Minimizing response time to accidents in big cities: a two ranked level model for allocating fire stations

Bolouri, Samira; Vafaeinejad, Alireza; Alesheikh, Ali Asghar; Aghamohammadi, Hossein

doi:10.1007/s12517-020-05728-6

Cited by 8 publications

(3 citation statements)

References 29 publications

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“…Fire station numbers & location and Layout Plan / Mapping. Samira Bolouri et al (2020) examined a minimizing response time to accidents in big cities: a two-ranked level model for allocating fire stations in Tehran. The study focuses on demand allocation to fire stations at two ranked levels to determine the priorities of fire stations to service relevant demands.…”

Section: Resultsmentioning

confidence: 99%

Industrial Fire Brigade Emergency Response Time Parameters for Oil & Gas Facility

Bujang,

Adam Assim,

Adam

et al. 2023

IJARBSS

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Emergency response operations are essential activities in the oil and gas facility. As an incident in an oil and gas facility area can have a considerable economic and social impact, a response to such an incident must be provided in a very short time. An essential part of every plant emergency organization should be the industrial fire brigade. In relation to the emergency management process, the estimation of response time plays a very crucial role. Response time is the time required by the emergency services, particularly for the industrial fire brigade, to reach the incident point after getting incident information. The use of parameters plays an important role in the successful implementation of emergency response. The decisions for such emergency responses should consider the available emergency resources and other factors such as emergency responders' competency, drill and exercise, fire protection and fire detection system, and the characteristics of surrounding affected oil and gas facilities. This case study considered configuration emergency response time parameters for an industrial fire brigade at an oil and gas facility. Therefore, to predict the response time, consideration must be given to the characteristics of emergency response parameters, their effectiveness, and their efficiency. Having this process will enable the company to improve emergency preparedness and response management. The results obtained from the analysis show that the framework is applicable to designing the response to incident conditions. It is expected that the framework would help in better resource allocation and efficient response to incidents in industrial areas.

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Section: Resultsmentioning

confidence: 99%

Industrial Fire Brigade Emergency Response Time Parameters for Oil & Gas Facility

Bujang,

Adam Assim,

Adam

et al. 2023

IJARBSS

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“…Various optimization models to solve facility location problems, both in the context of cost and service coverage optimizations, are widely studied in the literature [13][14][15]. In [16], an optimization methodology based on response time of the fire stations is proposed to select new facilities.…”

Section: Related Workmentioning

confidence: 99%

Urban Fire Station Location Planning using Predicted Demand and Service Quality Index

Dey¹,

Andrew²,

England³

2021

Preprint

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In this article, we propose a systematic approach for fire station location planning. We develop a machine learning model, based on Random Forest, for demand prediction and utilize the model further to define a generalized index to measure quality of fire service in urban settings. Our model is built upon spatial data collected from multiple different sources. Efficacy of proper facility planning depends on choice of candidates where fire stations can be located along with existing stations, if any. Also, the travel time from these candidates to demand locations need to be taken care of to maintain fire safety standard. Here, we propose a travel time based clustering technique to identify suitable candidates. Finally, we develop an optimization problem to select best locations to install new fire stations. Our optimization problem is built upon maximum coverage problem, based on integer programming. We present a detailed experimental study of our proposed approach in collaboration with city of Victoria Fire Department, MN, USA. Our demand prediction model achieves true positive rate of 70% and false positive rate of 22% approximately. We aid Victoria Fire Department to select a location for a new fire station using our approach. We present detailed results on improvement statistics by locating a new facility, as suggested by our methodology, in the city of Victoria.

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“…Para asistir a la toma de decisión en el dimensionamiento de dichos servicios, los Sistemas de Información Geográfica (SIG) y, en particular, aquellos que permiten generar diversas soluciones ateniendo a diferentes Modelos de Localización-Asignación (MLA), son herramientas de especial interés. El resultado de estos modelos ofrece, como mínimo, propuestas de ubicaciones óptimas o cercanas al óptimo de entre el conjunto de localizaciones candidatas, según el tipo de función objetivo y restricciones operativas (Bolouri et al, 2020). De la misma manera, dichos modelos se pueden usar para evaluar situaciones actuales.…”

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El sistema médico de emergencias de Madrid a prueba: análisis del rendimiento espaciotemporal del SAMUR-PC en los primeros meses de la nueva normalidad postCOVID-19

Pérez-Fernández

Moya-Gómez

2023

BAGE

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Quienes requieren de atención sanitaria de emergencia no pueden esperar. Las ambulancias deben llegar al lugar del suceso lo más rápido posible. Las ambulancias suelen estar asignadas a bases, que se distribuyen por toda la ciudad para minimizar el tiempo de llegada al suceso. Sin embargo, la distribución espacial de los sucesos cambia a lo largo del día, debido al ritmo y uso que las personas hacen de la ciudad. Este artículo evalúa, por medio de modelos de localización-asignación, el desempeño espaciotemporal del SAMUR-PC, el Servicio Médico de Emergencias de Madrid (España) en dos escenarios diferenciados, antes de la pandemia de la COVID-19 y durante los primeros meses de la nueva normalidad. Los resultados muestran que el sistema respondió relativamente bien al cambio de los patrones de los sucesos debidos a la pandemia, aunque hubiese sido necesario hacer algunas intervenciones para garantizar el mismo servicio que antes de la crisis epidemiológica.

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Minimizing response time to accidents in big cities: a two ranked level model for allocating fire stations

Cited by 8 publications

References 29 publications

Industrial Fire Brigade Emergency Response Time Parameters for Oil & Gas Facility

Industrial Fire Brigade Emergency Response Time Parameters for Oil & Gas Facility

Urban Fire Station Location Planning using Predicted Demand and Service Quality Index

El sistema médico de emergencias de Madrid a prueba: análisis del rendimiento espaciotemporal del SAMUR-PC en los primeros meses de la nueva normalidad postCOVID-19

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