Emergency response facility location in transportation networks: A literature review

Liu, Yang; Yuan, Yun; Shen, Jieyi; Wei, Gao

doi:10.1016/j.jtte.2021.03.001

Cited by 39 publications

(33 citation statements)

References 152 publications

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“…The allocation of available teams and the PPE levels set to protect the EMS personnel control the internal factors. The external factor depends on an interaction between emergency facilities location models and transportation networks that include routing and distance to patient case based on the time and location of available ambulance [17]. We do not have data on external factors, as it was not a lever engaged in by the data provider, however, our analysis has shown the potential correlation between the performance of the EMS provider and the variation of COVID-19 cases, the greater potential for panic, governmental decisions on lockdowns, etc.…”

Section: Closing Remarksmentioning

confidence: 99%

Exploring the System Dynamics of COVID-19 and Reconfiguration of Emergency Medical Services

et al. 2022

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Emergency Medical Services (EMS) are essential to the medical healthcare landscape. EMS play a crucial role in maximizing the overall expected survival probability of patients with various health emergencies. During the COVID-19 pandemic, peoples’ lifestyle changed and their decisions to seek medical assistance were mixed with fear. This affected the type and number of missions that EMS responded to worldwide. Furthermore, COVID-19 affected EMS systems in terms of response protocols and personal protective equipment levels. These changes influenced the EMS’ provision of these essential services – fluctuations in demand impacted capacity decisions, the need for PPE altered response times, and lockdowns influenced roadway dynamics. This research focuses on describing and analyzing the performance of a major EMS provider in Lebanon in terms of response time to emergency missions during the COVID-19 pandemic compared to previous years and to the context of EMS in other countries. Results show that the number of calls and number of missions dropped yet, the emergency response time was found to be higher than previous years. The change in response time is most strongly tied to capacity allocation and PPE protocols. This study is an initial step toward exploring EMS system dynamics, with the goal of effectively allocating resources for improved EMS service during periods of heightened uncertainty and variability.

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Section: Closing Remarksmentioning

confidence: 99%

Exploring the System Dynamics of COVID-19 and Reconfiguration of Emergency Medical Services

et al. 2022

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“…Constraint (6) represents that the emergency rescue site in the network covers all emergency demand sites (at least one rescue site is covered).…”

Section: Emergency Rescue Mandatory Sitesmentioning

confidence: 99%

“…As the research continues, more and more scholars are optimizing the model based on the previous studies and outcomes. e main aspects of optimization include the construction of multiobjective function models (e.g., minimization of rescue time and minimization of rescue cost), decision models that consider uncertainty factors (e.g., sharing uncertainty factors and demand uncertainty factors), and dynamic site selection model [6]. For example, Duan and Xiaowan [7] developed a maximum coverage model with station importance as the key factor and with the optimal emergency of response time and emergency rescue stations number as the decision objective.…”

Section: Introductionmentioning

confidence: 99%

Rational Selection of Rail Transit Emergency Site Using Complex Network Topology and Genetic Algorithm

Zhou

Shao

2022

Scientific Programming

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Rail transit network is a convenient and reliable transportation mode in urban areas. With every surge in transportation demand, the network undergoes operational changes like restructuring and making of multiline tracks. Urban rail transit network is a complex network and needs proper emergency services and controlling mechanisms. Unexpected mishaps in rail transit will cause more hazard and panic than the other transportation systems. Therefore, a reliable emergency service station is required to ensure the safety and security of passengers. In the proposed method, the basic statistical properties of complex network topology are considered to construct a P-center site selection model for urban rail transit emergency service stations. The P-center site selection model is solved by genetic algorithm. Validity and reasonableness of the model are demonstrated by implementing it in the Hangzhou emergency service stations for rail transportation. The results show that the P-center site selection model based on statistical properties of complex network topologies can better solve the urban rail transit emergency service station site selection problems. The model minimizes the number of emergency service stations while satisfying the optimal objective function and reduces the construction cost of emergency service stations. The approach has a significant effect on improving system reliability and reducing the risk of emergencies.

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“…e International Strategy for Disaster Reduction (UNISDR) defined a disaster as the negative effects of hazards on vulnerable socioeconomic systems, where vulnerability limits the coping capability to the impact of the hazard [36]. Emergencies are characterized by strong randomness, wide coverage, and a significant negative diffusion effect [37]. Once emergencies occur, it will be in a state of rapid diffusion initially.…”

Section: Related Workmentioning

confidence: 99%

Urban Road Network Emergency: An Integrative Vulnerability Identification Method

Xiang

2021

Complexity

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The vulnerability of an urban road network is affected by many factors, such as internal road network layout, network structure strength, and external destructive events, which have great uncertainty and complexity. Thus, there is still no unified and definite vulnerability analysis scheme available to cities. This paper proposes an integrative vulnerability identification method for urban road networks, which mainly relates to the vulnerability connotation and characteristics analysis of urban road networks during emergency, and vulnerability comprehensive evaluation indices design based on urban road network connectivity, traffic efficiency and performance, and an empirical study on a vulnerability identification method of an urban road network. In the empirical case, a real road network and traffic operation data were used from Science and Technology Park of Shenzhen City, China. In the context of one certain emergency scenario, the stated preference survey method and maximum likelihood method are used to solve the road users’ random travel choice behavior parameters; subsequently, based on the traffic equilibrium distribution prediction, the traffic vulnerability identification methods of the road network in this region were verified before and after the emergency. The method presented here not only considers the impact of network topology changes on road network traffic function during emergency but also considers the impact of dynamic changes in road network traffic demand on vulnerability; therefore, it is closer to the actual distribution of urban road network traffic vulnerability.

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Emergency response facility location in transportation networks: A literature review

Cited by 39 publications

References 152 publications

Exploring the System Dynamics of COVID-19 and Reconfiguration of Emergency Medical Services

Exploring the System Dynamics of COVID-19 and Reconfiguration of Emergency Medical Services

Rational Selection of Rail Transit Emergency Site Using Complex Network Topology and Genetic Algorithm

Urban Road Network Emergency: An Integrative Vulnerability Identification Method

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