AVARS - Alleviating Unexpected Urban Road Traffic Congestion using UAVs

Jiaying Guo, Michael Richard Jones, Soufiene Djahel, Shen Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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 opensource 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 uncongested level within the typical battery life duration of a UAV.
Original languageEnglish
Title of host publication2023 IEEE 98th Vehicular Technology Conference (VTC2023-Fall)
PublisherIEEE
Number of pages6
ISBN (Electronic)9798350329285
ISBN (Print)9798350329292
DOIs
Publication statusPublished - 11 Dec 2023
Event2023 IEEE 98th Vehicular Technology Conference - Sheraton Hong Kong Tung Chung hotel, Hong Kong, Hong Kong
Duration: 10 Oct 202313 Oct 2023
Conference number: 98
https://events.vtsociety.org/vtc2023-fall/

Publication series

NameIEEEVTS Vehicular Technology Conference Proceedings
PublisherIEEE
ISSN (Print)1090-3038
ISSN (Electronic)2577-2465

Conference

Conference2023 IEEE 98th Vehicular Technology Conference
Abbreviated titleVTC2023-Fall
Country/TerritoryHong Kong
CityHong Kong
Period10/10/2313/10/23
Internet address

Cite this