AVARS -- Alleviating Unexpected Urban Road Traffic Congestion using UAVs
Jiaying Guo Michael R. Jones Soufiene Djahel Shen Wang
Reducing unexpected urban traffic congestion caused by en-route events (e.g.,
road closures, car crashes, etc.) often requires fast and accurate reactions to
choose the best-fit traffic signals. Traditional traffic light control systems,
such as SCATS and SCOOT, are not efficient as their traffic data provided by
induction loops has a low update frequency (i.e., longer than 1 minute).
Moreover, the traffic light signal plans used by these systems are selected
from a limited set of candidate plans pre-programmed prior to unexpected
events' occurrence. Recent research demonstrates that camera-based traffic
light systems controlled by deep reinforcement learning (DRL) algorithms are
more effective in reducing traffic congestion, in which the cameras can provide
high-frequency high-resolution traffic data. However, these systems are costly
to deploy in big cities due to the excessive potential upgrades required to
road infrastructure. In this paper, we argue that Unmanned Aerial Vehicles
(UAVs) can play a crucial role in dealing with unexpected traffic congestion
because UAVs with onboard cameras can be economically deployed when and where
unexpected congestion occurs. Then, we propose a system called "AVARS" that
explores the potential of using UAVs to reduce unexpected urban traffic
congestion using DRL-based traffic light signal control. This approach is
validated on a widely used open-source traffic simulator with practical UAV
settings, including its traffic monitoring ranges and battery lifetime. Our
simulation results show that AVARS can effectively recover the unexpected
traffic congestion in Dublin, Ireland, back to its original un-congested level
within the typical battery life duration of a UAV.