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Fuel-based vehicles are a significant source of greenhouse gas emissions in America. Electric vehicles (EVs) offer a potential solution to this problem as a cleaner transportation technology. However, to promote the use of EVs, it is crucial to establish a robust infrastructure of charging stations. This research utilized a multi-factor geospatial analysis to address the complex problem of establishing an optimal EV infrastructure. Several factors, such as land use, demographics, and employment centers, were individually analyzed to determine their suitability for EV charging stations. By considering both positive and negative impacts of these factors, we scored them using geospatial analysis to identify the optimal locations for installing charging stations.
*Address all correspondence to: yzhang@deltastate.edu
1. Introduction
Gasoline has been the primary fuel for transportation in the United States over the past 100 years [1]. In 2021, petroleum products accounted for about 90% of the total energy use in the U.S. transportation sector. Long-term use of fossil fuels deteriorated urban air quality, with road transportation being responsible for 70% of pollutants and 40% of greenhouse gas emissions in urban areas [2]. In American, more than half of Americans (166 million) live in counties with unhealthy air quality conditions [3]. Alternative forms of energy such as solar, wind, hydrogen fuel cells, and electricity have been considered as potential energy sources. Electric vehicles (EVs) are powered by electricity which enables them not to introduce harmful pollutants into our atmosphere like gasoline vehicles. EVs convert approximately 59–62% of the electrical energy at the wheels, whereas conventional vehicles only convert about 17–21% of gasoline energy. EVs emit no tailpipe pollutants [4] and have gained support as a strong alternative candidate for future fuel transportation due to the fact of not introducing harmful pollutants to the atmosphere. The U.S. federal government has started some incentive programs to encourage the purchase and use of EVs [5, 6]. Some relevant policies and several incentive programs have been released to ease dependence on gasoline consumption, including purchasing tax credits and installing EV charging stations. These incentives have been adopted by state and local governments. EV charging stations are typically installed by various entities, including governments, companies, and other organizations, to demonstrate their commitment to promoting cleaner transportation options. This support for EV infrastructure is essential to encourage the widespread adoption of electric vehicles and reduce reliance on gasoline-powered transportation.
Although this promising transportation option is available in some places, most of the programs for EV charging stations lack a comprehensive analysis of the locations and infrastructure is not yet in great supply. Many programs install charging stations in urban areas at popular places such as city centers, shopping areas, train stations, and university campuses. More scientific research is needed to better understand where EV charging stations should be located, and provide sound solutions for the establishment of a robust EV charging station infrastructure. In Greater Chicago Area, an agent-based decision support system for electric vehicle charging infrastructure deployment was investigated for the four surrounding counties [7]. The research identified patterns in residential EV ownership and driving activities to facilitate the strategic deployment of new charging infrastructure. An equilibrium-modeling framework was developed to explore interactions between the availability of public charging opportunities, prices of electricity, and destination and route choices of EVs at regional transportation and power transmission networks [8].
The transportation industry has benefited greatly from the use of GIS to help solve complex transportation-related issues and plan the infrastructure of EVs [9, 10, 11]. GIS provides a variety of geospatial analysis tools that allow transportation practitioners to create spatial models that can provide answers to challenging transportation questions. It has been used to identify new transportation corridors, determine the socioeconomic and environmental impacts of future transportation facilities, track the construction progress of transportation projects, and many others. GIS analysis also has been used to identify prime locations for EV charging stations. GIS has been used to analyze grid impact of EVs and origin-destination to model spatial and temporal characteristics of EV charging loads [12]. Grid partition has been used in minimizing the distance to the charging station, zoning the planning area, and selecting the best locations for each partition with the considerations of traffic density and charging station capacity constraints [13]. A GIS multi-criteria analysis method was developed to map optimal locations for EV charging stations in Athens, Greek [14]. The method uses a number of different weighted parameters such as population, points of interest, income, and parking distance to map optimal locations. A GIS site suitability model was also used to locate EV charging stations on public facilities in Los Angeles County, CA [15]. Chen et al. [16] have developed a regional methodology to locate EV charging stations through the use of a regression equation that can predict parking demand in urban areas.
