TR2023-059

Optimization-based Coordination and Control of Traffic Lights and Mixed Traffic in Multi-Intersection Environments

    •  Suriyarachchi, N., Quirynen, R., Baras, J.S., Di Cairano, S., ,, "Optimization-based Coordination and Control of Traffic Lights and Mixed Traffic in Multi-Intersection Environments", American Control Conference (ACC), DOI: 10.23919/​ACC55779.2023.10156306, May 2023, pp. 3162-3168.
      BibTeX TR2023-059 PDF
      • @inproceedings{Suriyarachchi2023may,
      • author = {Suriyarachchi, Nilesh and Quirynen, Rien and Baras, John S. and Di Cairano, Stefano and},
      • title = {Optimization-based Coordination and Control of Traffic Lights and Mixed Traffic in Multi-Intersection Environments},
      • booktitle = {American Control Conference (ACC)},
      • year = 2023,
      • pages = {3162--3168},
      • month = may,
      • publisher = {IEEE},
      • doi = {10.23919/ACC55779.2023.10156306},
      • url = {https://www.merl.com/publications/TR2023-059}
      • }
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  • Research Areas:

    Control, Dynamical Systems, Optimization

Abstract:

Coordinating the flow of traffic through urban areas with multiple intersections is a complex problem whose solution has the potential to improve safety, increase through- put, and optimize energy efficiency. In addition to controlling traffic lights, the introduction of connected and automated vehicles (CAVs) offers opportunities in terms of additional sensing and actuation points within the traffic network. This paper proposes a centralized and a decentralized implemen- tation for the joint coordination and control of both traffic signals and mixed traffic, including CAVs and human driven vehicles (HDVs), in a network of multiple connected traffic intersections. Mixed-integer linear programming (MILP) is used to compute safe control trajectories for both CAVs and traffic light signals, which minimize overall congestion and fuel consumption. Our approaches are validated using extensive traffic simulations on the SUMO platform and they are shown to provide improvements of around 32-60%, 90-96% and 40-60% in travel time, waiting time and fuel consumption, respectively, when compared to gap-based adaptive and timed traffic lights.