Schedule-Driven Coordination for Real-Time Traffic Network Control


  • Xiao-Feng Xie Carnegie Mellon University
  • Stephen Smith Carnegie Mellon University
  • Gregory Barlow Carnegie Mellon University



distributed scheduling, multi-agent coordination, intelligent transportation systems


Real-time optimization of the dynamic flow of vehicle traffic through a network of signalized intersections is an important practical problem. In this paper, we take a decentralized, schedule-driven coordination approach to address the challenge of achieving scalable network-wide optimization. To be locally effective, each intersection is controlled independently by an on-line scheduling agent. At each decision point, an agent constructs a schedule that optimizes movement of the observable traffic through the intersection, and uses this schedule to determine the best control action to take over the current look-ahead horizon. Decentralized coordination mechanisms, limited to interaction among direct neighbors to ensure scalability, are then layered on top of these asynchronously operating scheduling agents to promote overall performance. As a basic protocol, each agent queries for newly planned output flows from its upstream neighbors to obtain an optimistic projection of future demand. This projection may incorporate non-local influence from indirect neighbors depending on horizon length. Two additional mechanisms are then introduced to dampen ``nervousness'' and dynamic instability in the network, by adjusting locally determined schedules to better align with those of neighbors. We present simulation results on two traffic networks of tightly-coupled intersections that demonstrate the ability of our approach to establish traffic flows with lower average vehicle wait times than both a simple isolated control strategy and other contemporary coordinated control strategies that use moving average forecast or traditional offset calculation.




How to Cite

Xie, X.-F., Smith, S., & Barlow, G. (2012). Schedule-Driven Coordination for Real-Time Traffic Network Control. Proceedings of the International Conference on Automated Planning and Scheduling, 22(1), 323-331.