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24-32 

24 

 

 

 

Article 

Potential measurement and spatial priorities 

determination for gas station construction using 

WLC and GIS 
Faraz Estelaji1, Alireza Naseri2, Mansour Keshavarzzadeh3, Rahim Zahedi4*, Hossein Yousefi4, 

Abolfazl Ahmadi5 

1Department of Construction Engineering and Management, Faculty of Civil Engineering, Khajeh Nasir Toosi University, 

Tehran, Iran 
2Department of Road and Transport Engineering, Faculty of Civil Engineering, Amirkabir University of Technology, 

Tehran, Iran 
3Department of Mechanical Engineering Science, University of Johannesburg, Johannesburg, South Africa 
4Department of Renewable Energy and Environmental Engineering, University of Tehran, Tehran, Iran 
5School of Advanced Technologies, Iran University of Science and Technology, Tehran, Iran 

A R T I C L E   I N F O 
 

Article history: 
Received 10 February 2022  
Received in revised form 
09 March 2023 
Accepted 12 March 2023 
 
Keywords:  
Geographic Information System (GIS), 
WLC model, Localization, Gas station 
 
*Corresponding author 
Email address: 
rahimzahedi@ut.ac.ir  
 
 
DOI: 10.55670/fpll.futech.2.4.3 
 

A B S T R A C T 
 

Improper location of gas stations leads to waste of resources, time, and user 
dissatisfaction. On the other hand, the optimal location of these facilities will 
have a significant impact not only on the quality of traffic in the network but 
also on their economic success. The aim of this research is the spatial-physical 
organization of inner-city structures with an emphasis on the location of gas 
stations using the weighted linear integrated model method on the GIS platform 
using the descriptive-analytical method. First, the location of the existing 
stations and the areas that need gas stations were determined using the 
weighted linear integrated model (WLC) and ArcGIS. A scoring-based method 
was used to convert the maps into a standard scale ranging from 0 to 1 and 0 to 
255. The Analytical Hierarchy Process (AHP) method and the Expert Choice app 
were used to determine the criteria weights. Then, the GIS and WLC capability 
to provide a suitable model for locating stations was tested. The result states 
that for the construction of gas stations, the Bahmanyar region will be the 
priority. North Khani Abad region is the second priority, and South Khani Abad 
and Esfandiari regions are the following priorities. Finally, with the local 
investigation of the prioritized areas by WLC, it was found that these areas are 
suitable for constructing gas stations. This method can be used for finding a 
suitable location for gas station construction in all other cases. 

1. Introduction 

With the increase in population in big cities, the public 
services demand has increased [1]. Also, with more usage of 
cars, the need to create multiple fuel stations has increased. 
Iran is one of the owners of fuel reserves in the world, and for 
a long time, gasoline and diesel have been used as two 
common car fuels, like most countries in the world. 
Population growth and improper development of cities have 
created many problems for cities, and principled spatial 
organization of urban services can be very effective to a large 
extent in regulating the performance of cities [2]. The issue of 
land and how to use it is considered the leading platform of 
urban planning [3]. Equitable access to land and its optimal 
use and organization is also considered an essential 

component of sustainable development. Today, the concept of 
urban spaces and places has changed qualitatively both from 
a natural and physical point of view and from an economic-
social point of view. It has made the dimensions of land use 
planning and place organization very diverse and rich. The 
physical system of the city and the urban space is considered 
a public resource and life and wealth of the public and public 
good. Its usage can be carefully managed to provide public 
benefits in the present and future [4]. Various methods and 
solutions have been presented in different parts of the world 
to determine the suitable location for gas stations. For 
example, in Switzerland, a study has been conducted by 
determining the desirability of the stations in the form of 
maximizing the objective function whose parameters include 

 

 

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F. Estelaji et al. /Future Technology                                                                                    November 2023| Volume 02 | Issue 04 | Pages 24-32 

