Integration of GIS and analytical hierarchy process method for locating road emergency medical services station

Feizizadeh

et al. 2019

IJGI

The appropriate locations of road emergency stations (RESs) can help to decrease the impact of traffic accidents that cause around 50 million injuries per year worldwide. In this research, the appropriateness of existing RESs in the Khuzestan province, Iran, was assessed using an integrated fuzzy analytical hierarchy process (FAHP) and geographic information system (GIS) approach. The data used in this research were collected from different sources, including the department of roads, the department of health, the statistics organization, forensics, police centers, the surveying and geological department, remotely-sensed and global positioning system (GPS) data of accident high crash zones. On the basis of previous studies and the requirements of the Ministry of Health and Medical Education, as well as the department of roads of Iran for the location of RESs, nine criteria and 19 sub-criteria were adopted, including population, safety, environmental indicators, compatible area in RES, incompatible area in RES, type of road, accident high crash zones, traffic level and performance radius. The FAHP yielded the criteria weights and the ideal locations for establishing RESs using GIS analysis and aggregation functions. The resulting map matched the known road accident and high crash zones very well. The results indicated that the current RES stations are not distributed appropriately along the major roads of the Khuzestan province, and a re-arrangement is suggested. The finding of the present study can help decision-makers and authorities to achieve sustainable road safety in the case study area.of medical services, such as pre-hospital medical and transportation, trauma care and assistance for people who in need of emergency care [7]. Definitions differ, but in most cases, the geographical availability and provision of services are introduced as main factors [8,9]. Choosing optimal locations for emergency stations can help to achieve these aims. The locations of road emergency stations (RESs) are one of the most critical factors that can play a role in the degree of damage caused by accidents. Proper distribution of RESs is key to being able to respond to accidents within a standard time [10]. Therefore, determining optimum locations for RESs is a vital issue that road administrators face [11].After cardiovascular diseases, road accidents are the main reason for death in Iran [12][13][14]. Iran has a very high rate of road accidents; it is twenty times greater than that of the world's average [15]. The number of fatalities per 100,000 people per year for Iran is 32.1 (Figure 1). According to a UNICEF report, every 19 min one person passes away on Iran's roads, and every 2 min somebody is informed that one of his/her family members has survived a crash accident but with severe injury that may cause lifetime disability [16]. Each year, road traffic accidents cause the death of almost 28,000 people and disable or injure 300,000 more in Iran [12]. Cost of traffic fatalities in Iran is estimated at $6 billion U.S. every y...

Section: Methodsmentioning

confidence: 99%

“…FAHP has a hierarchical structure. It is based on pairwise comparisons of individual opinions, reducing inconsistency and model uncertainty in decisions [24][25][26][27]. RESs must be located carefully because, in an emergency, staff should be able to reach the site of the accident within 5 min [28].…”

mentioning

confidence: 99%

Traffic Accident Spatial Simulation Modeling for Planning of Road Emergency Services

Feizizadeh

et al. 2019

IJGI

“…In the next step, 10 experts familiar with the region were invited to allocate each criterion a number of 1 to 9, whose result is presented in Table 3. There are multiple methods to combine the expert opinions (Khaki et al 2015), among which the Geomean method was used in this study. The highest weight was allocated to "stream depth" and the least value to "stream ow type".…”

Section: -3-survey and Weightingmentioning

confidence: 99%

Introducing an Efficient Socio-Economically Oriented Data Envelopment Analysis Method for Selecting the Optimum Groundwater Dam Site Location

Maghsoudy

Fathianpour

2021

Preprint

Self Cite

Groundwater dams are commonly constructed in arid and semiarid regions for subsurface storage of water. In this study, an attempt is made to present a new efficiency-based site selection methodology for constructing groundwater dams based on socio-economic approaches. The site selection process was conducted in a semiarid region in Central Iran. A total of 13 different criteria in four major classes of climate, geology, hydrology, and socio-economy were defined. Nine multi-criteria decision making (MCDM) methods were used for site selection, followed by integrating the results by Borda and Copeland algorithms. The data envelopment analysis (DEA) was employed for selecting the optimum groundwater dam site from the same choices. The DEA method is based on efficiency and it is capable for evaluating the performance of each alternative. Four different scenarios, including the CCR-Input, CCR-Output, BCC-Input, BCC-Output, were used to implement the DEA approach. In addition, the socio-economic-based optimization equation was solved by considering three socio-economic criteria as the output of the DEA model. The results show that the CCR method provides the best results for groundwater dam site selection by maximizing the outputs. Two algorithms, namely the Potential Improvement and Input/output contribution, were used to evaluate the performance of each alternative. These algorithms show the amount of improvement for each selected option. The application of the DEA method, as a new approach for groundwater dam site selection, can increase the performance of the selected option and facilitate dam maintenance throughout its operation time.

“…Vahidnia et al used the AHP method to solve the MCDM problem including hospital location selection (17). Khaki et al employed the AHP method as well as the geographic information system (GIS) to locate the roadside rescue and relief stations (18). Daskin and Stern used a hierarchical objective set covering model for emergency medical service vehicle deployment (19).…”

Section: Introductionmentioning

confidence: 99%

Assessment of Fuzzification Effect of AHP and TOPSIS in Site Selection of Roadside EMS Stations

Mansourkhaki

Mojarradi

Ghobadipour

et al. 2019

jorar

INTRODUCTION:In order to prevent and reduce the death and disability rates caused by road accidents, it is necessary to optimize the location of the roadside rescue and relief stations and emergency medical service. Optimal selection of the roadside rescue and relief stations is a multi-criteria decision-making (MCDM) problem and usually involves the analysis of a large number of possible options and evaluation criteria. Nowadays, various MCDM methods are used to solve location problems that may generate different results. The fuzzification of these methods has always been one of the controversial issues with many agreements and disagreements. METHODS: In this study, a review was first performed on the weighting methods including five non-fuzzy weighting methods as row sum, column sum, arithmetic mean, geometric mean, and eigenvalues as well as two fuzzy weighting methods including: "Liu and Chen method" and "Chang Method". Then, the fuzzy and non-fuzzy MCDM methods [including analytic hierarchy process (AHP), fuzzy analytic hierarchy process (FAHP) Chang, FAHP Liu, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and fuzzy TOPSIS (FTOPSIS)] were employed to locate the roadside rescue and relief Stations. Due to insufficient information and all the required layers in Iran, the information of the Interstate-65 (I-65) Highway between Montgomery and Birmingham, Alabama, USA was used in the present study. Finally, the results of these methods were compared using the mean-score, Borda, and Copeland prioritization strategies. FINDINGS: Given the importance and sensitivity of the issue, a combination of the MCDM methods was utilized to locate the roadside rescue and relief stations and the most appropriate non-fuzzy and fuzzy weighting methods were identified and the methods used were compared in terms of complexity, volume and time of computations, and the level of impact of the expert opinion. CONCLUSION:The AHP, FAHP Liu, and Chen, FAHP Chang, and TOPSIS methods yielded more reliable results in locating the roadside rescue and relief stations; in addition, using FTOPSIS fuzzy method was more risky and is not recommended. The non-fuzzy AHP method was identified to be the most reliable method in the present study.