These research methods aim to solve the same complex problem of identifying optimal geographic locations for new EV charging stations. Each uses a defined set of weighted demand factors within a spatial model to determine prime locations and proves to be a success in determining locations for EV charging stations. However, these studies used a limited amount of input demand factors to identify optimal locations. The limited use of input demand factors targets specific facilities to install EV charging station areas. The network provides charging stations to a very selective portion of the driving population. These previous studies have only considered a limited number of demand factors. At the same time, the walking distance between an EV charging station and the desired destination tended to be overlooked. Many studies just used an assumption factor for this variable [16]. Identifying optimal locations for EV charging stations is a complex process that involves various factors. These limited approaches may not accurately capture the full range of variables that influence the adoption and usage of electric vehicles. Therefore, it is crucial to conduct more comprehensive analyses that consider a broader range of demand factors to identify the most suitable locations for EV charging stations.
The purpose of this research was to conduct a comprehensive analysis of the various factors that influence the installation of EV charging stations. To achieve this, we developed a multi-factor geospatial method that evaluates a range of positive and negative impacts on potential locations. By considering these factors, our method aims to identify the most suitable locations for EV charging stations to provide widespread availability for the driving public.
As a new vehicle system, the current number of public EV charging stations is insufficient to meet the demand, in contrast to the vast number of gas stations available. The average range of EVs per charge is much less than that of conventional gasoline vehicles. This lack of infrastructure presents a challenge for the widespread adoption of electric vehicles, and also leads to range anxiety and limited access to charging stations. Range anxiety (driver’s fear that EV has insufficient battery charge to cover the road distance before the destination is reached) prevents this technology from being adopted rapidly by the traveling public. EVs can provide up to 100 miles of distance on a fully charged battery [17]. According to the U.S. Department of Transportation Federal Highway Administration, 100 miles is ample for 90% of trips generated by the traveling public in the United States [18]. Longer trips would require proper planning to ensure the availability of EV charging stations to one’s destination. Range anxiety is a common feeling among EV owners that they may be unable to reach their destination before running out of battery power. This phenomenon is enhanced by driving habits, excessive speed, and weather conditions where battery power is used to heat or cool the cabin of the vehicle [19].
In this research, we used a GIS suitability model to evaluate multiple factors and determine the optimal locations for charging stations. This GIS model intakes input demand factors through a series of spatial analysis procedures to generate an output demand factor grid. To run the model and perform the spatial analysis properly, all the input demand factors need to be collected and prepared or spatially interpolated to the right spatial format. Geospatial analysis of these input demand factors are an important step in modeling. A series of data sets that characterize the physical and urban features of the study area are analyzed first to identify trip attractions by the driving public. Highly desirable and most visited locations are usually identified as employment centers, shopping districts, major transportation hubs, public facilities, and recreational areas. Urban and environmental factors can have negative or positive factors to the driving public. A comprehensive analysis of these location factors is performed and a scoring system is developed to evaluate the contributing impact of each factor on the charging station. To prevent any of the input factors from dominating the scoring system, a uniform weighted system is developed to distribute weight to each input demand factor. This systematic scoring method provided the necessary input parameters that were utilized during the analysis to determine suitable and unsuitable areas for EV charging stations.
Input demand factor is evaluated through the use of a GIS suitability model. The suitability model intakes input demand factor through a series of spatial analysis procedures to generate an output demand factor grid. Figure 1 illustrates each spatial analysis step used in the GIS suitability model.
Figure 1.
Input demand factor model.
The first step is to intersect the input demand factor layer with a walkability grid. A walkability grid indicates how far a commuter would be willing to walk to a particular destination (charging station). A 0.5 × 0.5 mile walkability grid was created in this research as a comfortable walking distance (0.25 mile) for commuters to the charging stations [20].