25 

 

the factors influencing the desirability of the station, and the 
location has been finalized, which has been done by using one 
of the mathematical optimization models and applying it to 
the mentioned objective function [5]. In Malaysia, the amount 
of incoming traffic to the station is recognized as a parameter 
that indicates the desirability of the station location. In this 
research, with the regression modeling method, a function 
that includes various station characteristics has been defined 
to estimate the amount of incoming traffic to the station. 
Using the resulting function, traffic forecasting in candidate 
points determines the suitable places for the station's 
construction [6]. In Iran, according to the distribution of 
traffic volume in the transportation network, gas station 
localization has been done by using an optimization method 
[7]. The evaluation that is carried out on the plans at different 
levels and stages in selecting the best solutions from among 
the different options makes sure that the material and 
resources of the plans are not wasted. Wherever a mistake 
happens, the agency will find out and fix the defect. The 
existence of a robust evaluation system that controls projects 
at different stages can be of great help in achieving the 
project's goal [8]. The basis of the evaluation is to measure the 
relative merit of different solutions. In short, improving the 
living quality of the community, comprehensiveness, 
increasing participation, uncertainty, comprehensiveness, 
and the use of defined and targeted criteria are among the 
features considered during the evaluation [9]. On the other 
hand, one of the most critical issues in urban planning is the 
placement of urban services. This means that various urban 
activities require suitable spaces, and it is not possible to 
establish them in every area of the city. Therefore, the 
placement of any urban element in a specific physical-spatial 
position of the city is subject to certain principles, rules, and 
mechanisms, which, if followed, will lead to the success and 
functional efficiency of that element in the same place [10]. 
The essential optimal criteria in determining suitable 
locations for urban activities and services can be listed as 
follows [11]: 
• Compatibility: placing compatible usages next to each other 

and separating incompatible uses from each other. 
• Comfort: distance and time are important factors in 

measuring the level of users' comfort because, as a result of 
providing them, ease of access to city services, which is one 
of the main goals of urban planning, becomes possible. 

• Efficiency: means that the chosen place is optimal from an 
economic point of view [12]. 

• Desirability: means preserving and maintaining natural 
factors and creating open and pleasant spaces according to 
the location of roads, buildings, and urban spaces. 

• Health: It means compliance with health standards. 
• Safety standards: The goal is to protect the city against 

possible dangers [13]. 
• Research main question: According to the above studies, in 

this research, the main question is can we reduce the 
problem of traffic and crowding by optimizing the location 
of new fuel stations in the studied area (19th district of 
Tehran)? 

• Research assumption: several parameters can be examined 
to locate fuel supply stations, such as population density, 
access to the road network, available gas stations, etc., and 
examining each of the above factors requires a lot of 
statistics and information. 

Many studies have been done on locating urban services using 
different techniques and methods. WLC and GIS are important 
methods in determining the optimal location of urban uses, 

which have been used in various levels of this system. Some 
of the research conducted with various models and their 
results are mentioned in Table 1. The main task of this 
research is to help urban planners and decision-makers 
determine the optimal location of gas stations so that all 
urban residents can easily access them. 

Table 1. Summary of studies and research background 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Research title Authors 
Publication 

year 
Results 

Spatial modeling 

of areas suitable 

for public libraries 

construction by 

integration of GIS 

and multi-attribute 

decision making: 

Case study 

Tehran, Iran 

Shorabeh et 

al. [14] 
2020 

They standardized the research 

indicators with spatial analysis 

and overlapped them at the last 

stage. The results indicate that 

positions No. 2, 108, 115, 145, 

and 153 are located in optimal 

locations, positions 15, 22, and 

110 are located in partly 

suitable locations, and position 

No. 24 is located in an 

inappropriate position. 

A review on 

criteria and 

decision-making 

techniques in 

solving landfill 

site selection 

problems 

Mat et al. 

[15] 
2017 

The results of this research 

indicated that 7% of Tehran 

municipality's district 5 has 

excellent potential, 26% has 

medium potential, and 67% is 

unsuitable for the construction 

of a gas fuel station. Also, the 

results of their location survey 

were done with the existing 

stations, and they observed 

33% matching in areas with 

high potential and 17% in areas 

with medium potential. 

Assessment of 

sustainable urban 

development 

based on a hybrid 

decision-making 

approach: Group 

fuzzy BWM, 

AHP, and 

TOPSIS–GIS 

Foroozesh 

et al. [16] 
2022 

It was concluded that a very 

limited part of the northern 

Karaj watershed has the 

appropriate capacity for urban 

development. 

