MPC-Based Emergency Vehicle-Centered Multi-Intersection Traffic Control

Hosseinzadeh, Mehdi; Sinopoli, Bruno; Kolmanovsky, Ilya; Baruah, Sanjoy

doi:10.1109/tcst.2022.3168610

Cited by 13 publications

(8 citation statements)

References 47 publications

(31 reference statements)

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“…This effectively reduced traffic conflicts and negative impacts, and improved the driving efficiency of EVs [16]. Hosseinzadeh et al proposed a new traffic control scheme for EV in traffic congestion by utilizing centralized computing technology and IoV, which not only improved the driving efficiency of EV but also helped to quickly disperse vehicles in congested sections [17]. Alkhatib A et al proposed an intelligent urban road traffic control management system for the intelligent EVs scheduling in congested urban road networks, taking into account urban traffic flow.…”

Section: Related Workmentioning

confidence: 99%

Priority Selection of Road Traffic Net-Works in Emergency Situations Based on Internet of Vehicles

Wang,

Geng

2024

IEEE Access

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The current emergency vehicle priority control methods in road traffic net-works are difficult to cope with the increasing traffic demand. Therefore, a traffic control method based on multi-vehicle collaborative lane change strategy, fleet convergence and gap adjustment model is proposed and its effectiveness is verified. These experiments confirmed that the speed of vehicle Cj+1 under strategy 1 showed a positive correlation with time at 0-5s, reaching an extreme value of 16.85 m/s around 4s, and a negative correlation after 5s. Under strategy 2, its speed showed a negative correlation with time at 0-4s, and a parallel relation-ship after 4s. The multi-vehicle col-laborative lane changing strategy was validated. Except for fleet C with a density of +0.0333m-1, the actual adjustment time of vehicle C1 was gradually increasing at all other densities. The maximum time for B1 adjustment was 9.358s, and the maximum time for C1 adjustment was 10.798s. The longitudinal relative dis-placement of C1 was larger than that of B1. In addition, compared with Model T, the research method increased the average vehicle speed by 12.64% under four different flow rates. Compared with Model Y, the average flow rate of the research method under the four experimental flows was 1.45%. Overall, the research method is effective and feasible in the priority selection control of road traffic net-works. It improves the operational efficiency of emergency vehicle sections and can be effectively applied in actual traffic net-works.INDEX TERMS Internet of Vehicles, Emergency situations, Multi-vehicle col-laborative lane changing strategy, Gap adjustment model, Flow

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Section: Related Workmentioning

confidence: 99%

Priority Selection of Road Traffic Net-Works in Emergency Situations Based on Internet of Vehicles

Wang,

Geng

2024

IEEE Access

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“…Solving the conflict problem at intersections is of the key points of traffic management. Therefore, some studies proposed methods for autonomous traffic signal control [32][33][34][35][36][37][38]. These methods are critical to maximize intersection capacity and reduce vehicle delay.…”

Section: Smart Mobilitymentioning

confidence: 99%

Day-to-Day Dynamic Traffic Flow Assignment Model under Mixed Travel Modes Considering Customized Buses

et al. 2023

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In recent years, customized buses (CBs), a new form of public travel mode between bus and car, has sprung up in China. Its characteristics include flexible routes, each person having a seat and point-to-point travel have attracted travelers who seek high-quality travel, especially car travelers, alleviating traffic congestion at peak periods and leading to a change in urban travel modes. In addition to providing new travel modes, an exclusive bus lane (EBL) is also an effective means to alleviate traffic congestion. Therefore, this paper establishes link impedance functions under mixed travel modes considering the EBL, including customized buses on different kinds of links, and then presents a day-to-day dynamic traffic flow assignment model based on stochastic user equilibrium (SUE). Some conclusions were summarized by numerical case studies. First, the parameter of travelers’ sensitivity to route travel time affects the speed of traffic flow evolution. When it increases to positive infinity, the final state of the traffic network moves from SUE to deterministic user equilibrium (DUE). Second, the parameter on the degree of dependence of travelers on previous experience can not only influence the value size of actual travel time, but also influence the direction of actual travel time evolution. Third, conventional buses and customized buses have higher transportation efficiency than cars, but if the proportion of conventional bus travelers is too large, the total travel time of all travelers in the traffic network may increase. Fourth, when travel demands increase, the proportion of travelers who choose public transit is required to increase to achieve minimum total travel time. Lastly, from the perspective of the whole traffic network in any case, the EBL is not always beneficial. It is recommended to set EBLs when conventional buses and customized bus flows are heavy, which can be judged based on the model established in this paper.

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“…Pre-emption strategies for multiple EMV requests were introduced by [65]. Wu et al [66] approached the emergency vehicle lane clearing from an microscopic motion planning perspective and Hosseinzadeh et al [67] investigated an EMV-centered traffic control scheme through multiple intersections to alleviate traffic congestion. These work scrutinized strategies on pre-emption, but they had not considered EMV's dynamic routing which leads to the optimal path.…”

Section: Literature Reviewmentioning

confidence: 99%

EMVLight: a Multi-agent Reinforcement Learning Framework for an Emergency Vehicle Decentralized Routing and Traffic Signal Control System

Su,

Zhong,

Chow

et al. 2022

Preprint

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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.

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MPC-Based Emergency Vehicle-Centered Multi-Intersection Traffic Control

Cited by 13 publications

References 47 publications

Priority Selection of Road Traffic Net-Works in Emergency Situations Based on Internet of Vehicles

Priority Selection of Road Traffic Net-Works in Emergency Situations Based on Internet of Vehicles

Day-to-Day Dynamic Traffic Flow Assignment Model under Mixed Travel Modes Considering Customized Buses

EMVLight: a Multi-agent Reinforcement Learning Framework for an Emergency Vehicle Decentralized Routing and Traffic Signal Control System

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