Multimodal Traffic Light Control with Connected Vehicles: A Deep Reinforcement Learning Approach

Research output: Contribution to book/Conference proceedings/Anthology/ReportConference contributionContributedpeer-review

Contributors

Abstract

Multimodal traffic light control is a cost-effective way to deal with urban congestion. The development of V2X (Vehicle to Everything) technologies offers unprecedented data and hence new opportunities for situation awareness, but the conventional control algorithms fall short of fully exploiting the real-time vehicle information at the intersections. In this work, a Double Deep Q-learning (DDQL) approach is proposed for multimodal traffic light control with different priority requests in a connected vehicle environment. The proposed DDQL approach is integrated with the existing actuated controller and is readily implementable. The integrated system can terminate the DDQL controller and switch to the actuated controller for safety when an urgent issue occurs such as an electric power outage. The simulation results demonstrate the advantage of the proposed approach compared with actuated control and indicate the reduction of delays for both public transportation and personal vehicle by the proposed approach.

Details

Original languageEnglish
Title of host publication2023 8th International Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (electronic)9781665455305
Publication statusPublished - 11 Sept 2023
Peer-reviewedYes

Publication series

SeriesInternational Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS)

Conference

Title2023 8th International Conference on Models and Technologies for Intelligent Transportation Systems
Abbreviated titleMT-ITS 2023
Conference number8
Duration14 - 16 June 2023
Website
Degree of recognitionInternational event
LocationHoliday Inn ‘Port Saint Laurent'
CityNice
CountryFrance

External IDs

ORCID /0000-0002-1623-8051/work/147672572
ORCID /0000-0001-6555-5558/work/171064773

Keywords

Sustainable Development Goals

Keywords

  • Delay, Double Deep Q-learning, Multimodal traffic light control, Public transportation priority, V2X technology