Site selection for 

multi-story car 

parks with 

emphasis on urban 

sustainable 

development 

management 

Shafiei 

Nikabadi 

and 

Hashemi 

[17] 

2021 

They considered and suggested 

three points suitable for the 

construction of multi-story 

parking lots. 

Site selection for 

small gas stations 

using GIS 

Mohammadi 

and Ali [18] 
2011 

The results of this research 

have focused on the importance 

of fuel stations and their 

important role in reducing 

traffic nodes, safety and the 

environment. 

 



F. Estelaji et al. /Future Technology                                                                                    November 2023| Volume 02 | Issue 04 | Pages 24-32 

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2. Case study 

The city of Tehran currently has 22 municipal districts, 
and the studied area is located in district 19, located in the 
south of Tehran (Figure 1). District 19 of Tehran is from 51 
degrees 6 minutes to 51 degrees 38 minutes east longitude 
and from 35 degrees and 34 minutes to 35 degrees and 51 
minutes north latitude. It is one of the peripheral areas of the 
city of Tehran that has undergone its formation process 
during the last 30-40 years. District 19 is adjacent to district 
17 from the north, district 16 from the east, and district 18 
from the west. This district has five regions. Zamzam Street 
and Ayatollah Saeidi Highway form the common border 
between District 19 and neighboring areas in the north and 
west. Bahmanyar Street and the northern part of Tondgouyan 
Highway are the eastern borders between District 19 and 
District 16, and it is limited to Azadegan Highway from the 
south [19]. The 19th district of Tehran is located in the 
entrance area of southwest Tehran, has a special place, and 
contains some of the structural elements of the city. The area 
of this district is currently over 2032 hectares, which is about 
3.16% of the area of Tehran (64396 hectares). 

 

 
Figure 1. Case study location 

 
3. Methodology 

The method of this study is descriptive-analytical, and its 
type is practical. The statistics and information required are 
collected through documents referring to the 19th district 
municipality of Tehran, libraries, and field studies at the 
regional level, then to analyze the information and determine 
the current status of the stations. The WLC model was used in 
the GIS environment, which includes five steps in the 
following order to determine the appropriate location for gas 
stations. In addition to combining all the parameters or layers, 
the WLC method also considers the importance of each 
parameter based on the weight given to that parameter. As a 
result, the map resulting from WLC locating has a high ability 
to provide suitable options.  

3.1 Criteria 
To determine suitable areas for constructing gas 

stations, criteria are needed to locate based on them. For this 
purpose, after reviewing the sources and using the opinions 
of the expert group, the criteria for the location of the gas 
station were considered, which are listed in Table 2. It 
represents the general condition of the proposed site. They 
were taken into consideration, while the necessity of using 
operations such as overlay, search, spatial analysis, ground 
reference, and rasterization provided a turning point for the 
effective use of ArcGIS software in this research. Table 3 
shows the required geospatial data. 

 
3.2 Preparation of benchmark maps 

To analyze the compatibility, the layer of roads, the layer 
of residential areas, etc., and the information layer related to 
gas stations were extracted from the digitized maps of land 
use in the ArcGIS environment. Then, after determining the 
square coordinates of the studied area and the number of 
rows and columns in the cellular network, the extracted 
benchmark maps were imported into the ArcGIS environment 
and saved as raster maps to be used in the next step using the 
Distance function [25]. 

3.3 Standardization (fuzzification) of benchmark maps 
The benchmark maps used in this research were on 

different scales, and it was impossible to perform arithmetic 
operations on them. Accordingly, the method based on the 
score range was used to eliminate the effect of different scales 
and convert them into a standard scale between zero to one 
and zero to 255. In this procedure, the following equations are 
used [26]. 

𝑥𝑖𝑗−𝑥𝑗
𝑚𝑖𝑛

𝑥𝑗
𝑚𝑎𝑥−𝑥𝑗

𝑚𝑖𝑛 = 𝑋𝑖𝑗
′                                                                                       (1) 

 
𝑥𝑗
𝑚𝑎𝑥−𝑥𝑖𝑗

𝑥𝑗
𝑚𝑎𝑥−𝑥𝑗

𝑚𝑖𝑛 = 𝑋𝑖𝑗
′                                                                                      (2) 

