Toward Intelligent Traffic Light Control with Quality-of-Service Provisioning
For urban traffic management, this work addresses the problem of quality-of-service provisioning by focusing on worst-case wait times, an improvement over existing average-wait-time-focused approaches.
This paper studies how traffic light scheduling affects worst-case wait time, deriving theoretical bounds and proposing a controller that outperforms fixed-cycle controllers in both light and heavy traffic scenarios.
Today's fixed-cycle traffic signaling is highly suboptimal and aggravates traffic congestion and waste of energy in urban areas. In addition, it offers no quality-of-service guarantee and makes travel time prediction extremely hard. While existing traffic light control research primarily focuses on improving the average wait time of cars, we study in this paper how traffic light scheduling affects the worst-case wait time. In particular, we derive the time a car spends at an intersection in the best-case and the worst-case, respectively. Using the theoretical results, we propose a simple but effective controller and run simulation to verify its performance. The result shows that it works much better than fixed-cycle controllers in both light and heavy traffic scenarios.