Related papers: EMVLight: A Decentralized Reinforcement Learning F…
Emergency vehicles (EMVs) play a critical role in a city's response to time-critical events such as medical emergencies and fire outbreaks. The existing approaches to reduce EMV travel time employ route optimization and traffic signal…
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…
Emergency response vehicles (ERVs), such as fire trucks, operate to save lives and mitigate property damage. Emergency vehicle preemption (EVP) is typically implemented to provide the right-of-way to ERVs by giving green signals as they…
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, with half of delays…
Cooperative vehicular technology in recent times has aided in realizing some state-of-art technologies like autonomous driving. Effective and efficient prioritization of emergency vehicles (EVs) using cooperative vehicular technology can…
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.…
Reducing the response time of Emergency Vehicles (EVs) has an undoubted advantage in saving life and property. Implementing pre-emption can aid in achieving it. EVs get unobstructed movement via pre-emption, usually by altering traffic…
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 vehicles blocking…
This paper proposes an eco-driving framework for electric connected vehicles (CVs) based on reinforcement learning (RL) to improve vehicle energy efficiency at signalized intersections. The vehicle agent is specified by integrating the…
This paper develops a decentralized reinforcement learning (RL) scheme for multi-intersection adaptive traffic signal control (TSC), called "CVLight", that leverages data collected from connected vehicles (CVs). The state and reward design…
Traffic signal control is of critical importance for the effective use of transportation infrastructures. The rapid increase of vehicle traffic and changes in traffic patterns make traffic signal control more and more challenging.…
This paper proposes a traffic control scheme to alleviate traffic congestion in a network of interconnected signaled lanes/roads. The proposed scheme is emergency vehicle-centered, meaning that it provides an efficient and timely routing…
Traffic signal control aims to coordinate traffic signals across intersections to improve the traffic efficiency of a district or a city. Deep reinforcement learning (RL) has been applied to traffic signal control recently and demonstrated…
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 etc. can prevent…
The Internet of Vehicles (IoV) enables real-time data exchange among vehicles and roadside units and thus provides a promising solution to alleviate traffic jams in the urban area. Meanwhile, better traffic management via efficient traffic…
The ever-increasing heavy traffic congestion potentially impedes the accessibility of emergency vehicles (EVs), resulting in detrimental impacts on critical services and even safety of people's lives. Hence, it is significant to propose an…
Traffic signal control (TSC) is a core challenge in urban mobility, where real-time decisions must balance efficiency and safety. Existing methods - ranging from rule-based heuristics to reinforcement learning (RL) - often struggle to…
Traffic signal control has a great impact on alleviating traffic congestion in modern cities. Deep reinforcement learning (RL) has been widely used for this task in recent years, demonstrating promising performance but also facing many…
Connected and automated vehicles (CAVs) have the potential to enhance driving safety, for example by enabling safe vehicle following and more efficient traffic scheduling. For such future deployments, safety requirements should be…
In this paper we revisit some of the fundamental premises for a reinforcement learning (RL) approach to self-learning traffic lights. We propose RLight, a combination of choices that offers robust performance and good generalization to…