𝑋′𝑖𝑗  : Standardized score concerning the option j and the 

attribute i 
𝑋𝑖𝑗: Raw score 

𝑋𝑗
𝑚𝑎𝑥  : Maximum score for attribute i 

𝑋𝑗
𝑚𝑖𝑛  : Minimum score for attribute i 

𝑋𝑗
𝑚𝑎𝑥  - 𝑋𝑗

𝑚𝑖𝑛  indicates the range of values related to the 

attribute i.  
The value of standardized scores can be between 0 to 1 and 0 
to 255 [27]. In this research, using the features that exist in 
the fuzzy function of ArcGIS to standardize the maps that 
were prepared in the form of standard maps is used 
appropriately in formats such as uniformly increasing 
patterns and uniformly decreasing patterns. Figure 2 shows 
an example of standardized layers resulting from fuzzy 
functions. 

3.4 Data weighting method  
In this research, to determine the weight of the criteria, 

the two-by-two comparison method, which is used under the 
Analytical Hierarchy Process (AHP) method, was used. In this 
method, the conceptual complexity involved in decision-
making is significantly reduced because, at any given time, 
only two components are considered (Table 4, Figure 3). At 
this stage, Expert Choice software and the method AHP was 
used to produce the importance coefficients of the criteria. 
Table 5 shows the prioritizing location criteria by AHP 
method. 

3.5 Multi-criteria evaluation through the weighted 
linear combination method (WLC) 
The multi-criteria evaluation aims to select the best 

options based on their ranking by evaluating several main 
criteria. There are several methods to analyze the evaluation 
of several criteria, the most important of which include the 
Weighted Linear combination method, Value/Utility 
Function, AHP, Ideal Point, and the Concordance method. The 
weighted linear combination method is the most common 
technique in multi-criteria evaluation analysis that has been 
widely used in the GIS environment.  

 



F. Estelaji et al. /Future Technology                                                                                    November 2023| Volume 02 | Issue 04 | Pages 24-32 

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Table 3. Required geospatial data 
 

Data type Data sample 

Visual (Raster) Area elevation model (Dem) 

Vector 

Roads and streets layer 

Available gas stations layer 

Natural hazards layer (faults) 

Public parking lots layer 

Bus terminals and stations layer 

Fire stations layer 

Schools and educational centers layer 

Hospitals and medical centers layer 

Parks layer 

Residential areas layer 

Descriptive Area population information layer 

 
 
 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
This technique is called the Simple Additive Weighting 

and Scoring method [28]. This method is based on the concept 
of weighted average. The decision-maker directly assigns 
weights to the criteria based on the relative importance of 
each considered criterion. Then, by multiplying the relative 
weight by the value of that attribute, a final value is obtained 
for each alternative. After the final value of each alternative is 
determined, the alternative with the highest value will be the 
most suitable alternative for the intended purpose, which can 
be the optimal land suitability for a specific application (for 
example, a gas station). The weighted linear combination 
method based on GIS includes the following steps: 
1. Specifying a set of evaluation criteria (map layers) and 

sets of possible options. 
2. Standardization of each layer of the benchmark map. 
3. Determining the criterion weight so that relative 

importance weight is directly assigned to each criterion 
map. 

4. Creating standardized map layers (by multiplying the 
standardized map layers by their corresponding 
weights). 

 

    Table 2. Effective parameters in the location of gas station stations 

Compatible parameters 

 
 

Fire stations: the potential and risk of 
danger in different areas of the city, 
according to the number and frequency of 
incidents, leads to identifying vulnerable 
points in fire incidents and places with 
high potential [20]. Therefore, the access 
of fire stations to fuel stations leads to 
reduce these damages. 

 

 
 

Bus terminals and stations: City buses are one 
of the most important parts of city 
transportation. The small distance between the 
terminals and stations to reduce the access time 
to fuel stations leads to the improvement of 
services to citizens. 

 
 

Public parking lots: the proximity 
of parking lots to gas stations leads 
to ease of refueling. Otherwise, 
traveling a long distance for this 
purpose will lead to increased 
crowding and congestion in 
neighborhoods, fuel consumption, 
neighborhood pollution, and noise 
pollution. 

Incompatible parameters 

 
 
Schools: Exposure to chemical 
compounds in gasoline can 
lead to adverse health effects 
such as asthma, headache, and 
cancer [21]. Due to the high 
vulnerability of children and 
teenagers to substances 
affecting health, the distance of 
schools from these fuel 
stations reduces these 
damages. 

