Conceptualization, Methodology, Formal analysis, Writing – Original, Review &
Editing, Visualization Writing – Review & Editing, Data Curation [cor1]E-mail:
<EMAIL_ADDRESS>Conceptualization, Formal analysis, Methodology, Writing -
Review & Editing, Supervision

a]Department of Civil, Construction and Environmental Engineering, The
University of Alabama, Tuscaloosa, 35487, AL, U.S.

b]School of Traffic and Transportation Engineering, Tongji University,
Shanghai, China

# Understanding the Opportunity-Centric Accessibility for Public Charging
Infrastructure

Hossein Gazmeh  Yuntao Guo  Ph.D  Xinwu Qian  Ph.D  [  [

###### Abstract

In this study, we show that distance-based accessibility measures of civil
infrastructure may fail to aptly address the distinct challenges associated
with accessing Public Charging Stations (PCSs), and we highlight the
importance of incorporating the availability of opportunities near PCSs.
Specifically, we distinguish between two distinct scenarios: one where
charging access is secondary to opportunity access and the other where
opportunity access is secondary to charging access. We conduct a comprehensive
comparison between distance-based accessibility and the new opportunity-
centric measures and further perform counterfactual analyses under varying PCS
deployment strategies, utilizing data from over 28,000 PCSs and more than 5.5
million points of interest across twenty major metropolitan areas in the U.S.
Our analysis reveals substantial inequalities in PCS accessibility across all
metropolitan areas, where the PCS accessibility in the top 1% of census block
groups (CBGs) can be 3.6 times better than in the bottom 50%. We also
statistically confirm that an increase in PCSs will further amplify these
inequalities. In terms of opportunity-based accessibility, we show access
disparities that are universal across all metropolitan areas. Importantly,
inequalities stemming from the conventional accessibility definition are
further exacerbated when considering opportunity availability near PCSs. Our
results suggest that access inequality is linked to the income levels of CBGs,
with improved opportunity-centric accessibility significantly associated with
higher income in over 60% of the metros. Despite existing disparity issues,
our counterfactual analyses of different PCS deployment strategies indicate
that the equity-centric deployment strategy based on distance measures turns
out to be the least equitable when opportunities are considered. Instead, the
current PCS deployment is found to achieve a higher POI attachment and
Charging-induced Activity Accessibility score across all 20 metros over
alternative strategies, with a median increase in scores by at least 63%.
Nevertheless, the opportunity-centric performance of PCS deployments remains
to be largely affected by the pre-existing unequal distribution of
opportunities in our cities. Our results underscore the complexity of locating
charging stations and provide important implications that may guide the design
of nationwide charging networks for long-term societal benefits.

###### keywords:

public charging station accessibility opportunity-based accessibility equity
electric vehicles

##  1 Introduction

Reflecting a significant shift towards electrified transportation, global
Electric Vehicle (EV) sales have surged tenfold, from 1 million in 2017 to 10
million in 2022 [25]. Accompanying the growth of EVs, the rapid development of
public charging stations (PCSs) plays an essential role in supporting this
transition. In the U.S., motivated by the pursuit of achieving net zero
emissions by 2050, states across the nation, in conjunction with the federal
government, are rapidly advancing toward the implementation of EV charging
stations to reach the target of 500,000 EV chargers by 2030 [51, 52]. Beyond
its obvious necessity for those without home charging capabilities, the public
charging infrastructure is equally vital for those with home charging,
providing essential top-ups and offering a safety net for longer journeys.
Recent studies suggest that by 2030, approximately 53% of EV owners will have
access to home charging [44], leaving a significant portion reliant solely on
public infrastructure, and PCSs are projected to serve at least 30% of all
charging needs. Consequently, developing an extensive, accessible, and
efficient public charging network is integral to sustaining and further
accelerating the global EV boom. Despite the strong momentum in EV penetration
and PCS expansion, one essential research question remains open: How do we
evaluate the effectiveness and efficiency of our charging station deployment?

