Deep Reinforcement Learning for the Joint Control of Traffic Light
  Signaling and Vehicle Speed Advice
Johannes V. S. Busch Robert Voelckner Peter Sossalla Christian L. Vielhaus Roberto Calandra Frank H. P. Fitzek
Traffic congestion in dense urban centers presents an economical and
environmental burden. In recent years, the availability of vehicle-to-anything
communication allows for the transmission of detailed vehicle states to the
infrastructure that can be used for intelligent traffic light control. The
other way around, the infrastructure can provide vehicles with advice on
driving behavior, such as appropriate velocities, which can improve the
efficacy of the traffic system. Several research works applied deep
reinforcement learning to either traffic light control or vehicle speed advice.
In this work, we propose a first attempt to jointly learn the control of both.
We show this to improve the efficacy of traffic systems. In our experiments,
the joint control approach reduces average vehicle trip delays, w.r.t.
controlling only traffic lights, in eight out of eleven benchmark scenarios.
Analyzing the qualitative behavior of the vehicle speed advice policy, we
observe that this is achieved by smoothing out the velocity profile of vehicles
nearby a traffic light. Learning joint control of traffic signaling and speed
advice in the real world could help to reduce congestion and mitigate the
economical and environmental repercussions of today's traffic systems.