 
 

Hospitals: Sick people 
desperately need a healthy 
environment. Avoiding the 
proximity of hospitals to gas 
stations is necessary, 
considering that gasoline is 
placed in the first tier of 
cancer risk by international 
health associations [22]. 

 
 
Residential areas: 
Establishing a calm and safe 
environment for urban 
residents requires staying 
away from gas stations. Due to 
noise pollution and health 
damage from fuel stations, the 
distance of stations from these 
areas is one of the urban 
planning goals [23]. 

 
 

Parks: Urban parks have 
various functions, including air 
pollutant absorption and 
purification, microclimate 
stabilization, and temperature 
adjustment [24]. it is 
necessary to avoid the vicinity 
of parks and gas stations 
because of the chemical 
composition of gasoline and 
the accumulation of cars at gas 
stations. 

 



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5. By applying the collective overlap operation on the 
layers of the weighted standardized map, the total score 
is calculated for each option, and the options are ranked 
according to the total functional score. Furthermore, the 
option with the highest score (rank) is the best. Formally, 
in the decision rule to evaluate each option or 𝐴𝑖, 
equation 3 is used: 

𝐴𝑖 = ∑ 𝑊𝑗𝑋𝑖𝑗
𝑛
𝑗=1                                                                               (3) 

 
𝑋𝑖𝑗: The score concerning option i and the attribute j 

𝑊𝑗: The weight for criterion j 

 
 
 
 
 
 
 
 
 

 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

This research carried out the WLC operation in the 
ArcGIS environment. In addition, the output of the WLC model 
was standardized with a simple linear stretch using the 
STRETCH function in the range of 0-255 to compare the 
scores of the options with the desired situation. The weighted 
linear combination (WLC) method can also be implemented 
using the geographic information system and the overlapping 
capabilities of this system. Overlay techniques in the 
geographic information system allow us to combine and 
combine them to produce a composite map layer (output 
map). This method is practical in the geographic information 
system's raster and vector formats [29]. 

Figure 2. Standardized fuzzy map of distance from city bus terminals and stations 

Table 4. Criteria used in gas station location 

Variable X1 X2 X3 X4 X5 X6 

description 

Distance from 

existing gas 

stations 

Distance from 

main roads 

and streets 

Distance from 

the fault 

Distance from 

public parking 

lots 

Distance from 

the fire station 

Distance from 

bus terminals 

and stations 

Variable weight from 

AHP method 
0.071 0.1 0.148 0.029 0.153 0.025 

Variable X7 X8 X9 X10 X11 X12 

description 
Area 

population 

Distance from 

schools and 

educational 

centers 

Distance from 

hospitals and 

medical 

centers 

Distance from 

parks 

Distance from 

residential 

areas 

The slope of 

the area 

Variable weight from 

AHP method 
0.052 0.035 0.149 0.104 0.072 0.062 

 



F. Estelaji et al. /Future Technology                                                                                    November 2023| Volume 02 | Issue 04 | Pages 24-32 

29 

 

Table 5. Prioritizing location criteria by AHP method 
 

Priority Variable Weight 

1 fire station 0.153 

2 hospitals and medical centers 0.149 

3 Natural hazards (faults) 0.148 

4 parks 0.104 

5 main roads and streets 0.100 

6 residential areas 0.072 

7 existing gas stations 0.071 

8 The slope of the area 0.062 

9 Area Population 0.052 

10 schools and educational centers 0.035 

11 public parking lots 0.029 

12 bus terminals and stations 0.025 

 
 

 
Figure 3. Gas station location flow diagram 

 
4. Discussion 

4.1 Distribution of gas stations in the 19th district of 
Tehran 
According to Figure 4, gas station service coverage is 

unsuitable in district 19. There is no proper distribution 
between the usages mentioned earlier at the region's level. 
Hence, the northern and western areas of the city have good 
access to gas stations. However, the eastern and southeast 
areas and peripheral areas do not have gas stations, including 
North and South Khani Abad, Bahmanyar, Esfandiari, South 
Shariati, and Ismail Abad. In Tehran's 19th district, based on 
the current situation, there are five gas stations named 

Mehran station in Abdul Abad, station 186 in Sports Street in 
Abdul Abad, station 187 in Nemat Abad police station on 
Shahid Kazemi highway, station 204 in Azadegan Kholazir 
and station 207 in Azadegan wet market. According to Figure 
(4), their spatial distribution is such that this service use has 
gathered in the north and southwest of the city, so other areas 
do not have easy access to the existing stations. As a result, 
the center, southeast, and northeast regions suffer from the 
lack of this service, which indicates the incorrect location of 
this service in the region. 

