Deep Reinforcement Learning for the Joint Control of Traffic Light Signaling and Vehicle Speed Advice

Publikation: Beitrag in Buch/Konferenzbericht/Sammelband/GutachtenBeitrag in KonferenzbandBeigetragenBegutachtung

Abstract

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.

Details

OriginalspracheEnglisch
Titel22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023
Redakteure/-innenM. Arif Wani, Mihai Boicu, Moamar Sayed-Mouchaweh, Pedro Henriques Abreu, Joao Gama
Herausgeber (Verlag)Institute of Electrical and Electronics Engineers Inc.
Seiten182-187
Seitenumfang6
ISBN (elektronisch)979-8-3503-4534-6
PublikationsstatusVeröffentlicht - 2023
Peer-Review-StatusJa

Konferenz

Titel22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023
Dauer15 - 17 Dezember 2023
StadtJacksonville
LandUSA/Vereinigte Staaten

Externe IDs

ORCID /0000-0001-9430-8433/work/158768051
ORCID /0000-0001-8469-9573/work/161891144

Schlagworte

Schlagwörter

  • Intelligent Transportation Systems, Reinforcement Learning