The second step is to perform a quantile classification to produce a master grid layer. The walkability grid created in step one was then applied to intersect each input demand factor for the study area. The ID of the walkability grid is assigned to associate each individual input demand factor with the walkability grid. A summary statistics is generated using the attribute table of the newly generated input demand factor grid. Based on the summary statistics, a new composite index score is calculated for each grid. This procedure took each individual grid square and tallied up all the input demand factor index scores to produce a composite index score for that particular grid square. For example, if a single grid square contained the following land use types: residential (−4), commercial (+5), and mixed use (+5), the summary statistics would calculate the composite output score to be −4 + 5 + 5 = 6. This composite index score of 6 would then be associated with its grid’s id. Each input demand factor grid is added to the intersect tool in ArcMap to produce a master grid layer. This process allowed for each individual index demand factor layer score generated by the model to be combined into one layer for further analysis. A new attribute field called ‘factor score’ is added to the master grid layer to hold the initial calculated index demand factor score. This calculation is conducted using ArcMap’s field calculator which allowed for an equation to be written that summarizes all attribute fields representing each input demand factor layer’s composite scores.
The resulting summary statistics table is then joined back to the walkability grid to produce a final output demand factor grid. The final output demand factor grid is then exported out to a new feature class and stored in a file geodatabase. This spatial procedure is then repeated again for each individual input demand factor.
The study area of this research project is in the Utah state, United States, where EV technology has gained public’s attention as clean transportation to alleviate the impacts on Utah’s air quality. The Salt Lake and Provo/Orem metropolitan areas are part of a unique mountainous region called the Wasatch Front in the State of Utah. The State of the Air reported serious pollution in both short-term and long-term particle air pollution in this area [21].
Salt Lake and Provo/Orem metropolitan areas are listed on the top of short-term air pollution, ranked 6th and 9th, respectively. Atmospheric inversion is the major factor causing air pollution in the area [22]. During winter time, cold air is caped under warm air and traps air pollutants near the valley floor (Figure 2). The harmful small particles of air pollutants, such as nitrogen oxides, sulfur dioxide, carbon monoxide, and ozone accumulate in the cold air above the safety levels defined by the U.S. Environmental Protection Agency [23]. The Utah Department of Environmental Quality reported that fuel-based vehicles produced 60% of the polluting particle matter [24]. Exposure to poor air quality conditions has substantial adverse effects on human health, especially for people who have respiratory and cardiovascular conditions. A variety of health issues in Utah is found be to associated with poor air quality. Struggling against air pollution is a longtime task for the state. The Intermountain Air Quality and Health Group was established in 2014 to address the escalating evidence. Intermountain Health Care encouraged its employees to utilize public transportation which contributed to declining emissions of 3.5 million pounds [23]. The State of Utah developed several programs to combat poor air quality along the Wasatch Front, such as the statewide TravelWise program that aims to reduce air pollution by providing alternative transportation ideas. These ideas range from alternative work schedules, active transportation, carpooling, public transit, skip the trip, teleworking, trip chaining, and trip planning [25]. This program encourages employers and citizens to participate in activities that will reduce air pollution throughout the Wasatch Front. There are approximately 106 EV charging stations throughout the State of Utah [18]. Only 77 of those EV charging stations are located along the Wasatch Front where 77% of Utah’s population resides [26]. To combat range anxiety and increase EV usage, a well-developed robust EV charging station infrastructure needs to be established.
Figure 2.
Atmospheric inversion in Salt Lake City [22].
This project took the Wasatch Front in Utah as a case study for its significant role of being the economic hub of the state, being the most populated area in the state, and presenting a large portion of work-related trips from nearby counties [26]. 17.2% of the workforce in Utah County and 47% in Davis County work commute from outside of the county [27]. The commute tends to increase with the growth of the population and economy.
A series of data sets, 34 GIS polygon data layers in total, that characterize the physical and urban features of the study area were collected from various government agencies. The focus was to identify GIS datasets that exhibited a high trip attraction by the driving public. Although this research project focused on locations that matter to the driving public, both positive and negative factors were taken into account. Special attention was given to environmentally sensitive areas along the Wasatch Front to prevent these areas from being identified as prime locations in the analysis. The datasets were compiled and preprocessed to GIS data format.
All data layers were given a weighted score based on their positive or negative influence on an EV charging station. The scoring system for each input demand factor was broken down into the following categories: high suitability (score of 5), moderate suitability (score of 3), low suitability (score of 1), and unsuitable (negative score). These weighted spatial input parameters can be taken into a suitability model to determine the final suitable spatial outcome.