4.2 Location assessment of gas stations 
In general, the optimal location of gas station centers is 

one of the crucial issues affecting the city's economy from 
various dimensions. In other words, inappropriate 
distribution of the mentioned uses, in addition to spending 
high transportation costs to access them, wastes citizens' time 
and creates roadblocks and traffic nodes, and the resulting 
costs are not possible to calculate most of the time. Therefore, 
positioning is a locating analysis that significantly impacts 
reducing costs, increasing accessibility, and launching various 
activities. For this reason, it is considered one of the most 
important and practical implementation projects. As 
mentioned, after preparing the standardized maps to each of 
the mentioned criteria in measuring the level of desirability 
of the location for the establishment of a gas station and 
applying the relevant weights, the resulting maps are entered 
into the WLC model, and by applying different steps on the 
maps, the final output was obtained. As shown in Figure 5, the 
range of changes in the resulting value is categorized from 
0.26 to 0.54. Lands with low values have the lowest land 
suitability for allocating gas stations, respectively; with the 
increase in the range of values, the suitability of lands for the 
construction of said stations also increases, so the highest 
suitability is related to lands with a value of 0.49 and above. 
Therefore, in equal conditions for allocating land to a gas 
station, priority is given to land with a higher value. In any 
case, the values shown on the map can help decide on the 
suitable land to allocate to a gas station at the regional level. 
Of course, it should be noted that the prioritization shown has 
been obtained according to the criteria used and their weight. 
If other uses in the current state occupy the zones with high 
scores, if it is not possible to change the use or it is not cost-
effective, one should go to the following priorities. This model 
significantly reduces the limitations and complications 
caused by a large amount of information and the 
inconsistencies caused by the diversity of the nature of the 
criteria and also reduces the duration of calculations and 
analysis. At the same time, it has relatively good accuracy. In 
analyzing the location of gas station stations, in addition to the 
weighted linear combination (WLC) method, fuzzy methods 
such as AHP and integrated methods such as fuzzy AHP can 
be used. The limitations of the research increased. However, 
one of the advantages of the weighted linear combination 
model is the simplicity and speed of operation of this model 
despite the high accuracy in positioning. Also, weighting gives 
the decision-maker the power to consider the more important 
factors that he considers to be the location problem. It affects 
it with the same importance in the problem, and due to this 
superiority, positioning by the WLC method has a better 
resolution between the spectrums in it. 

 
 
 



F. Estelaji et al. /Future Technology                                                                                    November 2023| Volume 02 | Issue 04 | Pages 24-32 

30 

 

 
Figure 4. The spatial distribution of gas stations 
 
 

 
 
 
 
 

 
 
 
 

 
 
 
 
 

Figure 5. The leveled map of spatial suitability in relation to the establishment of a gas station based on the output of the WLC 

model 



F. Estelaji et al. /Future Technology                                                                                    November 2023| Volume 02 | Issue 04 | Pages 24-32 

31 

 

5. Conclusion 

Gas stations are one of the critical urban service uses due 

to their performance and impact. In recent years, due to the 

rapid growth of urbanization and the reciprocal lack of 

comprehensive planning and management in the urban 

system of Iran, like other urban services, these spaces have 

also faced many problems, which are caused mainly by the 

small number, uneven and disproportionate distribution, lack 

of optimal location and lack of provision of suitable spaces for 

these uses in cities. According to the land use map and the 

field studies carried out on the distribution of the existing gas 

stations in the 19th district of Tehran, it was found that a large 

part of the area, despite the population density, proximity to 

first-class roads, etc., was outside the operating radius of the 

existing stations, which is the reason for the lack of gas 

stations to cover the entire region and the need to locate and 

establish new stations. The research results show that gas 

stations have a disorderly state in terms of expansion. Their 

accumulation in the center and southwest of the city has 

caused the central, southeast, and northeast areas to suffer 

from the lack of this use, which indicates the incorrect 

location of this use at the level of district 19. Therefore, the 

first hypothesis, "in the 19th district of Tehran, the spatial 

distribution of gas stations is unbalanced and does not match 

with common patterns and scientific models," is confirmed. 

