A Deep Reinforcement Learning Agent for Traffic Intersection Control Optimization

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The efficiency of traffic flows in urban areas largely depends on signal operation. The state-of-the-art traffic signal control strategies are not able to efficiently deal with varying or over-saturated conditions. To optimize the performance of existing traffic signal infrastructure, we present an end-to-end autonomous intersection control agent, based on Deep Reinforcement Learning (DRL). In the recent years, DRL has emerged as a powerful tool, solving control problems involving sequential decision making and demonstrating unprecedented success in complex settings. Our DRL traffic intersection control agent configures the traffic signal regimes based solely on live photo-realistic camera footage. We demonstrate that our agent consistently, significantly outperforms state-of-the-art fixed (pre-defined) and adaptive (induction loop-based) signal control methods under a wide range of ambient conditions, by increasing the traffic throughput and decreasing the intersection traversal time for individual vehicles.

Original languageEnglish
Title of host publication2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019
PublisherIEEE
Pages4222-4229
Number of pages8
ISBN (Electronic)9781538670248
ISBN (Print)978-1-5386-7025-5
DOIs
Publication statusPublished - 28 Nov 2019
Event2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019 - Auckland, New Zealand
Duration: 27 Oct 201930 Oct 2019

Conference

Conference2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019
CountryNew Zealand
CityAuckland
Period27/10/1930/10/19

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Garg, D., Chli, M., & Vogiatzis, G. (2019). A Deep Reinforcement Learning Agent for Traffic Intersection Control Optimization. In 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019 (pp. 4222-4229). [8917361] IEEE. https://doi.org/10.1109/ITSC.2019.8917361