The factor score in the master grid was then multiplied by the physical feature index score (1 for valley floor and − 1 for mountains and waterbodies) which resulted in a series of positive and negative final index demand scores. The resulting negative scores indicated that these grid squares fell among mountainous and waterbody areas that would be unsuitable for an EV charging station. Grid squares that contained positive scores were indicators of suitable valley floor locations for EV charging stations. These positive final index demand scores were further evaluated to determine a proper classification that would assist in identifying prime locations for EV charging stations. An unsupervised classification within ArcMap was initially utilized to first visualize the spatial pattern of the final index demand factor scores. A series of supervised classifications were then applied to the final index demand factor scores and viewed on the top of an aerial photograph of the study area. Visual checks were applied to areas known to have a high attraction for EV charging stations. The resulting scoring patterns in these areas were used to establish a uniform supervised classification system that was used to display high, moderate, and low suitability areas for EV charging stations throughout the study area.
Table 1 details the scoring for each input demand factor. The input demand factors are classified into four major categories: environmental factors, demographics, infrastructure, and land use. The environmental factors describe the physical features of the landscape in the study area. Demographic analysis was given to have a perspective on the population, employment status, and trip destinations. Infrastructure entails the locations that the driving public may visit. The land surface is classified based on its purpose of usage.
Category
Factor
Score
Score definition
Environmental
Physical features
Mountains
−1
Unsuitable
Water bodies
−1
Unsuitable
Valley floor
1
Suitable
Major rivers
−1
Unsuitable
Wetlands
−5
Unsuitable
Demographics
Population (added factor)
High
2
High suitability
Low
1
Low suitability
Employment
High
5
High suitability
Medium
3
Moderate suitability
Low
1
Low suitability
Trip destinations
High
5
High suitability
Medium
3
Moderate suitability
Low
1
Low suitability
Infrastructure
Airports
5
High suitability
Existing EV charging stations
−5
Unsuitable
Gas stations
5
High suitability
Golf courses
5
High suitability
Government fuel sites
3
Moderate suitability
Government offices
5
High suitability
Health care facilities
5
High suitability
Libraries
5
High suitability
Major attractions
5
High suitability
Major intersections
5
High suitability
Major roads
5
High suitability
Major parking garages
5
High suitability
Park & ride lots
5
High suitability
Parks
5
High suitability
Places of worship
5
High suitability
Post offices
5
High suitability
Schools
5
High suitability
Senior centers
5
High suitability
Shopping malls
5
High suitability
Ski resorts
5
High suitability
Land use
Agriculture
−5
Unsuitable
Open space
−5
Unsuitable
Commercial
5
High suitability
Mixed use
5
High suitability
Industrial
5
High suitability
Government/institution
5
High suitability
Residential
−4
Unsuitable
Sensitive areas
−5
Unsuitable
Table 1.
Input demand factor scoring.
The study area has several landscape types including mountains, lakes, rivers, and wetlands. To delineate the landscape types for the scoring process, a digital elevation model was first applied to separate the landscape into two categories: mountains and valley floor. The subsequent layer was converted to a polygon and intersected with a major lake feature class to get polygons of mountain and lake areas. A score of −1 (unsuitable) was assigned to mountain and lake areas, a score of 1 (suitable) was assigned to valley floors, and a score of −1 (unsuitable) was assigned to a separate polygon layer that contains major rivers. A score of −5 (unsuitable) was given to protected wetlands in the study area, which protects these locations from being used for potential EV charging stations.
A score of 5 (high suitability), meaning a strong attraction to the driving public, was assigned to the majority of the input demand factors for infrastructure. Facilities that are often visited by the public belong to this category and are assigned a score of 5. These infrastructures are identified as gas stations, airports, health care facilities, libraries, golf courses, government buildings, postal offices, schools, shopping malls, places of worship, senior centers, popular parking garages, park and ride lots, major attractions such as parks and ski resorts, as well as major road intersections and roads. A score of 3 (moderate suitability) was given to government fuel sites since these locations might be unavailable to the traveling public. Adding these sites to the analysis would encourage government agencies that utilize fleet vehicles to invest in EV technology. Existing EV charging stations were the only negative infrastructure input factor that was assigned a −5 (unsuitable) score. The reason for this negative input was to reduce the chance of highlighting areas that already had an EV charging station established.
