Efficient Traffic Coordination Strategies at Intersections Using Multiple Collision Sets

  • Yangan MoEmail author
  • Mengqi Wang
  • Tingting Zhang
  • Hongguang Xu
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 517)


Appropriate traffic coordination at intersections where multiple roads merge plays an important role in modern intelligent transportations systems. In this paper, we try to propose an efficient traffic coordination framework using multiple collision sets. Aiming at the essentially non-convex problem, we try to reformulate the original problem into a mixed binary integer quadratic programming one by proper relaxations. Low complexity solutions are also given afterwards. Numeric results show that the traffic throughput at intersections can be significantly improved compared to the existing investigations.


Intelligent transportations Intersection coordination Traffic optimization Mixed binary integer programming 



This paper was supported by the Natural Science Foundation of China (under Grant No. 91638204 and 61771159), Guangdong Natural Science Foundation under Grant No. 2017A030313392, Shenzhen Fundamental Research Project (under Grant No. JCYJ2017081115369780 and JCYJ20160318094236224).


  1. 1.
    2010 Motor vehicle crashes: Overview. In: Traffic Safety Facts—Research Note (2012)Google Scholar
  2. 2.
    Lin, P., Liu, J., Jin, P.J.: Autonomous vehicle-intersection coordination method in a connected vehicle environment. IEEE Trans. Intell. Transp. Syst. Mag. pp. 37–47 (2017)CrossRefGoogle Scholar
  3. 3.
    Hafner, M.R., Cunningham, D., Caminiti, L.: Cooperative collision avoidance at intersections: algorithms and experiments. IEEE Trans. Intell. Transp. Syst. 1162–1175 (2013)CrossRefGoogle Scholar
  4. 4.
    Lioris, J., Pedarsani, R., Tascikaraoglu, F.Y.: Platoons of connected vehicles can double throughput in urban roads. Transp. Res. Part C, 292–305 (2017)CrossRefGoogle Scholar
  5. 5.
    Kim, K.D., Kumar, P.R.: An MPC-based approach to provable system-wide safety and liveness of autonomous ground traffic. Autom. Control IEEE Trans. 3341–3356 (2014)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Campos, G.R., Falcone, P., Sjoberg, J.: Autonomous cooperative driving: a velocity-based negotiation approach for intersection crossing. In: International IEEE Conference on Intelligent Transportation Systems, pp. 1456–1461 (2013)Google Scholar
  7. 7.
    Onieva, E., Milan\({\acute{e}}\)s, V., Villagr\({\acute{a}}\), J.: Genetic optimization of a vehicle fuzzy decision system for intersections. Expert Syst. Appl. 13148–13157 (2012)Google Scholar
  8. 8.
    Hult, R., Campos, G.R., Falcone, P.: An approximate solution to the optimal coordination problem for autonomous vehicles at intersections. In: American Control Conference, pp. 763–768 (2015)Google Scholar
  9. 9.
    Bienstock, D.: Computational study of a family of mixed-integer quadratic programming problems. In: International IPCO Conference on Integer Programming and Combinatorial Optimization. Springer, pp. 80–94 (1995)Google Scholar
  10. 10.
    Hult, R., Campos, G.R., Steinmetz, E.: Coordination of cooperative autonomous vehicles: toward safer and more efficient road transportation. IEEE Signal Process. Mag. 74–84 (2016)CrossRefGoogle Scholar
  11. 11.
    Kamal, M.A.S., Imura, J.I., Hayakawa, T.: A vehicle-intersection coordination scheme for smooth flows of traffic without using traffic lights. IEEE Trans. Intell. Transp. Syst. 1136–1147 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Yangan Mo
    • 1
    Email author
  • Mengqi Wang
    • 1
  • Tingting Zhang
    • 1
  • Hongguang Xu
    • 1
  1. 1.Shenzhen Graduate SchoolCommunication Engineering Research Center, Harbin Institute of TechnologyShenzhenPeople’s Republic of China

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