SAINT+: Self-Adaptive Interactive Navigation Tool+ for Emergency Service Delivery Optimization

Shen, Yiwen; Lee, Jinho; Jeong, Hohyeon; Jeong, Jaehoon; Lee, Eunseok; Du, David Hung-Chang

doi:10.1109/tits.2017.2710881

Cited by 31 publications

(15 citation statements)

References 30 publications

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“…Bouziyane et al [77] use a hybrid algorithm that combines GA and variable neighbourhood search to solve a DVRP with soft time windows, and Ren [76] applies a hybrid GA. Gendreau et al [72] in turn developed a dynamic relocation strategy based on linear programming. Shen et al [73] developed the Self-Adaptive Interactive Navigation Tool+, called "SAINT+" for emergency service delivery optimization. The objective of this tool is to reduce the delivery time of emergency services, taking traffic congestion into account.…”

Section: Application Of Solution Methodsmentioning

confidence: 99%

“…On the other hand, the performance of the more advanced SDVRPs can be evaluated by a simulation model, e.g., discrete-time simulation [47]. Shen et al [73] use Simulation of Urban Mobility (SUMO) for their performance evaluation. Furthermore, various articles describe simulation methods for the evaluation of the effectiveness of their proposed strategy in a realistic simulation environment [38,66,72,82,83,85].…”

Section: Performance Evaluation Of the Solution Methodsmentioning

confidence: 99%

“…Bertsimas and van Ryzin [70,71] elaborate on the Dynamic Traveling Repairman Problem (DTRP). In turn, Gendreau et al [72] analyse the Maximal Expected Coverage Relocation Problem for emergency services and also Patrascu et al [66], Shen et al [73] and Yong and Xinping [74] focus on optimization strategies for the emergency vehicles. In addition, taxi services [67,68], courier services [6,75] and the Min-Max Vehicle Routing Problem (MMVRP) [76] are all types of DVRPs.…”

Section: Police Patrol Routing and The Literature On The Dvrpmentioning

confidence: 99%

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Analysing the Police Patrol Routing Problem: A Review

Dewinter

Vandeviver

Beken

et al. 2020

IJGI

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Police patrol is a complex process. While on patrol, police officers must balance many intersecting responsibilities. Most notably, police must proactively patrol and prevent offenders from committing crimes but must also reactively respond to real-time incidents. Efficient patrol strategies are crucial to manage scarce police resources and minimize emergency response times. The objective of this review paper is to discuss solution methods that can be used to solve the so-called police patrol routing problem (PPRP). The starting point of the review is the existing literature on the dynamic vehicle routing problem (DVRP). A keyword search resulted in 30 articles that focus on the DVRP with a link to police. Although the articles refer to policing, there is no specific focus on the PPRP; hence, there is a knowledge gap. A diversity of approaches is put forward ranging from more convenient solution methods such as a (hybrid) Genetic Algorithm (GA), linear programming and routing policies, to more complex Markov Decision Processes and Online Stochastic Combinatorial Optimization. Given the objectives, characteristics, advantages and limitations, the (hybrid) GA, routing policies and local search seem the most valuable solution methods for solving the PPRP.

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Section: Application Of Solution Methodsmentioning

confidence: 99%

Section: Performance Evaluation Of the Solution Methodsmentioning

confidence: 99%

Section: Police Patrol Routing and The Literature On The Dvrpmentioning

confidence: 99%

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Analysing the Police Patrol Routing Problem: A Review

Dewinter

Vandeviver

Beken

et al. 2020

IJGI

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“…On the basis of the submitted reports by the network nodes, the traffic control center uses a mathematical model to compute the global optimized path for each vehicle with respect to other network nodes. SAINT was applied by Shen et al [24] for an emergency management framework called SAINT+, which used vehicular-to-vehicular communication to decrease the response time of emergency services in case of traffic accidents. Pan et al [25] introduced DIVERT for vehicular networks which is a distributed rerouting and congestion avoidance system.…”

Section: Related Workmentioning

confidence: 99%

T-Coin: Dynamic Traffic Congestion Pricing System for the Internet of Vehicles in Smart Cities

et al. 2020

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Alleviating traffic congestion is one of the main challenges for the Internet of Vehicles (IoV) in smart cities. Many congestion pricing systems have been proposed recently. However, most of them focus on punishing the vehicles that use certain roads during peak hours, neglecting the proven fact that rewards can encourage drivers to follow the rules. Therefore, in this paper, we propose a new congestion pricing system based on reward and punishment policies for the IoV in a smart city environment, where the vehicles are rewarded for voluntarily choosing to take an alternative path to alleviate traffic congestion. The proposed system is implemented using vehicular ad hoc networks, which eliminate the need for installing a costly electronic toll collection system. We propose a new virtual currency called T-Coin (traffic coin), that is used to reward the vehicles for their positive attitude. T-Coin is also used in the tender between vehicles to manage the road reservation process. The proposed system uses dynamic pricing to adapt to peak-hour traffic congestion. Using simulated traffic on a real map of Beijing city, we prove the usefulness of T-Coin as a traffic congestion pricing system.

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“…O problema do gerenciamento de rotas nos centros urbanos tem sido pesquisado por vários trabalhos [Lin et al 2017, Shen et al 2018, Liebig et al 2017, Pan et al 2013, Jeong et al 2016, Brennand et al 2016, de Souza et al 2016] nosúltimos anos, e esta seção possui o intuito de apresentar os principais desafios de pesquisa que este trabalho investiga, destacando os que não foram sanados.…”

Section: Trabalhos Relacionadosunclassified

Um Novo Serviço de Gerenciamento de Tráfego para ITS baseado em Computação em Névoa

Brennand¹,

Pereira²

2019

Workshop De Computação Urbana (CoUrb)

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Frustrações, perdas monetárias, perda de tempo, alto consumo de combustível e emissões de CO2 são um dos principais problemas causados por engarrafamento nos centros urbanos. Com o intuito de sanar tal problema, este artigo propõe o uRbAN - Rotemento veiculAr na Névoa, um serviço de tráfego para controlar os congestionamentos nos centros urbanos. O uRbAN visa reduzir os problemas gerados por um engarrafamento de forma distribuída para sugestão de novas rotas para os veículos. Ao contrário dos trabalhos da literatura, o uRbAN é modelado usando o paradigma da computação em névoa. Com isso, é possível aproveitar os aspectos inerentes de tal paradigma, tais como baixa latência, balanceamento de carga, escalabilidade, a importância da localização geográfica dos dados e a redução do uso da largura de banda. Para validar o uRbAN, nossa avaliação de desempenho foi realizada em um cenário urbano realístico. Quando comparado com outros trabalhos relacionados, o uRbAN apresenta uma redução no tempo de parada em até 70%, na emissão de CO2 em até 29%, no planning time index em até 49% com um desempenho satisfatório no tempo de resposta.

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SAINT+: Self-Adaptive Interactive Navigation Tool+ for Emergency Service Delivery Optimization

Cited by 31 publications

References 30 publications

Analysing the Police Patrol Routing Problem: A Review

Analysing the Police Patrol Routing Problem: A Review

T-Coin: Dynamic Traffic Congestion Pricing System for the Internet of Vehicles in Smart Cities

Um Novo Serviço de Gerenciamento de Tráfego para ITS baseado em Computação em Névoa

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