# Allocation of Electric Vehicle Charging Stations in Urban Areas Using GIS

Yiwen Gu, Berk Celik, Maxime Hachette, Fabrice Locment, Manuela

Sechilariu

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Yiwen Gu, Berk Celik, Maxime Hachette, Fabrice Locment, Manuela Sechilariu. Allocation of Electric Vehicle Charging Stations in Urban Areas Using GIS. The 26th European Conference on Power Electronics and Applications, GDR SEEDS France & EPE Association, Mar 2025, Paris, France. 10.34746/epe2025-0319. hal-05075546

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# Allocation of Electric Vehicle Charging Stations in Urban Areas Using GIS

Yiwen Gu, Berk Celik, Maxime Hachette, Fabrice Locment, Manuela Sechilariu
Avenues

Université de Technologie de Compiègne
Compiègne, France

yiwen.gu@utc.fr, {berk.celik, maxime.hachette, fabrice.locment, manuela.sechilariu}@utc.fr

***Index Terms***—Electric vehicle, photovoltaic, transport, charge station, geographic information system.

***Abstract***—The current EV charging points can be inadequately equipped to meet the rapidly increasing battery charging demand of EVs. Therefore, in this paper, a Geographic Information System (GIS)-based Multiple-criteria decision analysis (MCDA) approach is adopted to help providing a solution of charging points location problem. Furthermore, the Analytical Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) are used to select the optimal charging station site. A five-step approach is developed: determination of 14 criteria across four perspectives, spatial analysis using QGIS, criteria prioritization with AHP, site ranking in use of TOPSIS, and site capacity estimation. By integrating these methodologies, optimal charging point locations can be identified while ensuring effective deployment and utilization of EV charging infrastructure. Through a complete and considerate analysis, the study identifies the three most appropriate alternative locations for EVs including photovoltaic power.

## I. INTRODUCTION

In 2035, European Parliament has taken the decision to ban the sale of new cars and vans with combustion engines in order to accelerate the increase of electric vehicles (EV) penetration in the transportation sector. In this direction, by the end of 2023, there were approximately 1 600 000 EVs in France, marking a 52.4% increase compared to the previous year [1]. One of the key issues facing policymakers and energy companies is the optimal placement of charging stations. Poorly distributed charging stations could lead to long wait times, range anxiety, and ultimately slow down the adoption of EVs. To address this, an effective charging network must account for population density, travel patterns, and grid capacity, while ensuring that stations are located in both urban and rural areas to support widespread EV usage.

Several methods have been applied to electric vehicle charging station siting problem over last years. At the outset of our research, we selected 25 of the most relevant articles related to the charging station location problem, specifically focusing on methods that utilize GIS. Different approaches are applied when considering the problem of the location of charging stations. Most authors use the multiple-criteria decision-making (MCDM) approach. Iravani et al. [2] developed a zoning method using hexagonal grids with a radius of 2 kilometres to apply the Set Covering Location Problem (SCLP) methodology. In addition, the Maximum Covering Location Problem (MCLP) was also used to maximise the demand served by charging stations. The criteria taken into account are associated with lifestyle habits, such as the number of residents, jobs, cars owned in high-income areas, households without a car, and students. Rane et al. [3] delineated the optimal places for charging station in considering of transportation, social/geographical, energy related and environmental aspect associated with a total of 13 sub-criteria. They used Multi Influencing Factor (MIF) to determine the criteria weighting. Furthermore, TOPSIS was used to rank alternative sites based on performance analysis. Earlier, Khalife et al. [4] categorized criteria into demand and supply categories with 16 sub-criteria. AHP was used for reflecting the attractiveness of each criterion. Then they clustered suitability index map using k-means clustering algorithm. Some papers focus on photovoltaic charging stations, Zhou et al. [5] established four groups of evaluation criteria including natural, economic, technical and social factors. They used the interval number, triangular fuzzy number and hesitant fuzzy linguistic term set to collect the information of the criteria. In the phase of ranking the alternatives, the Interactive and Multicriteria Decision Making (TODIM) method was applied. Besides, Mohammad et al. [6] used Matlab to
simulate the genetic optimization algorithm for locating and determining the capacity of solar sources and charging stations.

Considering papers above, this paper employs GIS-based AHP MCDA approach to determine the optimal alternative site of charging point from environmental, transportation, social and technical perspectives. AHP and TOPSIS methods are applied to establish the weights of criteria and rank the alternative sites. A five-step approach is developed: (i) KPI and criteria determination: 14 criteria which are crucial for assessing the suitability of potential charging station locations are identified from literature review across four different perspectives, (ii) spatial analysis using QGIS: site availability scores are distributed as layers for each criterion, enabling the spatial analysis of potential charging station sites, (iii) criteria prioritization with AHP: The AHP is employed to hierarchically prioritize the identified criteria based on their relative importance in decision-making. This structured approach facilitates the systematic evaluation of criteria to guide optimal site selection, (iv) site ranking in use of TOPSIS: The TOPSIS method is to rank potential charging station sites based on their similarity to the ideal charging station site, (v) capacity estimation of site: in order to estimate the capacity of optimal site, the traffic flow from Google Maps is analysed.

