Route-Based Signal Preemption Control of Emergency Vehicle

Mu, Haibo; Song, Yubo; Liu, Linzhong

doi:10.1155/2018/1024382

Cited by 15 publications

(11 citation statements)

References 35 publications

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“…The work in [23] utilizes a multi-objective programming model for emergency vehicle pre-emption at intersections. The main intention of such a model is to clear the emergency vehicles quickly at the intersection and increase the passing rate of normal vehicles by minimizing the emergency vehicle preemption impact.…”

Section: B Ev Preemption Methodsmentioning

confidence: 99%

SG-TSE: Segment-based Geographic Routing and Traffic Light Scheduling for EV Preemption based Negative Impact Reduction on Normal Traffic

Kamble¹,

Kounte²

2021

IJACSA

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Emergency Vehicles (EVs) play a significant role in giving timely assistance to the general public by saving lives and avoiding property damages. The EV preemption models help the EVs to maintain their speed along their path by pre-clearing the normal vehicles from the path. However, few preemption models are designed in literature, and they lack in minimizing the negative impacts of EV preemption on normal vehicle traffic and also negative impacts of normal vehicle traffic on EV speed. To accomplish such goals, the work proposes a Segment-based Geographic routing and Traffic light Scheduling based EV preemption (SG-TSE) that incorporates two mechanisms: Segment based Geographic Routing (SGR) and Dynamic Traffic Light Scheduling and EV Preemption (DTSE) for efficient EV preemption. Firstly, the SGR utilized a geographic routing model through the Segment Heads (SHs) along the selected route and passed the EV arrival messages to the traffic light controller to pre-clear the normal traffic. Secondly, the DTSE designs effective scheduling at traffic lights by dynamically adjusting the green time phase based on the minimum detection distance of EVs to the intersections. Thus, the EVs are passed through the intersections quickly without negatively impacting normal traffic, even the signal head in the red phase. Moreover, the proposed SG-TSE activates the green phase time at the correct time and minimizes the negative impacts on the EV preemption model. Finally, the performance of SG-TSE is evaluated using Network Simulator-2 (NS-2) with different performance metrics and various network traffic scenarios.

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Section: B Ev Preemption Methodsmentioning

confidence: 99%

SG-TSE: Segment-based Geographic Routing and Traffic Light Scheduling for EV Preemption based Negative Impact Reduction on Normal Traffic

Kamble¹,

Kounte²

2021

IJACSA

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“…Although, in reality, routing and pre-emption are coupled, existing methods usually solve them separately. Many of the existing approaches leverage Dijkstra's shortest path algorithm or A* algorithm to get the optimal route (Wang, Ma, and Yang 2013;Mu, Song, and Liu 2018;Kwon, Kim, and Betts 2003;Jotshi, Gong, and Batta 2009;Nordin et al 2012). However, as this line of work assumes that the routes and traffic conditions are fixed and static, they fail to address the dynamic nature of real-world traffic flows.…”

Section: Related Workmentioning

confidence: 99%

EMVLight: A Decentralized Reinforcement Learning Framework for Efficient Passage of Emergency Vehicles

Zhong

Dey

et al. 2022

AAAI

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Emergency vehicles (EMVs) play a crucial role in responding to time-critical events such as medical emergencies and fire outbreaks in an urban area. The less time EMVs spend traveling through the traffic, the more likely it would help save people's lives and reduce property loss. To reduce the travel time of EMVs, prior work has used route optimization based on historical traffic-flow data and traffic signal pre-emption based on the optimal route. However, traffic signal pre-emption dynamically changes the traffic flow which, in turn, modifies the optimal route of an EMV. In addition, traffic signal pre-emption practices usually lead to significant disturbances in traffic flow and subsequently increase the travel time for non-EMVs. In this paper, we propose EMVLight, a decentralized reinforcement learning (RL) framework for simultaneous dynamic routing and traffic signal control. EMVLight extends Dijkstra's algorithm to efficiently update the optimal route for the EMVs in real-time as it travels through the traffic network. The decentralized RL agents learn network-level cooperative traffic signal phase strategies that not only reduce EMV travel time but also reduce the average travel time of non-EMVs in the network. This benefit has been demonstrated through comprehensive experiments with synthetic and real-world maps. These experiments show that EMVLight outperforms benchmark transportation engineering techniques and existing RL-based signal control methods.

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“…ey developed a two-phase algorithm consisting of a relaxation method and a stepwise search strategy to solve the optimal control model. Mu et al conducted several studies of emergency vehicle preemption strategies while considering route-based applications, timed colored Petri Nets, and a nondominated sorting genetic algorithm II [23][24][25].…”

Section: Other Related Preemption Studies Kim Et Al Applied a Genetic...mentioning

confidence: 99%

Advanced Transition Preemption Strategy for Signalized Intersections near Highway-Rail Grade Crossings with Dual Tracks

Chen¹,

Rilett

Wu³

2022

Journal of Advanced Transportation

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The current preemption method for traffic signals at intersections located near highway-rail grade crossings (IHRGC) is known as the standard preemption (SP). The SP strategy is designed to give priority to the phases which clear vehicles off the railroad tracks as quickly as possible before a train arrives at HRGCs and provides the drivers and pedestrians with a minimum warning time (MWT). However, the SP considers neither pedestrian safety nor system efficiency at IHRGCs. As a result, this may lead to safety and delay problems at IHRGCs. To solve the problems, a state-of-the-art transition preemption strategy (TPS) algorithm, named TPS_DT, is developed in this paper. The new TPS algorithm is designed for corridors with multiple HGRCs that have dual tracks. An urban highway corridor with multiple HRGCs in Lincoln, NE, was selected as the study corridor. A calibrated VISSIM model of the study corridor was used to test the safety and efficiency of the proposed algorithm. The algorithm was coded in VAP, which is an add-on module of VISSIM. A roadway-railway corridor with multiple IHRGC and dual rail tracks in Lincoln, NE, was used as the testbed. The Measurements of Effectiveness (MOEs) used for evaluation include the rate of pedestrian phase cutoffs, intersection vehicle delay, and corridor vehicle delay. It was found that TPS_DT can significantly improve pedestrian safety and reduce vehicle delay at IHRGCs. Furthermore, the effects of train arrival prediction errors on safety and efficiency of the IHRGCs are also analyzed in the paper.

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Route-Based Signal Preemption Control of Emergency Vehicle

Cited by 15 publications

References 35 publications

SG-TSE: Segment-based Geographic Routing and Traffic Light Scheduling for EV Preemption based Negative Impact Reduction on Normal Traffic

SG-TSE: Segment-based Geographic Routing and Traffic Light Scheduling for EV Preemption based Negative Impact Reduction on Normal Traffic

EMVLight: A Decentralized Reinforcement Learning Framework for Efficient Passage of Emergency Vehicles

Advanced Transition Preemption Strategy for Signalized Intersections near Highway-Rail Grade Crossings with Dual Tracks

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