An analysis on the optimum location of fire department based on ambulance dispatch situation—A case study in Utsunomiya City

Suzuki, Takeyuki; Satoh, Eiji

doi:10.1002/2475-8876.12144

Cited by 5 publications

(11 citation statements)

References 2 publications

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“…They write that such functions can be useful in order to find optimal resource allocations. A similar topic and interest can be found in Sozuki et al 9 They want to optimize the locations of fire departments. In order to do this, they minimize average response times.…”

Section: Introductionmentioning

confidence: 93%

Optimization of distance between fire stations: effects of fire ignition probabilities, fire engine speed and road limitations, property values and weather conditions

Lohmander¹

2021

IRATJ

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A general spatial fire brigade unit network density optimization problem has been solved. The distance to a particular road, from a fire station, is approximated as a continuous variable. It is proved, via integral convolution, that the probability density function of the total travel time, PDFT, is triangular. The size of the fire, when it stopped, is a function of the time it takes until the fire brigade reaches the fire location. An explicit continuous function for the expected total cost per square kilometer, based on the cost per fire station, the PDFT, the exponential fire cost function parameters, the distance between fire stations, and the speed of fire engines, is derived. It is proved that the optimal distance between fire brigade unit positions, OFD, which minimizes the total expected cost, is unique. Then, the OFDs are replaced by integers, OFDIs, for different parameter assumptions. In this process, also the optimal expected total costs are determined. It is proved that the OFD is a strictly decreasing function of the expected number of fires per area unit, a strictly increasing function of the speed of the fire engines, a strictly decreasing function of the parameters of the exponential fire cost function, and a strictly increasing function of the cost per fire station. These effects of parameter changes are also illustrated via graphs in the numerical section.

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Section: Introductionmentioning

confidence: 93%

Optimization of distance between fire stations: effects of fire ignition probabilities, fire engine speed and road limitations, property values and weather conditions

Lohmander¹

2021

IRATJ

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“…Another study [12][13][14][15][16][17][18] predicted travel time by applying parametric (e.g., linear regression, time series models, dynamic traffic assignment models, and Kalman filtering techniques) or nonparametric (e.g., neural network models, simulation models, Bayesian models, and support vector regression) methods.…”

Section: Travel Time Prediction Of Emergency Servicesmentioning

confidence: 99%

“…Deterministic or Stochastic Travel Time [7][8][9][10][11] Parametric or Nonparametric Methods [12][13][14][15][16][17][18] Geospatial Analysis [19][20][21][22][23] Table 1. Previous Studies on the Travel Time of Emergency Services…”

Section: Approach Previous Studiesmentioning

confidence: 99%

Prediction of Travel Time of Fire Service using Kriging and Weighted-sum Technique

2022

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The accessibility of city fire services is an important indicator for evaluating fire services and optimizing fire resource allocation. For firefighting, rescue, and first-aid activities, it is recommended that the time from fire vehicles leaving the garage to arrive at the scene is less than 5 minutes. Therefore, the travel time of fire services is one of the main concerns for many researchers. This study assumes that changing the urban environment affects the travel time of fire services. Therefore, in this study, weights were applied over the years to predict the travel time of fire service by using the kriging technique. As a result of the case study, temporal factors (elapsed year, term of travel time, and time spent) did not significantly affect travel time prediction accuracy using the kriging technique. As observed in previous studies, it is confirmed that the prediction accuracy is high because it is less affected by traffic-related factors at short travel distances. The results of this study contribute to the development of spatial analysis techniques to improve the accuracy of travel-time prediction.

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“…NSGA-II multi-objective algorithm is used to optimize the results obtained with one or more objective functions. The steps of this algorithm are as follows (Seshadri, A., 2006).…”

Section: Selection Of the Best Location By Genetic Algorithm Ii-nsgamentioning

confidence: 99%

“…The purpose of this study is to analyze the optimum location of fire departments. The point of view of this research is to minimize the response time by confirming emergency services dispatched and absent situations (Suzuki, T., & Satoh, E., 2020). Jing et al (2019):" Location optimization of urban fire stations: Access and service coverage."…”

mentioning

confidence: 99%

Introducing a New Model for Locating the Location of Firefighting Forces Based on Fuzzy Region and Nondominated Sorting Genetic Algorithm

Hassani¹,

Sharabyan²,

Qasabeh³

2022

AzJHPC

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The establishment of fire stations is considered an essential part of the security of any city. At the time of an accident, the location of fire stations is essential for timely and quick relief. The delay in providing aid causes irreparable damage to the life and property of the city's people, and the correct location of fire stations can prevent such incidents from happening, which is necessary to achieve this goal. It is systematic and integrated based on a suitable model. Therefore, in this research, a suitable model for locating the position of firefighting forces based on fuzzy logic and mutated genetic algorithm is proposed, which has two objective functions: one for optimizing the urban coverage and the other for optimizing Building the number of fires stations. The goal is to deploy stations in such a way as to create maximum urban coverage, and on the other hand, considering the cost of deploying each station, the method seeks to reduce the number of stations. The criteria needed for the stations' location have been examined, including the distance from the existing fire station. S the distance from the areas at risk of earthquakes, the high population density, the density of wooden buildings, the proximity to the roads—the main and density of hazardous materials facilities., the data set of fire stations in Istanbul city was used, to check the results and simulation in this research. This data set contains two parts, one of which contains information about the location of the stations, which has 124 data, and the other contains related information to the areas where the fire occurred and has 107 data. In this research, five scenarios were set, the first scenario of two parameters, the second scenario of three parameters, the third, fourth, and fifth scenarios of four parameters and their influence on the choice of the parent were investigated, and the results showed that the best solution is It is obtained that both goals have the same weight in the scenarios. It happens when the number of stations reaches the desired level. In fact, by increasing the number of stations to the appropriate size, the urban coverage amount reached the desired results.

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An analysis on the optimum location of fire department based on ambulance dispatch situation—A case study in Utsunomiya City

Cited by 5 publications

References 2 publications

Optimization of distance between fire stations: effects of fire ignition probabilities, fire engine speed and road limitations, property values and weather conditions

Optimization of distance between fire stations: effects of fire ignition probabilities, fire engine speed and road limitations, property values and weather conditions

Prediction of Travel Time of Fire Service using Kriging and Weighted-sum Technique

Introducing a New Model for Locating the Location of Firefighting Forces Based on Fuzzy Region and Nondominated Sorting Genetic Algorithm

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