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EMVLight: A multi-agent reinforcement learning framework for an emergency vehicle decentralized routing and traffic signal control system.
- Source :
-
Transportation Research Part C: Emerging Technologies . Jan2023, Vol. 146, pN.PAG-N.PAG. 1p. - Publication Year :
- 2023
-
Abstract
- Emergency vehicles (EMVs) play a crucial role in responding to time-critical calls such as medical emergencies and fire outbreaks in urban areas. Existing methods for EMV dispatch typically optimize routes based on historical traffic-flow data and design traffic signal pre-emption accordingly; however, we still lack a systematic methodology to address the coupling between EMV routing and traffic signal control. In this paper, we propose EMVLight, a decentralized reinforcement learning (RL) framework for joint dynamic EMV routing and traffic signal pre-emption. We adopt the multi-agent advantage actor–critic method with policy sharing and spatial discounted factor. This framework addresses the coupling between EMV navigation and traffic signal control via an innovative design of multi-class RL agents and a novel pressure-based reward function. The proposed methodology enables EMVLight to learn network-level cooperative traffic signal phasing strategies that not only reduce EMV travel time but also shortens the travel time of non-EMVs. Simulation-based experiments indicate that EMVLight enables up to a 42.6% reduction in EMV travel time as well as an 23.5% shorter average travel time compared with existing approaches. • Propose a multi-agent reinforcement learning framework for traffic signal control. • Simultaneously solve emergency vehicle (EMV) routing and congestion alleviation. • Formulate a model to capture the establishment of emergency lane for EMV passage. • Empower the actor-critic method with policy sharing and spatially adjusted reward. • Conduct comprehensive experiments on both synthetic and real-world maps. • Reduce 42.6% of EMV travel time and 23.5% average trip time. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 0968090X
- Volume :
- 146
- Database :
- Academic Search Index
- Journal :
- Transportation Research Part C: Emerging Technologies
- Publication Type :
- Academic Journal
- Accession number :
- 160938589
- Full Text :
- https://doi.org/10.1016/j.trc.2022.103955