Refined Path Planning for Emergency Rescue Vehicles on Congested Urban Arterial Roads via Reinforcement Learning Approach

Yan, Longhao; Wang, Ping; Yang, Jingwen; Hu, Yueyang; Han, Yu; Yao, Junfeng

doi:10.1155/2021/8772688

Cited by 15 publications

(8 citation statements)

References 30 publications

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“…Dilated causal convolution is used to solve the problem of the time dimension of big data. Among them, the expansion coefficient of the convolution kernel can be arbitrarily combined from the range of [1,2,4,8,16,32]. Through comparative experiments, it is found that the experimental results obtained by [1,2,4], [1,2,4,8,16], and [8,16,32] are relatively stable.…”

Section: Prediction Modulementioning

confidence: 99%

“…As shown in Figure 1(b), in the top 10 toll-gates ranked by congestion in China, the vehicle speed may drop to 10 km/h and the congestion index may even be up to 50-60. It might additionally cause traffic accidents, energy consumption, and environmental pollution in the areas of these toll-gates [4]. In order to alleviate or avoid the occurrence of these problems, an increasing number of researchers are working on two aspects: traffic prediction and congestion evaluation of the toll-gates area, in which further studies on quantitively predicted traffic indicators might be an alternative way to solve the problem to evaluate the congestion.…”

Section: Introductionmentioning

confidence: 99%

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CPT-DF: Congestion Prediction on Toll-Gates Using Deep Learning and Fuzzy Evaluation for Freeway Network in China

Shi

Wang

et al. 2023

Journal of Advanced Transportation

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Toll-gates are crucial points of management and key congestion bottleneck for the freeway. In order to avoid traffic deterioration and alleviate traffic congestion in advance, it is necessary to predict and evaluate the congestion in toll-gates scattering in large-scale region of freeway network. In this paper, traffic volume and operational delay time are selected from various traffic indicators to evaluate congestion considering the particular characteristics of the traffic flow within the toll-gate area. The congestion prediction method is designed including two modules: a deep learning (DL) prediction and a fuzzy evaluation. We propose a modified deep learning method based on graph convolutional network (GCN) structure in the fusion of dilated causal mechanism and optimize the method for spatial feature extraction by constructing a new adjacency matrix. This new AI network could process spatiotemporal information of traffic volume and operational delay time, that extracted from large-scaled toll-gates spontaneously, and predict key indicators in 15/30/60 min future time. The evaluation module is proposed based on these predicted results. Then, fuzzy C-means algorithm (FCM) is further modified by determining coupling weight for these two key indicators to detect congestion state. Original traffic data are obtained from the current 186 toll-gates served on the freeway network in Shaanxi Province, China. Experimental tests are carried out based on historical data of four months after preprogressing. The comparative tests show the proposed CPT-DF (congestion prediction on toll-gates using deep learning and fuzzy evaluation) outperforms the current-used other models by 6-15%. The successful prediction could extend to the real-time prediction and early warning of traffic congestion in the toll system to improve the intelligent level of traffic emergency management and guidance on the key road of disasters to some extent.

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Section: Prediction Modulementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

CPT-DF: Congestion Prediction on Toll-Gates Using Deep Learning and Fuzzy Evaluation for Freeway Network in China

Shi

Wang

et al. 2023

Journal of Advanced Transportation

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“…For finding optimal solutions in complex urban environments, Koh et al propose a new deep reinforcement learning-based vehicle route optimization method for finding the best route for vehicles to reach their destinations and avoid congestion in complex urban traffic networks [35]. For a similar problem mentioned above, Yan et al develop a refined rescue route planning environment based on the Markov decision process for congested urban arterial roads [36]. A value-based deep reinforcement learning algorithm is used to plan EVs' routes in this environment, aiming to reach the accident scene in the shortest time and reduce the length of road vehicle queues in the road network.…”

Section: Machine-learning-based Algorithmsmentioning

confidence: 99%

“…Constantinescu and Patrascu aim to find the route with the least road occupancy to shorten the EV response time [29]. Yan et al provide optimal path planning for EVs to reach the scene of traffic accidents with the shortest time and the least length of road queuing [36]. Yang et al propose optimization objectives for path reliability and emergency response time [38].…”

Section: Objective Metricmentioning

confidence: 99%

State-of-the-Art Review on Traffic Control Strategies for Emergency Vehicles

Bai

Luan

et al. 2022

IEEE Access

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Emergency vehicles (EVs) play an essential role in emergency services. One of the most intuitive indicators of the emergency service process is the response time of EVs. This survey focuses on reviewing the latest traffic control strategies to reduce response time during EV traveling. Firstly, it classifies traffic control strategies into route optimization, signal preemption, lane reservation, and mixed traffic control strategies. Then, a systematic literature review of traffic control strategies in terms of different algorithms is presented. In addition, this survey classifies the articles by objective metrics. In addition to response time, several other objective metrics are summarized. Finally, this survey reviews the limitations of existing emergency traffic control strategies and critically analyzes them. Based on this, it indicates the core problems and proposes potential research areas to be explored.

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“…The work of many authors has covered the answer to the first query. In work put forward by [8]- [10], the authors have tried to determine the attribute that should be measured to assess the performance of EMS.…”

Section: Litertaure Reviewmentioning

confidence: 99%

Optimally Allocating Ambulances in Delhi using Mutation based Shuffled Frog Leaping Algorithm

Zaheeruddin¹,

Gupta²

2022

IJACSA

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This paper presents a reliable and competent evolutionary-based approach for improving the response time of Emergency Medical Service (EMS) by efficiently allocating ambulances at the base stations. As the prime objective of EMS is to save people's lives by providing them with timely assistance, thus increasing the chances of a person's survivability, this paper has undertaken the problem of ambulance allocation. The work has been implemented using the proposed mutation-based Shuffled Frog Leaping Algorithm (mSFLA) to provide an optimal allocation plan. The authors have altered the basic SFLA using the concept of mutation to improve the quality of the solution obtained and avoid being trapped in local optima. Considering a set of assumptions, the new algorithm has been applied for allocating 50 ambulances among 11 base stations in Southern Delhi. The working environment of EMS, which includes stochastic requests, travel time, and dynamic traffic conditions, has been considered to attain accurate results. The work has been implemented in the MATLAB simulation environment to find an optimized allocation plan with a minimum average response time. The authors have reduced the average response time by 12.23% with the proposed algorithm. The paper also compares mSFLA, Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) for the stated problem. The algorithms are compared in terms of objective value (average response time), convergence rate, and constancy repeatability to conclude that mSFLA performs better than the other two algorithms.

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Refined Path Planning for Emergency Rescue Vehicles on Congested Urban Arterial Roads via Reinforcement Learning Approach

Cited by 15 publications

References 30 publications

CPT-DF: Congestion Prediction on Toll-Gates Using Deep Learning and Fuzzy Evaluation for Freeway Network in China

CPT-DF: Congestion Prediction on Toll-Gates Using Deep Learning and Fuzzy Evaluation for Freeway Network in China

State-of-the-Art Review on Traffic Control Strategies for Emergency Vehicles

Optimally Allocating Ambulances in Delhi using Mutation based Shuffled Frog Leaping Algorithm

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