Analysis of the distribution of demographics assisted with the perspective of socioeconomics in the area. The current and future population trends, employment and employers, and trip destinations were all evaluated for the study area to assign the scoring number for the input demand factors. A quantile classification was run in ArcMap to score the 2040 employment estimates contained in the Traffic Analysis Zones (TAZ). Areas of high employment (score of 5), medium employment (score of 3), and low employment (score of 1) were labeled respectively based on the classification. The 2040 TAZ population estimates were analyzed using a different approach. Most EV owners will generally have a home EV charging station installed in their homes for convenience. This factor needed to be taken into consideration since the targeted locations for this research project focused on trips away from home. To avoid highlighting residential areas, the population was analyzed on a regional scale and used as an added factor to the overall analysis. This allowed for high to moderately populated areas to receive additional points to better identify potential locations of high EV charging usage. Areas of high to moderate population were given a score of 2, while areas of the low population received a score of 1. The Utah Department of Workforce Services identifies Utah’s largest employers on an annual basis [28]. Out of the top 10 employers in Utah, 9 of them reside along the Wasatch Front as seen in Table 2. These employers generate a high number of work-related trips and therefore were given a score of 5 (high suitability).
Company
Industry
Employment
Location
Intermountain Health Care
Health Care
20,000+
Wasatch Front
State of Utah
State Government
20,000+
Wasatch Front
University of Utah
Higher Education
20,000+
Wasatch Front
Brigham Young University
Higher Education
15,000–19,999
Wasatch Front
Walmart Associates
Warehouse
15,000–19,999
Wasatch Front
Hill Air Force Base
Federal Government
10,000–14,999
Wasatch Front
Davis County School District
Public Education
7000–9999
Wasatch Front
Granite School District
Public Education
7000–9999
Wasatch Front
Utah State University
Higher Education
7000–9999
Cache County
Smith’s Food & Drug Centers
Grocery Stores
7000–9999
Wasatch Front
Table 2.
Largest employers in the state of Utah.
The State of Utah conducted a Household Travel Survey in 2012 to better understand the travel patterns of the driving public. The trip destination portion of this study was utilized in this research project to identify areas where the traveling public visit the most frequently. A quantile classification was performed to identify areas of trip frequency. The above-average trips are scored 5, a moderate amount of trips are scored 3, and low trips are scored 1.
Land use plays a direct role in determining the types of destinations that are taken by the driving public, therefore the attractiveness of the land use type determines the scores for this input factor. Plans for general land use of each county was referenced to project current and future land uses. Land use types that offer services and employment usually attract a high number of destination trips, such as lands used for commercial, industrial, government, institutional purposes, or mixed use of these functions were scored 5 with high suitability for charging stations. On the other hand, environmental land use types such as agriculture, open space, and sensitive areas are unsuitable for installing charging stations and were scored −5. Charging stations for residential use are generally installed in private residences for owners of vehicles. Residential areas were unsuitable and therefore scored −4. This scoring process eliminates the possibility of residential areas being highlighted but also prevented adjacent optimal land use types from being overshadowed.
Each input demand factor was produced as a separate grid to illustrate destination attractions and spatial distribution of the demand factor within the study area. Each of the resulting input demand factor grids was composed of 16,790 individual 0.5 × 0.5-mile grid squares. Figure 3 illustrates all the 28 input demand factor grids. In these maps created, input factors with positive score are shown in blue whereas negative scores are colored orange. There were a total of 23 out of 28 input demand factor grids that received a positive score. As shown in Figure 3, these positive input demand factor grids are characterized as major transportation hubs, employment centers, shopping districts, public facilities, health care centers, recreational areas, future populations, and trip destinations. The resulting positive input demand factor grids played an important role in determining the final output of this suitability analysis. Only 3 input demand factor grids received all negative scores which included existing EV charging stations, rivers, and wetlands. These particular negative input demand factor grids helped to deter these locations from being identified in the final analysis. Also, 2 of the input demand factor grids contained both positive and negative scores. These specific input demand factor grids represented land use and physical features. Additionally, these input demand factor grids assisted in identifying land area types that were suitable and unsuitable for locating EV charging stations within the study area. The scoring system contained in these input demand factor grids became the vital stepping stone that allowed for the final results to be generated. Converting the initial input datasets into these uniform input demand factor grids allowed for a final composite score to be spatially calculated. The final composite scores were used to display the final results as a hot spot map.
