Transportation Science, INFORMS, vol. 51(4), pages 1326-1348, November.
    8. Chen, Bi Yu & Chen, Xiao-Wei & Chen, Hui-Ping & Lam, William H.K., 2020. "Efficient algorithm for finding k shortest paths based on re-optimization technique," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 133(C).
    9. Jianyuan Zhai & Fani Boukouvala, 2022. "Data-driven spatial branch-and-bound algorithms for box-constrained simulation-based optimization," Journal of Global Optimization, Springer, vol. 82(1), pages 21-50, January.
    10. Doan, Xuan Vinh, 2022. "Distributionally robust optimization under endogenous uncertainty with an application in retrofitting planning," European Journal of Operational Research, Elsevier, vol. 300(1), pages 73-84.
    11. Hela Masri & Saoussen Krichen, 2018. "Exact and approximate approaches for the Pareto front generation of the single path multicommodity flow problem," Annals of Operations Research, Springer, vol. 267(1), pages 353-377, August.
    12. Fernández, Elena & Pozo, Miguel A. & Puerto, Justo & Scozzari, Andrea, 2017. "Ordered Weighted Average optimization in Multiobjective Spanning Tree Problem," European Journal of Operational Research, Elsevier, vol. 260(3), pages 886-903.
    13. Radu Baltean-Lugojan & Ruth Misener, 2018. "Piecewise parametric structure in the pooling problem: from sparse strongly-polynomial solutions to NP-hardness," Journal of Global Optimization, Springer, vol. 71(4), pages 655-690, August.
    14. Alessandra Griffa & Mathieu Mach & Julien Dedelley & Daniel Gutierrez-Barragan & Alessandro Gozzi & Gilles Allali & Joanes Grandjean & Dimitri Ville & Enrico Amico, 2023. "Evidence for increased parallel information transmission in human brain networks compared to macaques and male mice," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    15. Campos, Juan S. & Misener, Ruth & Parpas, Panos, 2019. "A multilevel analysis of the Lasserre hierarchy," European Journal of Operational Research, Elsevier, vol. 277(1), pages 32-41.
    16. Chan, Chi Kin & Fang, Fei & Langevin, André, 2018. "Single-vendor multi-buyer supply chain coordination with stochastic demand," International Journal of Production Economics, Elsevier, vol. 206(C), pages 110-133.
    17. Zheng, Xuyue & Wu, Guoce & Qiu, Yuwei & Zhan, Xiangyan & Shah, Nilay & Li, Ning & Zhao, Yingru, 2018. "A MINLP multi-objective optimization model for operational planning of a case study CCHP system in urban China," Applied Energy, Elsevier, vol. 210(C), pages 1126-1140.
    18. Qiang Tu & Han He & Xiaomin Lai & Chuan Jiang & Zhanji Zheng, 2024. "Identifying Critical Links in Degradable Road Networks Using a Traffic Demand-Based Indicator," Sustainability, MDPI, vol. 16(18), pages 1-20, September.
    19. T. Gomes & J. Craveirinha & L. Jorge, 2010. "An effective algorithm for obtaining the whole set of minimal cost pairs of disjoint paths with dual arc costs," Journal of Combinatorial Optimization, Springer, vol. 19(3), pages 394-414, April.
    20. Zhou, Bo & Eskandarian, Azim, 2006. "A Non-Deterministic Path Generation Algorithm for Traffic Networks," 47th Annual Transportation Research Forum, New York, New York, March 23-25, 2006 208047, Transportation Research Forum.

    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:jeners:v:15:y:2022:i:14:p:5168-:d:864334. 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.