V2I Connectivity-Based Dynamic Queue-Jump Lane for Emergency Vehicles: A Deep Reinforcement Learning Approach
Emergency vehicle (EMV) service is a key function of cities and is exceedingly challenging due to urban traffic congestion. A main reason behind EMV service delay is the lack of communication and cooperation between vehicles blocking EMVs. In this paper, we study the improvement of EMV service under V2I connectivity. We consider the establishment of dynamic queue jump lanes (DQJLs) based on real-time coordination of connected vehicles. We develop a novel Markov decision process formulation for the DQJL problem, which explicitly accounts for the uncertainty of drivers' reaction to approaching EMVs. We propose a deep neural network-based reinforcement learning algorithm that efficiently computes the optimal coordination instructions. We also validate our approach on a micro-simulation testbed using Simulation of Urban Mobility (SUMO). Validation results show that with our proposed methodology, the centralized control system saves approximately 15\% EMV passing time than the benchmark system.
Code (0)
등록된 구현이 없습니다.
Tasks
BlockingDeep Reinforcement LearningSimilar Papers 제목 키워드 기반
Dynamic Queue-Jump Lane for Emergency Vehicles under Partially Connected Settings: A Multi-Agent Deep Reinforcement Learning Approach
Emergency vehicle (EMV) service is a key function of cities and is exceedingly challenging due to urban traffic congestion. The main reason behind EMV service delay is the lack of communication and cooperation between ve…
BlockingDeep Reinforcement LearningMulti-agent Reinforcement Learningreinforcement-learning+2Hierarchical GNN-Based Multi-Agent Learning for Dynamic Queue-Jump Lane and Emergency Vehicle Corridor Formation
Emergency vehicles require rapid passage through congested traffic, yet existing strategies fail to adapt to dynamic conditions. We propose a novel hierarchical graph neural network (GNN)-based multi-agent reinforcement …
Multi-agent Reinforcement LearningGraph Neural NetworkFacilitating Emergency Vehicle Passage in Congested Urban Areas Using Multi-agent Deep Reinforcement Learning
Emergency Response Time (ERT) is crucial for urban safety, measuring cities' ability to handle medical, fire, and crime emergencies. In NYC, medical ERT increased 72% from 7.89 minutes in 2014 to 14.27 minutes in 2024, w…
Deep Reinforcement LearningMulti-agent Reinforcement LearningRouting Autonomous Emergency Vehicles in Smart Cities Using Real Time Systems Analogy: A Conceptual Model
Emergency service vehicles like ambulance, fire, police etc. should respond to emergencies on time. Existing barriers like increased congestion, multiple signalized intersections, queued vehicles, traffic phase timing et…
Autonomous VehiclesSchedulingJumping Fluid Models and Delay Stability of Max-Weight Dynamics under Heavy-Tailed Traffic
We say that a random variable is $light$-$tailed$ if moments of order $2+\epsilon$ are finite for some $\epsilon>0$; otherwise, we say that it is $heavy$-$tailed$. We study queueing networks that operate under the Max-We…
Open-Ended Question AnsweringScheduling