The rest of the paper is organized as follows: the methodology is introduced Section II. The case of study and the results are presented in Section III, and the paper is concluded with future works in Section V.

## II. RESEARCH MODEL

### A. Criteria evaluation system

1) **Criteria selection:** The selection of criteria is the basic work for identifying suitable sites for charge points. Based on the literature review, we identified 13 criteria in 4 perspectives. In consideration of photovoltaic charging points, additional criteria was added. Table I lists the criteria and their interpretations.

2) **Grid definition:** A layer of hexagonal grids with highly precise resolution based on a radius of 50m. The grids facilitate the detailed analysis of criteria by offering a clear visualization and allowing a direct comparison of point density. Drezner and Zemel [7] proved that hexagonal grids are better than square grids, also than triangular grids. The geometry of hexagons is ideal to represent the curves of geographical features. In our research, calculating the number of points in hexagonal grids can be more efficient because the hexagons cover the space without overlapping or leaving gaps, allowing more accurate calculations of local point densities.

Standardizing spatial resolution through hexagonal grids facilitates precise analysis and visualization.

3) **Criteria evaluation:** After defining the criteria, as shown in Table II, a scoring system is created from zero to five depending on the impact of each criterion. The following formula is applied for scaling purposes, in which five is the maximum achievable score:

$$ Score = V_{factor} * \frac{5}{\max(V_{factor-x})} \quad (1) $$

where $V_{factor}$ is the score value of a criteria; $\max(V_{factor-x})$ is the maximum value of the criteria.

### B. Weighting the criteria using AHP

The key of the accurate selection of the best charging points locations is to identify not only the relevant criteria, but also the relative importance of each criterion. This section is essential to prioritise the criteria according to their impact on the overall decision-making process. For this purpose, we used AHP method proposed by Saaty et al. [13], which is an effective approach for structuring and analysing the multi-criteria decision. The importance scale of intensity between these criteria is defined as: i) "1" has equal importance; ii) "3" has moderate importance of one in relation to another; iii) "5" is essential or very important; iv) "7" is very important; v) "9" is extremely important; and, vi) "2", "4", "6", and "8" are the intermediate values between two adjacent judgements.

1) The pairwise comparison matrix was created in $n \times n$, where each element represents the relative importance of the criteria compared with other criteria. The matrix form is as follows:

$$ A = \begin{pmatrix} 1 & a_{12} & \dots & a_{1n} \\ \frac{1}{a_{12}} & 1 & \dots & a_{2n} \\ \vdots & \vdots & \ddots & \vdots \\ \frac{1}{a_{1n}} & \frac{1}{a_{2n}} & \dots & 1 \end{pmatrix} \quad (2) $$

2) Each element $a_{ij}$ is divided by the sum of its column as the next matrix $A'$:

$$ A' = \begin{pmatrix} \frac{a_{11}}{\sum_{i=1}^{n} a_{i1}} & \frac{a_{12}}{\sum_{i=1}^{n} a_{i2}} & \dots & \frac{a_{1n}}{\sum_{i=1}^{n} a_{in}} \\ \frac{a_{21}}{\sum_{i=1}^{n} a_{i1}} & \frac{a_{22}}{\sum_{i=1}^{n} a_{i2}} & \dots & \frac{a_{2n}}{\sum_{i=1}^{n} a_{in}} \\ \vdots & \vdots & \ddots & \vdots \\ \frac{a_{n1}}{\sum_{i=1}^{n} a_{i1}} & \frac{a_{n2}}{\sum_{i=1}^{n} a_{i2}} & \dots & \frac{a_{nn}}{\sum_{i=1}^{n} a_{in}} \end{pmatrix} \quad (3) $$

3) After that, calculate the average values for each rank to obtain the weights $\omega_i$ as follows:

