Coalition Game for Emergency Vehicles Re-Routing in Smart Cities

Amer, Hayder M.; Al-Kashoash, Hayder A. A.; Kemp, Andrew H.; Mihaylova, Lyudmila; Mayfield, Martin

doi:10.1109/sam.2018.8448582

Cited by 13 publications

(12 citation statements)

References 11 publications

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“…For that to be considered, real-time traffic information is needed. In [5], an algorithm is proposed which, considering the data collected in V2V and V2I communications, tries to route the emergency vehicles in the area with low congestion. Once again, the system was only simulated, but the results showed a 32% reduction in travel time.…”

Section: Related Workmentioning

confidence: 99%

“…In an emergency, response time is a critical factor. A few minutes delay can result in serious injuries or even death [5]. The delay in emergency responses can be caused by several factors: traffic congestion, lousy route choices or drivers' slow response to the approach of an emergency vehicle (EV).…”

Section: Motivationmentioning

confidence: 99%

“…Increasing the safety in roads implies two fundamental issues: to reduce the number of accidents and to reduce the delay in response time of an emergency vehicle (EV). It is important to remember that delay in response time of an EV can lead to death, severe injuries and economical losses [5]. However, with constant increase of congestion on the roads, problems with the time response efficiency of EVs start to be raised.…”

Section: Introductionmentioning

confidence: 99%

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Mobility Sensing and V2X Communication for Emergency Services

et al. 2022

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Palavras-chavevanet, mobilidade veículos de emergência, sensorização em tempo real ResumoAo longo dos anos, a população mundial que vive em cidades tem vindo a crescer, levantando vários problemas de gestão de mobilidade. Os tempos de viagem, o número de acidentes e a poluição gerada têm vindo a aumentar. Com a evolução das smart cities, do desenvolvimento de sensores, da comunicação IoT e veicular, torna-se possível o processamento de dados em tempo real, assim como a comunicação entre veículos que permite não apenas o melhoramento da mobilidade nas cidades, mas também da segurança nas estradas. Esta dissertação propõe um sistema que recolhe, processa e dissemina dados em tempo real dentro de uma rede veicular, integrando dados de diferentes sensores relacionados com a mobilidade, e desenvolvendo um serviço de disseminação para o alerta de emergências, tendo como foco contribuir para a redução do tempo de resposta de veículos de emergência. Com o auxílio de RADARs e de mensagens enviadas por veículos, que são recolhidos e processados em tempo real, é possível obter informações sobre o congestionamento das vias, permitindo a geração de alertas de trânsito e alertas de emergência que serão disseminados por veículos e outros nós de uma smart city, mais particularmente no aviso de aproximação de veículos de emergência. Os sistemas desenvolvidos foram testados em laboratório e num cenário real de cidade, utilizando uma infraestrutura já implementada (Aveiro Tech City Living Lab) na cidade de Aveiro, Portugal. Com esta abordagem, os resultados obtidos demonstram a possibilidade de transmissão de informações em tempo real entre veículos e unidades rodoviárias para envio de avisos sobre a aproximação de veículos de emergência. Comparando os modos de comunicação infraestrutura-para-veículo, veículo-para-veículo, ou uma abordagem híbrida em que ambas as comunicações são simultaneamente usadas, houve uma redução de um máximo de 80% para 20% de perdas de comunicação na disseminação de avisos, quando a solução hibrida é utilizada.

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Section: Related Workmentioning

confidence: 99%

Section: Motivationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Mobility Sensing and V2X Communication for Emergency Services

et al. 2022

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“…IRT is an important parameter in the design of SC's emergency services. High IRT may significantly increase human and financial losses [38]. Figure 6 shows the probability to service an emergency request (ER) generated from a distant corner of the city within T time units for varying city areas.…”

Section: ) Estimation Of Emergency Response Latencymentioning

confidence: 99%

“…Smart city traffic conditions are influenced by time of the day, season, activities, etc. Bad traffic conditions, as point out in [38], significantly influence ERU journey time, fuel consumption, and air pollution. To study the impact of traffic conditions we varied the traffic congestion probabilities from 0 (no roads are congested) to 0.8 (highly congested roads).…”

Section: ) Emergency Response Latency Under Various Traffic Conditionsmentioning

confidence: 99%

Formal Analysis of Human-Assisted Smart City Emergency Services

et al. 2019

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The concept of IoT-based Smart Cities has gained momentum in recent years. The research in this domain has focused on modeling key characteristics of future smart cities along with exploring their design and implementation aspects from multiple perspectives. There is, however, a lack of research effort to provide a holistic approach towards modeling smart city services. A comprehensive approach has to view the smart city as a dynamic, uncertain and complex environment where multiple events with varying severity occur in a continuous and non-deterministic manner. These events have to be handled efficiently with the available resources. In this paper, we present a holistic approach to model the probabilistic as well as non-deterministic aspects of the emergency management services of a smart city by using probabilistic model checking. Our proposed model captures the emergency events of varying severity occurring at several locations in a continuous and non-deterministic manner. These events are responded to by a smart emergency management unit (SEMU), which dispatches the required emergency response units (ERUs) to the event location. In addition to modeling a completely automated system, we introduce a human-assisted decision-making process in the model to reduce false alarms. The proposed model can be used to study and evaluate key parameters in designing a smart city emergency management system to meet given service level agreements. We have implemented our proposed model using the PRISM model checker and have conducted several case studies to highlight the effectiveness of our proposed approach for different scenarios of varying complexities. INDEX TERMS Smart city emergency services, PRISM, probabilistic model checking.

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