Providing an answer to the above question is critical, as it will guide policy
design that can lead to long-term community benefits. Among various metrics
that could describe the multifaceted performances of infrastructure,
accessibility remains a central factor. It quantifies how effectively an
infrastructure plan serves the community, based on its definition “the ease
and convenience of access to spatially distributed opportunities with a choice
of travel” [15]. Initial methods of measurement, such as distance or time to
the nearest service, population-to-provider ratios, and cumulative opportunity
measures, offer ease of understanding and interpretation. However, they tend
to oversimplify the relationship between supply and demand regarding
accessibility and fail to account for the effects of competition for the
available opportunities [39, 60, 35]. To counter these shortcomings, the class
of gravity models was developed, taking into account factors like proximity,
availability, and competition. While these models marked an improvement, they
have attracted criticism for their complexity, interpretive challenges, and an
over-reliance on the selection or empirical determination of the distance-
decay function [40, 62]. Besides these known shortcomings, we further argue
that the current definition of accessibility is too generic and lacks specific
adaption to fully capture the distinct attributes of charging events. To
understand this, we must acknowledge the fact that, unlikely conventional
travel activities, charging does not necessarily constitute a primary travel
decision and, in many cases, are secondary to daily activities. To this end,
we claim that the evaluation of accessibility should not only cover the
physical hurdle in reaching a particular station but also embed the
opportunity of engaging in activities around the charging locations.

This paper initiates an exploration into extending the definition of
accessibility to PCS. It introduces new dimensions that capture the unique
characteristics of charging events and provides a quantitative and qualitative
assessment of the performance of the existing charging infrastructure in ten
major U.S. metropolitan areas. We will explore how traditional accessibility
measures present PCS performance. Moreover, our analysis will go further by
connecting accessibility with increased opportunity access. We will deeply
investigate two novel measures: one focusing on the convenience of charging a
vehicle while simultaneously engaging in other activities - the primary
emphasis here being on the activities themselves with a proximal PCS serving
as a secondary benefit; the other scenario underscores the direct access to
PCSs, with the supplemental advantage of conducting other activities during
the charging process. Our analyses leverage a dataset of over 5.5 million
points of interest linked to more than 28,000 PCSs across twenty metropolitan
areas in the U.S., aiming to address the following primary research questions:

  1. 1.

How accurate do traditional distance-based accessibility metrics characterize
the performances of the public charging infrastructure?

  2. 2.

Which additional perspectives related to charging behavior should be
considered to guide the evaluation and deployment of charging facilities, and
in what ways do traditional methods fail to adequately address these
considerations?

  3. 3.

How can we develop new accessibility metrics inspired by charging behavior for
a quantitative evaluation of charging facility deployment performances, and
what additional insights do these metrics offer that extend the conventional
understanding of accessibility?

  4. 4.

How can we employ counterfactual analyses for a quantitative evaluation of the
U.S. deployment of charging stations, leveraging both the existing and newly
proposed metrics?

By examining these research questions, our study aims to highlight the
complexity in guiding and evaluating the deployment of charging stations,
provide valuable insights into the factors shaping the EV charging landscape,
guide the development of effective policies, and facilitate the future growth
of charging infrastructure in diverse urban contexts. The rest of this study
is organized as follows. First, in Section 2, we provide an overview of public
charging as a chance to participate in nearby activities and review the
strategies to deploy and examine the charging infrastructure accessibility.
Section 3 presents the main data collection and processing steps. Next,
Section 4 outlines gravity and opportunity-centric metrics for accessibilty to
charging stations. We then provide the results of our comprehensive analysis
in Section 5. Finally, Section 6 provides a discussion by summarizing our key
findings, conclusions, and future direction.

##  2 Background

#### Deployment of Public Charging Stations

EV charging infrastructure plays a crucial role in the U.S.’s ambitious
efforts to combat climate change, mainly by removing a main long-term
challenge and creating a positive loop that enforces EV adoption [31].
Currently, home location chargers are the preferred charging venue, where more
than 50% of charging sessions occur [23]. Followed by work location charging,
public chargers remain indispensable for EV users who lack access to home
charging or face limitations in this regard [23]. This is particularly the
case for residents in densely populated urban areas, where off-street parking
and private garages are less accessible and the installation cost of chargers
in multi-unit dwellings (MUDs) is prohibitively high [17, 63].

