Spatiotemporal representation learning for rescue route selection: An optimized regularization based method

Li, Xin; Niu, Xiaotong; Liu, Guannan

doi:10.1016/j.elerap.2021.101065

Cited by 5 publications

(2 citation statements)

References 27 publications

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“…Yang et al propose a hybrid cuckoo search algorithm based on K optimal routes to optimize the weights and thresholds of a back propagation neural network model to determine the optimal routes in dynamic road networks [38]. Li et al focus on the ambulance driving environment during the rescue process and propose a framework based on optimal regularization [39]. Specifically, by extracting road features and surrounding environmental conditions, the algorithm establishes a regularized linear loss function to prioritize roads and optimize rescue route selection.…”

Section: Machine-learning-based Algorithmsmentioning

confidence: 99%

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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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Section: Machine-learning-based Algorithmsmentioning

confidence: 99%

“…Response time [17], [20], [26], [31], [34], [35], [37], [39], [40], [43] [44], [46], [47], [51] [52], [54]- [56], [58], [64], [70], [72] [80] [95] Response time + Cost of EVs [18], [19] [45], [49], [50] [79], [84], [86] \…”

Section: Signal Preemptionmentioning

confidence: 99%