On the other hand, the traditional methods of combining 

maps and evaluating several criteria often lack the necessary 

precision and accuracy due to multiple variables, the large 

area, etc. Statistical and mathematical functions in spatial 

analysis are either impossible or very difficult in traditional 

methods. However, as the results of these surveys show, it 

was found that by using the WLC model and the geographic 

information system capabilities and combining these two, it is 

possible to analyze and process a large amount of data and 

analyze difficult and complex issues. The final results of the 

research, which by case-by-case analysis of the priority pixels 

introduced in the output of the model, show that these pixels 

have high standardized scores at levels tending to 0.55 in 

most of the criteria used in land suitability evaluation; 

Therefore, the integration of this model with the geographic 

information system can be used by decision-makers as a 

decision support system (DSS) in the process of optimal 

location of gas station stations. So, the second hypothesis is 

also confirmed, "The weighted linear integrated model is a 

suitable model for the location of gas station stations in the 

19th district of Tehran". This method can be used to find a 

suitable location for gas station construction all over the 

world. 

Ethical issue 

The authors are aware of and comply with best practices in 
publication ethics, specifically with regard to authorship 
(avoidance of guest authorship), dual submission, 
manipulation of figures, competing interests, and compliance 
with policies on research ethics. The authors adhere to 
publication requirements that the submitted work is original 
and has not been published elsewhere. 

Data availability statement 
Datasets analyzed during the current study are available and 

can be given following a reasonable request from the 

corresponding author. 

Conflict of interest 

The authors declare no potential conflict of interest. 

References 

[1] Zahedi, R., S. Daneshgar, and S. Golivari, Simulation and 

optimization of electricity generation by waste to 

energy unit in Tehran. Sustainable Energy 

Technologies and Assessments, 2022. 53: p. 102338. 

[2] Estelaji, F., A. Naseri, and R. Zahedi, Evaluation of the 

Performance of Vital Services in Urban Crisis 

Management. Advances in Environmental and 

Engineering Research, 2022. 3(4): p. 1-19 

[3] Aguilera, T., F. Artioli, and C. Colomb, Explaining the 

diversity of policy responses to platform-mediated 

short-term rentals in European cities: A comparison of 

Barcelona, Paris and Milan. Environment and Planning 

A: Economy and Space, 2021. 53(7): p. 1689-1712. 

[4] Evans, D., M. Stephenson, and R. Shaw, The present and 

future use of ‘land’below ground. Land Use Policy, 

2009. 26: p. S302-S316. 

[5] Jorge, D., G. Molnar, and G.H. de Almeida Correia, Trip 

pricing of one-way station-based carsharing networks 

with zone and time of day price variations. 

Transportation Research Part B: Methodological, 2015. 

81: p. 461-482. 

[6] Gunawan, R.K., A Study of Spatiotemporal Distribution 

of Mobility-On-Demand in Generating Pick-Up/Drop-

Offs Location Placement. Smart Cities, 2021. 4(2): p. 

746-766. 

[7] Nama, M., et al., Machine learning‐based traffic 

scheduling techniques for intelligent transportation 

system: Opportunities and challenges. International 

Journal of Communication Systems, 2021. 34(9): p. 

e4814. 

[8] Zahedi, R., A. Ahmadi, and S. Gitifar, Feasibility study of 

biodiesel production from oilseeds in Tehran province. 

Journal of Renewable and New Energy, 2022 Nov 11. 

[9] Bonevski, B., et al., Reaching the hard-to-reach: a 

systematic review of strategies for improving health 

and medical research with socially disadvantaged 

groups. BMC medical research methodology, 2014. 

14(1): p. 1-29. 

[10] Marušić, B.G. and D. Marušić, Behavioural maps and 

GIS in place evaluation and design. Application of 

geographic information systems, 2012: p. 115-138. 

[11] Özmen, M. and E.K. Aydoğan, Robust multi-criteria 

decision making methodology for real life logistics 

center location problem. Artificial Intelligence Review, 

2020. 53(1): p. 725-751. 