$$ \omega_i = \frac{1}{n} \sum_{j=1}^{n} a'_{ij} \quad (4) $$
<table><caption>TABLE I: Criteria selection</caption><thead><tr><th>Perspectives</th><th>Criteria</th><th>Interpretation</th></tr></thead><tbody><tr><td rowspan="2">Environment</td><td>Forest</td><td>They can not be installed in forested areas due to the natural preservation and fire risks</td></tr><tr><td>Flood risk</td><td>Flooding can damage the infrastructure and impact on charge points' availability</td></tr><tr><td rowspan="4">Transport</td><td>Petrol station</td><td>Existing petrol stations can be converted into electric charge points</td></tr><tr><td>Existing charge point</td><td>They should be located away from existing ones to improve distribution</td></tr><tr><td>Main road</td><td>Accessing them along major roads reduces range anxiety for intercity journeys</td></tr><tr><td>Bus stop</td><td>Easy access to bus stops can encourage the combined use of EVs and public transport</td></tr><tr><td rowspan="6">Social</td><td>Place of work and study</td><td>Vehicles can be easily charged near work and study places</td></tr><tr><td>Attraction</td><td>Long-duration services as attractions are beneficial for charging</td></tr><tr><td>Public building</td><td>Location near public building makes charging accessible during daily activities</td></tr><tr><td>Residential zone</td><td>Areas with a higher number of dwellings have higher charging demand</td></tr><tr><td>Population density</td><td>The higher the population density, the greater the potential charging demand</td></tr><tr><td>Public parking capacity</td><td>Essential for charge points due to long charging times</td></tr><tr><td rowspan="2">Technology</td><td>Electricity grid</td><td>Charge points can be connected without the high cost of grid modifications</td></tr><tr><td>Global horizontal irradiation</td><td>Essential to power photovoltaic charge points</td></tr></tbody></table>

where $a'_{ij}$ is the normalised value.

To measure the strength of judgement on the weights in the pairwise comparison matrix, its consistency index (CI) is measured and evaluated. If a matrix is absolutely consistent as $\lambda_{max} = n$, then $CI = 0$.

$$ \lambda_{max} = \frac{\sum \frac{A \times W}{W}}{n} \qquad (5) $$

where $W$ is the weighting matrix and $n$ is the dimension of the matrix.

$$ CI = \frac{\lambda_{max} - n}{n - 1} \qquad (6) $$

where $\lambda_{max}$ is the maximum principal eigenvalue. Otherwise, if the matrix is not absolutely consistent, $\lambda_{max} > n$, then an index is needed to measure this level of inconsistency. To this end, the consistency rate (CR) is defined. A CR less than 0.1 indicates that the matrix is coherent. If the CR exceeds 0.1, the judgements need to be reviewed and adjusted to improve consistency. Based on this, we determined certain values in the comparison matrix to ensure CR is less than 0.1.

$$ CR = \frac{CI}{RI} \qquad (7) $$

where RI is the mean value of CI for random matrices as defined by Saaty et al. [13].

### C. Ranking the alternative locations using TOPSIS

The TOPSIS method, developed by Hwang et al. [14], is a multi-criteria decision analysis method based on their relative distance from an ideal solution.

1) Create a matrix with all scores of each alternative.

2) Normalise the above matrix using the following equation.

$$ r_{ij} = \frac{a_{ij}}{\sum_{i=1}^{n} a_{ij}^{2}} \qquad (8) $$

where $a_{ij}$ is the criterion score for each alternative.

3) Multiply each value in a column with the corresponding weight.

$$ v_{ij} = \omega_{ij} \times r_{ij} \qquad (9) $$

4) Identify the ideal solutions ($A^*$) and the negative ideal solutions ($A^-$).

$$ A^* = \max_{i=1}^{n} a_{ij} \qquad (10) $$

$$ A^- = \min_{i=1}^{n} a_{ij} \qquad (11) $$

5) Calculate the Euclidean distance between the target alternative and the best($S_i^+$)/worst($S_i^-$) alternative.

$$ S_i^- = \sqrt{\sum_{j=1}^{N} (a_{ij} - A_j^-)^2} \qquad (12) $$

$$ S_i^+ = \sqrt{\sum_{j=1}^{N} (a_{ij} - A_j^*)^2} \qquad (13) $$

6) For each alternative, calculate the similarity ($C_i^*$) with the worst-case alternative.