At the same time, EV sales in the US experienced an increase of 55% relative
to 2021 due to a plethora of factors such as purchase subsidies and an
expanded range of available models [25]. However, alongside this surge in EV
demand, developing a public charging infrastructure still requires addressing
a multi-dimensional challenge to ensure that the infrastructure can adequately
meet the escalating user needs. Recent studies have mainly explored the
charging station location problem, taking into account traffic flow dynamics
and drive range [12, 7, 45], charging cost [21], waiting times [58], electric
power distribution loads [48], or a combination of such factors [38].
Furthermore, the development of public charging infrastructure must prioritize
the equitable distribution of costs and benefits throughout society. Achieving
this goal requires acknowledging existing equity gaps and the limitations in
our current methods of evaluating the performance of the existing charging
layout. This understanding is imperative for addressing disparities and
ensuring that the infrastructure serves the needs of all segments of society
effectively.

#### Public Charging Stations as an Opportunity for Activities

The significant uptake of EV sales has resulted in a growing body of research
focusing on EV drivers’ charging preferences. Studies have found a plethora of
factors including charging duration [9, 2, 18], the need for detours [37, 22],
and range anxiety [27, 3] play a vital role in the selection of charging
stations. Given that charging can take several hours depending on the
vehicle’s battery size and charging power—for example, charging a Tesla Model
3’s battery from 40% to 50% on a Level 2 charger requires 30 minutes, and
reaching to 90% takes an additional 90 minutes [8]—drivers often look for ways
to occupy this time. This prompts users to engage in auxiliary activities,
particularly when options are proximate in a walking distance. Lei et al.
found that instead of visiting the nearest charging station, 40% of drivers
are willing to drive an extra 1.5 kilometers to reach a station that better
suits their additional needs, like the presence of amenities including rest
areas and dining options [32]. Simultaneously, the growing link between
businesses and charging stations is the other factor enforcing the attachment
between the charging behavior and the activities. Recent studies highlight
that over 50% of public charging stations are strategically positioned near
commercial venues, where the pricing often deviates from the optimal pricing
strategies [5], followed by a captured trend where nearby businesses
experience a 4% increase in monthly visits after the installation of Tesla
Superchargers in U.S. [6].

#### Equity Implications of Existing Public Charging Infrastructure

Recent studies have identified a strong correlation between the placement of
public EV charger installations and the ownership of electric vehicles [24,
59]. However, while rational for the early stages of charging infrastructure
development, such an approach can also lead to or amplify the existing
socioeconomic inequities among population groups. Consequently, as EVs become
more affordable and offer longer driving ranges, the layout of public charging
infrastructure can have significant and lasting implications for determining
which segments of the population can benefit from the advantages of EV
adoption [10]. In this regard, several studies have investigated the
socioeconomic equity concerns surrounding the distribution of charging
stations within communities [24, 41, 28, 47, 34, 36]. For instance, a study
conducted in California examined the presence of charging stations in
different census block groups and revealed disparities in public charging
access, particularly in lower-income block groups [24]. The research also
highlighted that areas with a higher concentration of multi-unit housing,
where residential charger access is less common, experienced more pronounced
disparities in public charger availability. Current data also reveals the
local benefits associated with offering more charging opportunities to be
unequally distributed. For instance, using 14 million housing transactions for
almost three decades showed that charging infrastructure can be capitalized
into property values where the average price premium for houses with PCSs
within 0.5 km compared with houses without proximate PCSs [36]. Others have
highlighted the potential ramifications of inadequate charging infrastructure
support among communities, leading to missed environmental and health benefits
at both the household and community levels. These include reduced local air
pollution, cost savings compared to traditional internal combustion engine
vehicles, and advantages of leveraging regional and local power grid ancillary
services, including localized voltage support [43, 42, 29].