Figure 3.
Maps of the demand factor grids.
A comparison of each input demand factor grid revealed a unique spatial distribution pattern. This unique pattern outlined where these input demand factors were concentrated along the Wasatch Front area. The majority of these input demand factors were located in urbanized centers or adjacent to major transportation facilities. This spatial pattern became very apparent when these input demand factor grids were compared with Utah’s major transportation facilities. I − 15 is the principal interstate that runs north and south through the entire State of Utah. Many of the input demand factors throughout the Wasatch Front were generally concentrated near this major transportation facility. This spatial concentration allows for greater access to these particular input demand factors. Prime examples that illustrate this spatial phenomenon can be seen in the following input demand factor grids: government offices, gas stations, libraries, major attractions, major employers, park & ride lots, post offices, and senior centers. Salt Lake County differs a bit from other counties along the Wasatch Front when it comes to core concentrations of input demand factors along I − 15. This distribution difference is due to a higher population density and the effects of urban sprawl that has led to a greater spatial expansion of input demand factors in this area. Salt Lake County is home to more than one interstate; I-15, I-80, and I-215, which provides greater accessibility than any other surrounding counties in the study area. This wider spatial distribution of input factors within Salt Lake County is also apparent in many of the resulting input demand factor grids. Higher concentrations of input demand factors will allow for a greater probability of locating a greater number of EV charging stations within Salt Lake County. Other input demand factors that did not necessarily follow the core concentration concept were factors that provided services to smaller geographic areas such as local parks, places of worship, health care facilities, schools, and ski resorts. These input demand factors had a broader geographical stretch compared to other input demand factor grids. This allows for areas outside the core concentration area to be considered and assists in developing a more expansive EV charging station system. These individual input demand factor grids help portray each input factor’s influence in the overall analysis.
A hot spot map was produced to demonstrate locations with high, moderate, and low potentials for installing an EV charging station for the study area (Figure 4). High suitable areas were highlighted in red on the map. The areas have an index demand score of 50 or greater, and contain the highest mixture of input demand factors. These are driving public frequently visited areas and are ideal for installing EV charging stations. Downtown business districts, shopping districts, employment hubs, public facilities, health care centers, park and ride lots, parking garages, and major roads and intersections, attractions, recreational areas etc., are all hot spots for EV charging stations. The yellow areas represented moderate suitable areas. This class displayed an uneven mixture of input demand factors. Moderate demand areas are secondary choices for charging station when prime areas cannot be located in surrounding areas. Examples are small business parks near larger residential neighborhoods, and areas that are seeing population and employment growth. Low suitability areas were colored green, where showed a low mixture of influential factors and surrounded by low suitability factors such as residential neighborhoods, open space, and agricultural areas.
Figure 4.
Optimal locations to install EV charging stations.
The multi-factor modeling system developed in this research classifies the hierarchical importance of each input demand factor, and identifies suitability of locations for installing EV charging stations. The ranking for each input demand factor was tested against the total number of input grids. The testing showed that the majority of the input demand factors had an average influential percentage of 80%, as listed in Table 3, indicating the model’s ability to produce reliable results [29].
This research was to develop a multi-factor methodology for identifying optimal locations for installing EV charging stations. A comprehensive geospatial analysis was conducted on multiple input demand factors to develop a scoring system and locate areas that are most frequently used by the driving public. This scoring system was able to calculate a composite score for each location and spatially classify influential factors to identify suitable locations for EV charging stations. The locations with high, moderate, and low suitability for installing EV charging stations was identified and spatially delineated. The hot spot map of suitable locations can assist decision-makers with developing a strong EV charging station infrastructure. Geospatial analysis plays a vital role in the comprehensive evaluation and determination of the scoring for each input demand factor and thereby identifying optimal locations for installing charging stations.
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Written By
Yongqin Zhang and Kory Iman
Submitted: 01 September 2022Reviewed: 13 March 2023Published: 01 May 2023
Open access peer-reviewed chapter