$$ C_i^* = \frac{S_i^-}{S_i^- + S_i^+} \qquad (14) $$

### III. CASE STUDIES

A case study was performed for the city of Compiègne in France. The analysed city stretches on an area of 53.1 km² and has population around 40 394 [15].
<table><caption>TABLE II: Criteria scoring system</caption><thead><tr><th>Criteria</th><th>Sub-criteria</th><th>Score</th><th>Source</th></tr></thead><tbody><tr><td rowspan="5">C1.1: Distance from forest (m)</td><td>&lt; 100</td><td>0</td><td rowspan="5">OSM</td></tr><tr><td>100 - 150</td><td>1</td></tr><tr><td>150 - 200</td><td>2</td></tr><tr><td>200 - 250</td><td>3</td></tr><tr><td>250 - 300</td><td>4</td></tr><tr><td>&gt; 300</td><td>5</td><td></td></tr><tr><td rowspan="2">C1.2: Flood risk</td><td>No risk</td><td>0</td><td rowspan="2">Flood prevention [8]</td></tr><tr><td>Identified risk</td><td>5</td></tr><tr><td rowspan="2">C2.1: Gas station</td><td>0 station</td><td>0</td><td rowspan="2">OSM</td></tr><tr><td>1 station</td><td>5</td></tr><tr><td rowspan="6">C2.2: Number of existing charging point</td><td>15 - 18 points</td><td>0</td><td rowspan="6">OSM</td></tr><tr><td>11 - 14 points</td><td>1</td></tr><tr><td>8 - 10 points</td><td>2</td></tr><tr><td>4 - 7 points</td><td>3</td></tr><tr><td>1 - 3 points</td><td>4</td></tr><tr><td>0 point</td><td>5</td></tr><tr><td rowspan="2">C2.3: Distance to main roads (m)</td><td>&gt; 50</td><td>0</td><td rowspan="2">OSM</td></tr><tr><td>&lt; 50</td><td>5</td></tr><tr><td rowspan="2">C2.4: Bus stop</td><td>1 stop</td><td>3</td><td rowspan="2">OSM</td></tr><tr><td>2 stops</td><td>5</td></tr><tr><td rowspan="5">C3.1: Place of work or study</td><td>1 location</td><td>1</td><td rowspan="5">OSM</td></tr><tr><td>2 locations</td><td>2</td></tr><tr><td>3 locations</td><td>3</td></tr><tr><td>4 locations</td><td>4</td></tr><tr><td>5 locations</td><td>5</td></tr><tr><td rowspan="2">C3.2: Location to attractions (m)</td><td>&gt; 200</td><td>0</td><td rowspan="2">OSM</td></tr><tr><td>&lt; 200</td><td>5</td></tr><tr><td rowspan="3">C3.3: Public building</td><td>1 location</td><td>1</td><td rowspan="3">OSM</td></tr><tr><td>2 locations</td><td>3</td></tr><tr><td>3 locations</td><td>5</td></tr><tr><td rowspan="3">C3.4: Residential zone</td><td>1 location</td><td>1</td><td rowspan="3">BD TOPO [9]</td></tr><tr><td>2 locations</td><td>3</td></tr><tr><td>3 locations</td><td>5</td></tr><tr><td rowspan="5">C3.5: Population density (km²)</td><td>1 - 169</td><td>1</td><td rowspan="5">INSEE [10]</td></tr><tr><td>169 - 338</td><td>2</td></tr><tr><td>338 - 506</td><td>3</td></tr><tr><td>506 - 675</td><td>4</td></tr><tr><td>675 - 843</td><td>5</td></tr><tr><td rowspan="5">C3.6: Public parking capacity</td><td>1 - 116</td><td>1</td><td rowspan="5">Google Earth</td></tr><tr><td>116 - 230</td><td>2</td></tr><tr><td>230 - 344</td><td>3</td></tr><tr><td>344 - 459</td><td>4</td></tr><tr><td>459 - 574</td><td>5</td></tr><tr><td rowspan="2">C4.1: Distance to power grid (m)</td><td>&gt; 50</td><td>0</td><td rowspan="2">Open data [11]</td></tr><tr><td>&lt; 50</td><td>5</td></tr><tr><td rowspan="6">C4.2: Global horizontal irradiation (kWh/m²)</td><td>1099 &lt;</td><td>0</td><td rowspan="6">SolarGIS [12]</td></tr><tr><td>1099 - 1106</td><td>1</td></tr><tr><td>1106 - 1114</td><td>2</td></tr><tr><td>1114 - 1121</td><td>3</td></tr><tr><td>1121 - 1128</td><td>4</td></tr><tr><td>&gt; 1128</td><td>5</td></tr></tbody></table>

### A. GIS processing

To apply the scoring system as described in Table II, data were collected from various sources, then imported into QGIS. Spatial analysis tools such as buffer and count points in polygon were performed to evaluate each

criterion. The processing results are shown as 14 layers in Fig. 1.

### B. Determine weight

Normally, significance intensities should be assessed on the basis of expert opinion. However, in a first step to validate the method, data is gathered for literature as listed in the Table III), while the rest were determined assumptions. According to Table IV, the most important criterion is “public parking capacity”, with a weighting value of 0.178, which indicates the importance of having enough space for vehicles to be recharged. This rises the question for cities of how to implement balanced parking reduction policies in a city while giving the appropriate room for less polluting electric vehicles. Other important criteria are ranked as follows: “Electricity grid” with a weighting of 0.138, indicates the necessity of reliable access to electricity for charging stations. “Main road” (0.126) as it is preferable to locate charging stations in easily accessible areas. “Flood risk” (0.116) emphasis the need to place charging stations in non-floodable areas to mitigate risks and ensure infrastructure sustainability. The lowest weighting values belong to: “Attraction” (0.030) and “Public building” (0.032). For photovoltaic charge points, as shown in Table IV, the important criteria are similar to electrical charge points, with particular attention paid to the “Global horizontal irradiation” because of the importance of solar gain for their operation.

### C. Ranking sites

After calculating the weightings, values of weightings are combined with the scores for each aspect using the "raster calculator" in QGIS to obtain final suitability maps with all criteria for electric and photovoltaic charge points. The 10 most suitable electric charging points (SA) and 10 most suitable photovoltaic charging points (PV) with the highest score values are determined as shown in Fig. 2.

