Deep Reinforcement Learning to Maximize Arterial Usage during Extreme
  Congestion
Ashutosh Dutta Milan Jain Arif Khan Arun Sathanur
Collisions, crashes, and other incidents on road networks, if left
unmitigated, can potentially cause cascading failures that can affect large
parts of the system. Timely handling such extreme congestion scenarios is
imperative to reduce emissions, enhance productivity, and improve the quality
of urban living. In this work, we propose a Deep Reinforcement Learning (DRL)
approach to reduce traffic congestion on multi-lane freeways during extreme
congestion. The agent is trained to learn adaptive detouring strategies for
congested freeway traffic such that the freeway lanes along with the local
arterial network in proximity are utilized optimally, with rewards being
congestion reduction and traffic speed improvement. The experimental setup is a
2.6-mile-long 4-lane freeway stretch in Shoreline, Washington, USA with two
exits and associated arterial roads simulated on a microscopic and continuous
multi-modal traffic simulator SUMO (Simulation of Urban MObility) while using
parameterized traffic profiles generated using real-world traffic data. Our
analysis indicates that DRL-based controllers can improve average traffic speed
by 21\% when compared to no-action during steep congestion. The study further
discusses the trade-offs involved in the choice of reward functions, the impact
of human compliance on agent performance, and the feasibility of knowledge
transfer from one agent to other to address data sparsity and scaling issues.