[12] Angeli, D., R. Amrit, and J.B. Rawlings, On average 

performance and stability of economic model 

predictive control. IEEE transactions on automatic 

control, 2011. 57(7): p. 1615-1626. 

[13] Zahedi, R., et al., Modeling and interpretation of 

geomagnetic data related to geothermal sources, 

Northwest of Delijan. Renewable Energy, 2022. 

[14] Shorabeh, S.N., et al., Spatial modeling of areas suitable 

for public libraries construction by integration of GIS 

and multi-attribute decision making: Case study 

Tehran, Iran. Library & Information Science Research, 

2020. 42(2): p. 101017. 



F. Estelaji et al. /Future Technology                                                                                    November 2023| Volume 02 | Issue 04 | Pages 24-32 

32 

 

[15] Mat, N.A., A.M. Benjamin, and S. Abdul-Rahman, A 

review on criteria and decision-making techniques in 

solving landfill site selection problems. Journal of 

Advanced Review on Scientific Research, 2017. 37(1): 

p. 14-32. 

[16] Foroozesh, F., et al., Assessment of sustainable urban 

development based on a hybrid decision-making 

approach: Group fuzzy BWM, AHP, and TOPSIS–GIS. 

Sustainable Cities and Society, 2022. 76: p. 103402. 

[17] Shafiei Nikabadi, M. and F. Hashemi, Site selection for 

multi-story car parks with emphasis on urban 

sustainable development management. Road, 2021. 

29(109): p. 123-140. 

[18] Mohammad, A. and A.A. Ali, Site selection for small gas 

stations using GIS. Scientific Research and Essays, 

2011. 6(15): p. 3161-3171. 

[19] Zahedi, R. and S. Golivari, Investigating Threats to 

Power Plants Using a Carver Matrix and Providing 

Solutions: A Case Study of Iran. International Journal of 

Sustainable Energy and Environmental Research, 

2022. 11(1): p. 23-36. 

[20] Zhu, Y., et al., Analysis and assessment of the Qingdao 

crude oil vapor explosion accident: lessons learnt. 

Journal of Loss Prevention in the Process Industries, 

2015. 33: p. 289-303. 

[21] Manisalidis, I., et al., Environmental and health impacts 

of air pollution: a review. Frontiers in public health, 

2020: p. 14. 

[22] Sharpe, R., et al., Household energy efficiency and 

health: Area-level analysis of hospital admissions in 

England. Environment international, 2019. 133: p. 

105164. 

[23] Daneshgar, S., R. Zahedi, and O. Farahani, 

Evaluation of the concentration of suspended particles 

in underground subway stations in Tehran and its 

comparison with ambient concentrations. Ann Environ 

Sci Toxicol, 2022. 6(1): p. 019-025. 

[24] Xing, Y. and P. Brimblecombe, Urban park layout and 

exposure to traffic-derived air pollutants. Landscape 

and Urban Planning, 2020. 194: p. 103682. 

[25] Moeinaddini, M., et al., Siting MSW landfill using 

weighted linear combination and analytical hierarchy 

process (AHP) methodology in GIS environment (case 

study: Karaj). Waste management, 2010. 30(5): p. 912-

920. 

[26] Abdulkareem, K.H., et al., A new standardisation and 

selection framework for real-time image dehazing 

algorithms from multi-foggy scenes based on fuzzy 

Delphi and hybrid multi-criteria decision analysis 

methods. Neural Computing and Applications, 2021. 

33(4): p. 1029-1054. 

[27] Gemitzi, A., et al., Assessment of groundwater 

vulnerability to pollution: a combination of GIS, fuzzy 

logic and decision making techniques. Environmental 

Geology, 2006. 49(5): p. 653-673. 

[28] Piasecki, K., E. Roszkowska, and A. Łyczkowska-

Hanćkowiak, Simple additive weighting method 

equipped with fuzzy ranking of evaluated alternatives. 

Symmetry, 2019. 11(4): p. 482. 

[29] Wieczorek, W.F. and A.M. Delmerico, Geographic 

information systems. Wiley Interdisciplinary Reviews: 

Computational Statistics, 2009. 1(2): p. 167-186. 

 

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