Table V shows that suitable site SA3, with a $C_i^*$ value of 0.6754, which has the best performance of the top 10 charge points. The performance analysis of the each site is presented in detail as follows:

* SA3 is a site located as city centre underground parking. This site is far from forested areas and flood risks. The most important criterion is its parking capacity, which is greater than other sites. It is close to attractions and has good access to the electricity grid. However, its "existing charging station" score (low weighting) is 0, which means that SA3 is
<figure><img src="image_2.png" /><figcaption>Fig. 1: GIS layer for each criteria</figcaption></figure>
<table><caption>TABLE III: Comparison matrix</caption><thead><tr><th colspan="7">By Aspects</th></tr><tr><th></th><th>C1</th><th>C2</th><th>C3</th><th>C4</th><th>—</th><th>—</th></tr></thead><tbody><tr><th>C1</th><td>1</td><td>1/2 [16]</td><td>1/3 [17]</td><td>2 [17]</td><td>—</td><td>—</td></tr><tr><th>C2</th><td>2 [16]</td><td>1</td><td>1/3 [17]</td><td>2 [17]</td><td>—</td><td>—</td></tr><tr><th>C3</th><td>3 [17]</td><td>3 [17]</td><td>1</td><td>2 [17]</td><td>—</td><td>—</td></tr><tr><th>C4</th><td>1/2 [17]</td><td>1/2 [17]</td><td>1/3 [17]</td><td>1</td><td>—</td><td>—</td></tr></tbody><thead><tr><th colspan="7">By Environment</th></tr><tr><th></th><th>C1</th><th>C1.1</th><th>C1.2</th><th>—</th><th>—</th><th>—</th></tr></thead><tbody><tr><th>C1.1</th><td>1</td><td>1/2 [18]</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><th>C1.2</th><td>2 [18]</td><td>1</td><td>—</td><td>—</td><td>—</td><td>—</td></tr></tbody><thead><tr><th colspan="7">By Transport</th></tr><tr><th></th><th>C2</th><th>C2.1</th><th>C2.2</th><th>C2.3</th><th>C2.4</th><th>—</th><th>—</th></tr></thead><tbody><tr><th>C2.1</th><td>1</td><td>1 [19]</td><td>1/5 [19]</td><td>2 [20]</td><td>—</td><td>—</td><td>—</td></tr><tr><th>C2.2</th><td>1 [19]</td><td>1</td><td>1/3 [19]</td><td>2</td><td>—</td><td>—</td><td>—</td></tr><tr><th>C2.3</th><td>5 [19]</td><td>3 [19]</td><td>1</td><td>3 [20]</td><td>—</td><td>—</td><td>—</td></tr><tr><th>C2.4</th><td>1/2 [20]</td><td>1/2</td><td>1/3 [20]</td><td>1</td><td>—</td><td>—</td><td>—</td></tr></tbody><thead><tr><th colspan="7">By Social</th></tr><tr><th></th><th>C3</th><th>C3.1</th><th>C3.2</th><th>C3.3</th><th>C3.4</th><th>C3.5</th><th>C3.6</th></tr></thead><tbody><tr><th>C3.1</th><td>1</td><td>1 [4]</td><td>2</td><td>1/3</td><td>1/4 [4]</td><td>1/7 [4]</td><td>1/7 [4]</td></tr><tr><th>C3.2</th><td>1 [4]</td><td>1</td><td>1 [19]</td><td>1/4</td><td>1/3 [4]</td><td>1/5 [19]</td><td>1/5 [19]</td></tr><tr><th>C3.3</th><td>1/2</td><td>1 [19]</td><td>1</td><td>1/2</td><td>1/4 [4]</td><td>1/3 [19]</td><td>1/3 [19]</td></tr><tr><th>C3.4</th><td>3</td><td>4</td><td>2</td><td>1</td><td>1/2</td><td>1/4</td><td>1/4</td></tr><tr><th>C3.5</th><td>4 [4]</td><td>3 [4]</td><td>4 [4]</td><td>2</td><td>1</td><td>1/2 [18]</td><td>1/2 [18]</td></tr><tr><th>C3.6</th><td>7 [4]</td><td>5 [19]</td><td>3 [19]</td><td>4</td><td>2 [18]</td><td>1</td><td>1</td></tr></tbody><thead><tr><th colspan="7">By Solar</th></tr><tr><th></th><th>C4</th><th>C4.1</th><th>C4.2</th><th>—</th><th>—</th><th>—</th><th>—</th></tr></thead><tbody><tr><th>C4.1</th><td>1</td><td>1/2</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><th>C4.2</th><td>2</td><td>1</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td></tr></tbody></table>

within the service area of another charging point. However, the charging capacity of this area should be increased. Although the population density is not as high as other suitable sites, SA3 has a "residential area" score of 1, indicating reasonable proximity to residential areas. Score of important criteria:

<figure><img src="image_3.png" /><figcaption>Fig. 2: Suitable sites for electric (SA) and photovoltaic (PV) charging points</figcaption></figure>

- C3.6 public parking capacity (0.178): 4

- C4.1 electricity grid (0.138): 5

- C3.5 population density (0.108): 2

* SA5 is a location in Rue Pierre et Marie Curie. This site is also far from forested areas and flood risks. It is close to attractions and the electricity grid, but has less public parking capacity than SA3. Although its "existing charging station" score is 4, indicating that it is already within a service area of another small capacity charging points. It has more score in 'population density', which shows a high demand of charging needs. Score of important criteria:

- C4.1 electricity grid (0.138): 5

- C3.5 population density (0.108): 4

- C3.6 public parking capacity (0.178): 1

<table><caption>TABLE IV: Overall weighting for charge points</caption><thead><tr><th>Criteria</th><th>Weighting</th></tr></thead><tbody><tr><td>C1.1</td><td>0.058</td></tr><tr><td>C1.2</td><td>0.116</td></tr><tr><td>C2.1</td><td>0.040</td></tr><tr><td>C2.2</td><td>0.044</td></tr><tr><td>C2.3</td><td>0.126</td></tr><tr><td>C2.4</td><td>0.027</td></tr><tr><td>C3.1</td><td>0.033</td></tr><tr><td>C3.2</td><td>0.030</td></tr><tr><td>C3.3</td><td>0.032</td></tr><tr><td>C3.4</td><td>0.071</td></tr><tr><td>C3.5</td><td>0.108</td></tr><tr><td>C3.6</td><td>0.178</td></tr><tr><td>C4.1</td><td>0.138</td></tr><tr><td colspan="2"><strong>Additional for solar</strong></td></tr><tr><td>C4.1</td><td>0.046</td></tr><tr><td>C4.2</td><td>0.092</td></tr></tbody></table>

<table><caption>TABLE V: Ranking the most suitable electrical sites</caption><thead><tr><th>Site</th><th>S<sub>i</sub>-</th><th>S<sub>i</sub>+</th><th>C<sup>*</sup><sub>i</sub></th><th>Ranking</th></tr></thead><tbody><tr><td>SA3</td><td>0.1179</td><td>0.0567</td><td>0.6754</td><td>1</td></tr><tr><td>SA5</td><td>0.0447</td><td>0.0913</td><td>0.3288</td><td>2</td></tr><tr><td>SA2</td><td>0.0565</td><td>0.1164</td><td>0.3266</td><td>3</td></tr><tr><td>SA10</td><td>0.0525</td><td>0.1198</td><td>0.3048</td><td>4</td></tr><tr><td>SA6</td><td>0.0539</td><td>0.1269</td><td>0.2981</td><td>5</td></tr><tr><td>SA1</td><td>0.0496</td><td>0.1235</td><td>0.2864</td><td>6</td></tr><tr><td>SA4</td><td>0.0496</td><td>0.1235</td><td>0.2864</td><td>7</td></tr><tr><td>SA7</td><td>0.0479</td><td>0.1210</td><td>0.2835</td><td>8</td></tr><tr><td>SA9</td><td>0.0439</td><td>0.1307</td><td>0.2513</td><td>9</td></tr><tr><td>SA8</td><td>0.0378</td><td>0.1252</td><td>0.2318</td><td>10</td></tr></tbody></table>
* SA2 is a location in the rue de Soissons. Like the previous sites, it is also far from forested areas, flood risk and existing charging stations. It is close to the secondary road, bus stop and attractions. However, it has less parking capacity than SA3 and a lower population density than SA5. Score of important criteria:

- C4.1 electricity grid (0.138): 5

- C3.5 population density (0.108): 3

- C3.6 public parking capacity (0.178): 1

Table VI shows that PV8, with a $C_i^*$ value of 0.4880, which has the best performance among the top 10 photovoltaic charge sites. The performance analysis of the each site is presented in detail as follows:

* PV8 is in a commercial zone car park, close to highway. This site is far from forested areas, flood risk and the service areas of other existing charging stations. Although it is a little way from the electricity grids, its global horizontal irradiation is the highest, maximising solar energy production. This location also has plenty of public parking space, which is the best of the 10 suitable sites. Its "population density" score is 2, indicating moderate density. Score of important criteria:

- C3.5 population density (0.108): 2

- C3.6 public parking capacity (0.178): 2

- C4.2 global horizontal irradiation (0.092): 5

* PV3 is a similar location to SA2, situated in the Rue de Soissons. This site is far from forested areas, flood risks and the service areas of other existing charging stations. It has an excellent electricity network, good accessibility and proximity to residential areas and local attractions. Its medium population density ensures stable demand. Although its overall horizontal irradiance score is not the highest, its other scores more than compensate. Score of important criteria:

- C4.1 electricity grid (0.138): 5

- C3.5 population density (0.108): 3

- C3.6 public parking capacity (0.178): 1

- C4.2 global horizontal irradiation (0.092): 4

* PV9 is located in Rue de Pierrefonds in the city centre. This site is far from forested areas, flood risk and the service areas of other existing charging stations. It is on the secondary road, thus it has good access to the main road. Score of important criteria:

- C4.1 electricity grid (0.138): 5

- C3.5 population density (0.108): 2

- C3.6 public parking capacity (0.178): 1

- C4.2 global horizontal irradiation (0.092): 4

### D. Determine the capacity

The capacity of top three suitable sites are estimated using regular traffic data from Google Maps. Due to the lack of accurate data on the number of vehicles passing, traffic periods for roads close to each site is investigated to determine the percentage share of high (red), medium (yellow) and low (green) traffic flow. Accordingly, the timetable is divided into 16 time slots from 6 a.m. to 10 p.m. To refine our analysis, the traffic flow of three different existing charging points is compared to estimate the capacity of proposed sites, as shown in Fig. 3. The analysis are realized as follows in Table VII:

The estimated charging capacity is based on the analysis of existing recharging stations. This method is relevant in the absence of precise data on daily vehicle counts, allowing us to infer charging demand from observed traffic patterns.

<table><caption>TABLE VI: Ranking the most suitable photovoltaic sites</caption><thead><tr><td>Site</td><td>S<sub>i</sub>-</td><td>S<sub>i</sub>+</td><td>C<sub>i</sub><sup>*</sup></td><td>Ranking</td></tr></thead><tbody><tr><td>PV8</td><td>0.0692</td><td>0.0726</td><td>0.4880</td><td>1</td></tr><tr><td>PV3</td><td>0.0632</td><td>0.0676</td><td>0.4832</td><td>2</td></tr><tr><td>PV9</td><td>0.0597</td><td>0.0733</td><td>0.4490</td><td>3</td></tr><tr><td>PV4</td><td>0.0475</td><td>0.0807</td><td>0.3706</td><td>4</td></tr><tr><td>PV2</td><td>0.0493</td><td>0.0843</td><td>0.3687</td><td>5</td></tr><tr><td>PV5</td><td>0.0493</td><td>0.0843</td><td>0.3687</td><td>6</td></tr><tr><td>PV1</td><td>0.0448</td><td>0.0823</td><td>0.3525</td><td>7</td></tr><tr><td>PV6</td><td>0.0499</td><td>0.0958</td><td>0.3425</td><td>8</td></tr><tr><td>PV7</td><td>0.0445</td><td>0.0931</td><td>0.3233</td><td>9</td></tr><tr><td>PV10</td><td>0.0373</td><td>0.0869</td><td>0.3005</td><td>10</td></tr></tbody></table>

<table><caption>TABLE VII: Analysis on the traffic data</caption><thead><tr><td>Site</td><td>Low</td><td>Medium</td><td>High</td><td>Capacity</td></tr></thead><tbody><tr><td>Electra (Fig. 3a)</td><td>24%</td><td>76%</td><td>—</td><td>4<sup>e</sup></td></tr><tr><td>Freshmile (Fig. 3a)</td><td>38%</td><td>37%</td><td>25%</td><td>5<sup>e</sup></td></tr><tr><td>MouvOise (Fig. 3a)</td><td>20%</td><td>72%</td><td>8%</td><td>4<sup>e</sup></td></tr><tr><td>SA3 (Fig. 3b)</td><td>18%</td><td>79%</td><td>3%</td><td>4<sup>P</sup></td></tr><tr><td>SA5 (Fig. 3b)</td><td>33%</td><td>67%</td><td>—</td><td>3<sup>P</sup></td></tr><tr><td>SA2 (Fig. 3b)</td><td>49%</td><td>51%</td><td>—</td><td>2<sup>P</sup></td></tr><tr><td>PV8 (Fig. 3c)</td><td>39%</td><td>53%</td><td>8%</td><td>3<sup>P</sup></td></tr><tr><td>PV3 (Fig. 3c)</td><td>49%</td><td>51%</td><td>—</td><td>2<sup>P</sup></td></tr><tr><td>PV9 (Fig. 3c)</td><td>40%</td><td>59%</td><td>1%</td><td>3<sup>P</sup></td></tr></tbody></table>

<footnote><sup>a</sup>Existing number of charging station.</footnote>

<footnote><sup>b</sup>Proposed number of charging station.</footnote>
<figure><img src="image_4.png" /><figcaption>Fig. 3: Traffic density on existing and determined locations of EV charging stations in Compiègne</figcaption></figure>

## E. *Comparison of different weightings*

It is essential to compare the impacts of the different weightings used: mixed, reference only, and no weighting. For the suitability map only with reference values, the other values were assigned 1. For the suitability map without weighting, all the criteria are considered to have equal importance. Fig. 4 illustrates the corresponding suitable maps for these three types of weighting. Comparing Fig. 4a and Fig. 4b, criteria such as “public parking capacity” and “existing charging station” have a higher weighting in the mixed model than in the reference model. On the other hand, “bus stop”, “attraction”, “place of work and study” and “public building” have low weights in the mixed model. Comparing Fig. 4a and Fig. 4c, the variations in weightings show that each criterion is affected differently when no weighting is applied, highlighting the importance of accurate weighting to reflect real priorities.

