Distributed traffic light control at uncoupled intersections with real-world topology by deep reinforcement learning
This addresses traffic management problems for urban planners and engineers, but it is incremental as it applies existing DRL methods to a specific real-world scenario.
The study tackled traffic light control at uncoupled intersections with real-world topology using deep reinforcement learning, showing that irregular intersection arrangements affect performance and that their DRL approach efficiently handles these influences.
This work examines the implications of uncoupled intersections with local real-world topology and sensor setup on traffic light control approaches. Control approaches are evaluated with respect to: Traffic flow, fuel consumption and noise emission at intersections. The real-world road network of Friedrichshafen is depicted, preprocessed and the present traffic light controlled intersections are modeled with respect to state space and action space. Different strategies, containing fixed-time, gap-based and time-based control approaches as well as our deep reinforcement learning based control approach, are implemented and assessed. Our novel DRL approach allows for modeling the TLC action space, with respect to phase selection as well as selection of transition timings. It was found that real-world topologies, and thus irregularly arranged intersections have an influence on the performance of traffic light control approaches. This is even to be observed within the same intersection types (n-arm, m-phases). Moreover we could show, that these influences can be efficiently dealt with by our deep reinforcement learning based control approach.