#### Performance Measures in Deployment of Public Charging Stations

Despite the growing evidence of disparities in charging station deployment,
the current approach to equity evaluation for charging infrastructure often
mirrors the spatial analysis and conventional accessibility metrics used for
other civil infrastructures [34, 20, 24, 11]. For instance, users’
accessibility to charging infrastructure is measured by counting the available
charging opportunities within a 15-minute driving range [11]. Other methods
for equity assessment of charging layout include consideration of distance
from the charging demand point to the stations [61], travel demands and
traffic flow volume passing through each region [14], determining a threshold
for the distance (or travel time) between the users and the charging stations
[26], and incorporating binary variable representing the presence of at least
one EV charging station in a particular zip code and The total number of EV
charging stations in each zip code before performing a correlation analysis
with socioeconomic factors [28]. Nevertheless, while they might still be
appropriate in other types of civil services like urban public facilities,
such approaches fall short of considering the intricate urban dynamics
involved in users’ charging station selection. This complexity arises in part
from the need to consider the spatial arrangement of various locations and
their interaction with the unique characteristics of neighborhoods and road
networks within the local area and in part due to the users’ charging
behaviors that are closely entwined with their day-to-day activities [53, 31].
Furthermore, examining the deployment of PCSs requires special attention to
the interplay of factors such as installation incentives and policies, user
behaviors, and the underlying business strategies driving the deployment
process, which can vary greatly in different regions.

##  3 Data

###  3.1 Data Collection

In this study, multi-source data capturing geolocation of PCSs,
sociodemographic information at the census block group level (CBG), and the
spatial distribution of major points of interest (POIs) are collected and
analyzed to gain insights into opportunity-centric PCS accessibility within 20
U.S. major metropolitan areas.

AFDC Public Charging Station Locator: The identification of PCSs is based on
the DOE’s Alternative Fuels Data Center (AFDC) as the most comprehensive
charging station locator in the U.S. [57]. The dataset includes over 65,000
public charging stations and more than 170,000 Electric Vehicle Supply
Equipment (EVSE) across the U.S. as of January 2024.

American Community Survey data: Sociodemographic data for the communities are
sourced from the 2016-2020 American Community Survey (ACS) 5-year estimates,
providing detailed information on population and income status at the census
block group (CBG) level [55]. The dataset includes over 242,000 CBG across the
U.S., with a total population exceeding 329 million.

SafeGraph Points-of-Interest data: To gather information on the location and
activity categories of various places, we leverage the SafeGraph Global Points
of Interest (POIs) data [50]. The dataset comprises information on more than
18.5 million unique places spanning across the United States as of January
2024. Specifically, along with the geometric coordinates, the dataset includes
brand affiliation and two levels of category tags for each place.
Additionally, each place is classified according to the North American
Industry Classification System (NAICS) codes, encompassing 607 distinct NAICS
codes and 253 major category tags such as ‘Restaurants and Other Eating
Places’ and ‘Offices of Physicians’. Listed POIs are evaluated to be no more
than 10 meters away from their location on Google Maps, where accuracy for top
brands’ attributes is reported as high as 99.9% compared to brand store
locators [49].

###  3.2 Data Processing

#### Selection of Metro Areas

We narrow our analysis of the U.S. charging infrastructure by focusing on
major metropolitan areas rather than state or regional levels. This decision
is based on two reasons. Firstly, a noticeably higher rate of EV adoption and
deployment of EV infrastructure is evident in larger metropolitan areas
compared to smaller urban or rural regions. This distinction allows for a more
meaningful analysis of accessibility and the interaction between activities
and charging facilities located in close proximity to each other. Second, even
in CBG resolution, rural areas have larger geographic units with considerably
less road network coverage. This can impact the accuracy of our accessibility
analysis, as a charging station located on one side of a block group may not
be accessible to the other side.

Next, to determine a representative sample of metro regions across the U.S.,
we examined the distribution of PCSs in metropolitan areas, as depicted in
Figure 1. Figure 1 reveals that the top 20 metropolitan areas, in terms of PCS
count, collectively represent 49.3% of the total PCSs located in metro areas-
that is, 28,762 out of 58,321 PCSs. Therefore, our further analysis focuses on
these twenty major metro areas in the U.S., boasting an extensive charging
infrastructure network that is distributed across different regions. Figure 2
displays the selected metro areas. Among the selected metros (and nationally),
the Los Angeles metro area has the largest charging infrastructure, offering
4,232 public charging stations and 13,957 EVSEs. In contrast, the Albany metro
area has the smallest infrastructure within the selected metros, with 611 PCSs
and 1,676 EVSEs.