Analyses of the three types of weighting reveal significant differences in the identification of suitable sites. The weights are calculated on the basis of assumptions. Consequently, changes in these weightings can alter the identification of suitable sites. The model only with reference values is based on literature specific to different cities. These values are backed up by solid data, but they come from questionnaires completed by different experts. Although these values are reliable, they may not accurately reflect the specific conditions of the case we studied. The mixed weighting, which combines the reference values with our own determined values, seems to provide a more personalized approach to the specific local context. However, to obtain even more accurate data tailored to the local context, it would be necessary to create questionnaires to be completed by experts. On the other hand, the model without weighting provides standardized views, but may lack contextual
relevance for our specific project in Compiègne. These comparisons highlight the importance of selecting the appropriate weighting model according to the specific objectives and priorities of the project.

## IV. DISCUSSION AND FUTURE WORK

The integration of spatial analysis in EV charging site selection has been a subject of increasing interest. This discussion aims to address the key aspects of our methodology as follows:

* **Incorporation of Additional Criteria:** Additional criteria such as “vehicle occupancy time in parking” and “private car park capacity” could indeed refine the spatial analysis and enhance assessment accuracy. These factors can influence the overall suitability of locations by considering temporal parking trends and available infrastructure capacity.

* **Clarification on Traffic-Based Capacity Estimation:** This estimation in our study differs from static demand models used in previous research. We leverage Google Maps traffic flow data (Table VII), categorizing traffic patterns into low/medium/high periods (6 AM – 10 PM). Unlike conventional studies (e.g., Iravani et al. [2]), our dynamic method correlates site rankings with real-time traffic patterns: For instance, SA3’s recommended capacity (4 stations) aligns with its 79% medium traffic flow, whereas SA2’s lower capacity (2 stations) corresponds to its 51% medium traffic level. This approach enhances the practical applicability of site selection models.

* **Justification for Scoring Ranges and Study Limitations:**

*Distance from Forest (C1.1):* Thresholds align with established buffer zones for infrastructure protection.

*Population Density (C3.5):* Ranges (1–843/km²) were calibrated against INSEE fata [10], ensuring clear differentiation between suburban (1–169/km²) and urban (675–843/km²) regions. *Reliance on Secondary Data:* GIS layers, including flood risk and electricity grid information, were sourced from public databases (OSM, INSEE). While these provide comprehensive coverage, they may lack real-time updates.

<figure><img src="image_5.png" /><figcaption>(a) With mixed weighting</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>(b) Only with reference weighting</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>(c) Without weighting</figcaption></figure>

<caption>Fig. 4: Suitability map</caption>

## V. CONCLUSIONS

Addressing the challenges of limited range and inadequate charging station infrastructure is essential to the

widespread adoption of electric vehicles. Indeed, appropriate sites for charging station construction need to be
identified to ensure high quality services and operational efficiency. This research proposes a methodology that uses an efficient GIS-based AHP and TOPSIS method for the siting of electric and photovoltaic charging stations. In this project, we used QGIS software to produce and visualise the results of the spatial analysis in the form of maps and graphs. The study has five main phases: selecting and calculating indicators, spatial analysing using QGIS, weighting indicators according to their relative importance, delimiting and classifying suitable sites, and determining their capacity. The "selection and calculation of indicators" phase consists of four main categories: "environment", "transport", "social" and "technology", as well as 13 criteria with an additional criterion for the photovoltaic charging station. The AHP method was used to weight each criterion according to their relative importance. QGIS was used for the spatial analysis of the data relating to the criteria in order to obtain the suitability maps. Based on the suitability maps, we selected 10 of the most suitable sites for both electricity and photovoltaic. They were ranked on the basis of a performance analysis using the relative proximity of the ideal solution in TOPSIS. Next, we estimated the capacity of suitable sites according to Google Maps traffic flow based on 3 existing charging stations. At the end, comparison of different weightings was studied in 3 models : mixed weighting, only with reference's weighting and no weighting.

As the future work, additional criteria such as “vehicle occupancy time in parking” and “private car park capacity” will be included to improve the assessment results from the spatial analysis. Our next steps conclude with modelling the terminal power, determining the daily electric vehicle, space occupation duration, electricity consumption profile, carrying out an impact analysis on the local electricity network using load flow and carry out a local survey to determine weighting.

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