Literature Review of Analytical Models on Emergency Vehicle Service: Location, Dispatching, Routing and Preemption Control

Lu, Lili; Wang, Shuaian

doi:10.1109/itsc.2019.8916885

Cited by 15 publications

(9 citation statements)

References 37 publications

(40 reference statements)

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“…Panahi et al [8] have designed a dynamic routing strategy that enables EVs to select the routes with the shortest travel time by leveraging the real-time information system. The intersection level is the EV preemption service(EVPS), and the critical issues related to this include vehicle detection, preemption strategies, and the impact of EVs priority on the entire traffic flow [1].…”

Section: Related Workmentioning

confidence: 99%

“…S HORTENING the travel time of EVs is a research hotspot of intelligent transportation systems [1], and it is essential to save lives and reduce property losses [2]. EVs are special-purpose vehicles such as ambulances, fire engines, or police vehicles that perform emergency tasks in the actual scene, bound by no traffic rules on ensuring safety, e.g., driving route, driving direction, driving speed, and traffic lights.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Real-Time Traffic Signal Control Strategy for Emergency Vehicles

Zhong

Chen

2022

IEEE Access

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Intelligent traffic signal control strategies for emergency vehicles (EV) is a research hotspot in intelligent transportation systems. Its research results will promote the shortening of the travel time of EVs, which is vital to saving lives and reducing property losses. This paper proposes a novel real-time traffic signal control strategy. The first step is called on-demand signal timing for reducing road saturation, which makes it possible that ordinary vehicles(OV) give way to EVs and make EVs travel faster, according to three indicators: the emergency response level(ERL), the congestion level of the road section(CLRS), and the time urgency level(TUL). The second step is called novel signal preemption, which combines nonintrusive preemption and intrusive preemption. This step provides green indicates and makes EVs pass through intersections quickly without stopping. The final step is called the recovery cycle strategy, which restores the road network to the normal situation as soon as possible by using linear programming(LP) to find the shortest green time in each phase after an EV passes the intersection. This paper conducts simulation experiments on an urban traffic simulator SUMO. The experimental results show that our novel strategy can shorten the travel time and reduce the traffic impact on the road network caused by prioritizing the EVs significantly. Compared with the fixed-time control method (FTCM), Min's flexible signal preemption method(FSPM), and Qin's intrusive signal preemption method (ISPM), our method can optimize travel time by up to 62.85%, 50.83%, and 11.62%, respectively.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Real-Time Traffic Signal Control Strategy for Emergency Vehicles

Zhong

Chen

2022

IEEE Access

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“…Please refer to Lu and Wang (2019) and Humagain et al (2020) for a thorough survey of conventional routing optimization and traffic signal pre-emption methods. We would also like to point out that the conventional methods prioritize EMV passage and have significant disturbances on the traffic flow which increases the average non-EMV travel time.…”

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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“…[20] has proposed pre-emption strategies for multiple EMV requests. Please refer to [21] and [22] for a thorough survey of conventional routing optimization and traffic signal pre-emption methods. We would also like to point out that the conventional methods prioritize EMV passage and have significant disturbances on the traffic flow which increases the average non-EMV travel time.…”

Section: Supplementary Materials S1 Related Workmentioning

confidence: 99%

A Decentralized Reinforcement Learning Framework for Efficient Passage of Emergency Vehicles

Su¹,

Zhong²,

Dey³

et al. 2021

Preprint

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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 pre-emption without accounting for the coupling between route these two subproblems. As a result, the planned route often becomes suboptimal. In addition, these approaches also do not focus on minimizing disruption to the overall traffic flow. To address these issues, we introduce EMVLight in this paper. This is 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 an EMV in real-time as it travels through the traffic network. Consequently, the decentralized RL agents learn network-level cooperative traffic signal phase strategies that reduce EMV travel time and the average travel time of non-EMVs in the network. We have carried out comprehensive experiments with synthetic and real-world maps to demonstrate this benefit. Our results show that EMVLight outperforms benchmark transportation engineering techniques as well as existing RL-based traffic signal control methods.

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Literature Review of Analytical Models on Emergency Vehicle Service: Location, Dispatching, Routing and Preemption Control

Cited by 15 publications

References 37 publications

A Novel Real-Time Traffic Signal Control Strategy for Emergency Vehicles

A Novel Real-Time Traffic Signal Control Strategy for Emergency Vehicles

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

A Decentralized Reinforcement Learning Framework for Efficient Passage of Emergency Vehicles

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