Figure 1: Number of Public Charging Stations in U.S. Metro Areas Figure 2:
Selected U.S. Metro Areas

#### Selection of Activities

To analyze the opportunity-centric accessibility to charging infrastructure,
we curated the SafeGraph POIs dataset by applying multiple essential filters
to ensure relevance to charging station activity. Initially, we excluded
specific business categories not directly related to charging. This led to the
elimination of seven high-level categories as defined by the North American
Industry Classification System (NAICS) [54], including ‘Administrative and
Support and Waste Management and Remediation Services,’ ‘Agriculture,
Forestry, Fishing and Hunting,’ ‘Mining, Quarrying, and Oil and Gas
Extraction,’ ‘Management of Companies and Enterprises,’ ‘Utilities,’
‘Construction,’ and ‘Manufacturing.’ These categories collectively represented
7.5% of the total POIs. Further refinement was achieved by examining the
median dwell time at various establishments using Advan’s spend patterns
dataset at individual POIs [1]. We excluded any location where the median
dwell time classified by their NAICS code was over 5 hours or less than 10
minutes. This criterion led to the removal of places under category tags such
as ‘Financial Transactions Processing, Reserve, and Clearinghouse Activities’
and ‘Petroleum Bulk Stations and Terminals’. Moreover, we identified and
excluded POIs that, despite a high count, did not facilitate engagement with
charging activities. This includes ATMs and specific in-store services like
Redbox vending machines within larger retail outlets, as well as POIs such as
USPS Collection Points, UPS Drop Boxes, Allpoint ATMs, and Redbox locations.
Finally, we filtered out locations designated as alternative fuel stations,
including sub-category of ‘Other Gasoline Stations’ and specific brands such
as ‘ChargePoint Network Charging Station’, ‘EVgo’, ‘Tesla Destination
Charger’, and ‘Blink Charging Co.’, to avoid redundancy with PCS data.

After this comprehensive filtering process, we retained 16,544,048 POIs, which
constitute 86.3% of our original dataset. The selected POIs were then
classified into nine groups based on their NAICS high-level business
categories, as follows:

  * •

Accommodation and Food Services (e.g., Restaurants & Other Eating Places,
Drinking Places - brands like Subway, and Starbucks)

  * •

Retail and Wholesale Trade (e.g., Supermarkets, Convenience Stores, Sporting
Goods Stores - brands such as CVS, and 7-Eleven)

  * •

Health Care and Social Assistance (e.g., Offices of Physicians, Offices of
Dentists, Child Day Care Services - brands such as DaVita, and Aurora
Healthcare)

  * •

Arts, Entertainment, and Recreation (e.g., Fitness & Recreational Sports
Centers, Golf Courses & Country Clubs, Casinos - brands like CrossFit, Club
Pilates)

  * •

Professional, Scientific, and Technical and Educational Services (e.g.,
Offices of Lawyers, Elementary & Secondary Schools - organizations like H&R
Block, and Mathnasium)

  * •

Public Administration and Other Services (e.g., Religious Organizations,
Public Offices, Drycleaning & Laundry Services - brands like Rotary Club)

  * •

Real Estate and Rental and Leasing (e.g., Lessors of Real Estate, Malls -
brands like Enterprise Rent-A-Car)

  * •

Finance, Insurance, and Information (e.g., Insurance Carriers, Wireless
Telecommunications Carriers, Libraries & Archives - brands like Edward Jones,
T-Mobile)

  * •

Transportation and Warehousing (e.g., Used Household & Office Goods Moving,
General Warehousing & Storages - brands like ArcBest, iStorage)

Finally, the datasets were mapped onto the census block group (CBG) level
within the major metro areas by using geometric boundaries from the 2020 TIGER
urban shapefiles [56]. We note that our definition of metro areas aligns with
the U.S. Census delineations of a Metropolitan Statistical Area (MSA) [46].
For instance, the New York, NY metro area corresponds to New York–Jersey
City–Newark, NY–NJ, and the Los Angeles, CA metro area corresponds to Los
Angeles–Long Beach–Anaheim, CA.

##  4 Method

We describe several metrics to evaluate the effectiveness of public charging
station layouts, incorporating traditional accessibility aspects along with
opportunity-centric measures for CBGs. We then proceed to introduce our
counterfactual analysis and the alternative charging infrastructure deployment
scenarios.

###  4.1 Metrics

####  4.1.1 Accessibility Measures

Our first metric on the CBGs’ accessibility to the public charging stations is
based on the gravity model of measuring accessibility, a widely utilized
approach in the urban accessibility literature [19, 33]. In our case, gravity
models enable us to quantify the degree of CBGs’ accessibility to public
charging stations within their catchment area (defined as the nearest 20
stations from their centroid). For brevity, we will use CBG accessibility or
gravity access to refer to the gravity model-based accessibility measure, with
the mathematical expression below:

$\displaystyle A_{i}$ $\displaystyle=\sum_{j\in S}\mathbb{I}_{ij}\times
N_{j}\times e^{-\beta t_{ij}},\quad\forall\,i\in Z$ (1)

where $A_{i}$ represents the accessibility score of a CBG $i$,
$\mathbb{I}_{ij}$ is an indicator variable that takes the value of 1 if
station $j\in S$ is within the catchment area (nearest 20 stations) of CBG
$i$, and 0 otherwise. $N_{j}$ denotes the number of EVSEs available at
charging station $j$, and $t_{ij}$ indicates the distance (km) between the
centroid of CBG $i$ and charging station $j$. The impedance factor $\beta$
influences the rate at which the accessibility score decreases with increasing
distance and is set to $\beta=0.08$ [33].

####  4.1.2 Opportunity Score and Opportunity-centric Accessibility Measures

The gravity-based accessibility measures characterize spatial impedance to and
from a charging station but fall short of considering EV users’ access to
additional opportunities while charging their vehicles. In many cases, users
are willing to sacrifice travel time in pursuit of engaging in additional
activities around the charging station. This asserts the need to examine the
number of points of interest (POIs) nearby a charging station as a proxy for
additional activity opportunities.

Thus, we begin by first introducing the POI opportunity score of a station $j$
for POI category $c$, $P_{j}^{c}$, written as:

$P_{j}^{c}=\sum_{k\in K^{c}}\frac{\mathbb{I}_{kj}}{|K^{c}|}$ (2)

where $K^{c}$ is the complete set of POIs in category $c$, $\mathbb{I}_{kj}$
is an indicator variable and takes the value of 1 if a POI $k$ is associated
with (within 250 meters from) the charging station $j$ and 0 otherwise. We
note that this measure is scale-invariant by diving the size of the set
$K^{c}$, so that the measure is comparable across different metro areas
despite the differences in the total number of POIs. $P_{j}^{c}$, in this
case, provides additional opportunity insights beyond traditional
accessibility measures, which will shed light on if certain groups of
activities are (disproportionally) concentrated near charging stations and
allow us to examine the disparities in accessing opportunities across all
PCSs.

Based on the POI score, we extend the conventional accessibility measure by
concurrently examining the physical impedance to reach a PCS and the
opportunity cost when charging at the PCS. Nevertheless, we need to
acknowledge the difference between two distinct scenarios: (1) an EV user
visiting a place as the primary decision and charging at that location as a
derivative activity, and (2) an EV user visiting a charging station as the
primary decision and engaging in extra activities as the derivative decision.
This gives rise to two distinct opportunity-centric accessibility measures as
detailed below:

Activity-Induced Charging Accessibility (AICA): This metric corresponds to
scenario (1) and measures how PCSs are easily accessible/available when EV
users engage in their daily activities from their home locations (the centroid
of CBG in our case). For example, EV users can utilize associated charging
stations at their nearby shopping venues. Mathematically, we express the AICA
of a CBG $i$ as:

$\displaystyle AICA_{i}^{c}$ $\displaystyle=\sum_{k\in K^{c}_{i}}\sum_{j\in
S}\mathbb{I}_{kj}\times N_{j}\times e^{-\beta t_{ik}}$ (3)

where $K^{c}_{i}$ is the set of the nearest 20 POIs in category $c$ to CBG $i$
and $\mathbb{I}_{kj}$ is the indicator variable that takes the value of 1 if a
PCS $j$ is within 250 meters of POI $k$ and 0 otherwise. $AICA_{i}^{c}$
agglomerates both physical impedance from a CBG $i$ to nearby activity
locations $k\in K_{i}^{c}$ and the availability of accessing a charging port
at PCSs associated with each POI.

Charging Induced Activity Accessibility (CIAA): This metric corresponds to
scenario (2) and measures the number of opportunities in category $c$ when EV
users from CBG $i$ access PCSs in its catchment area (nearest 20).
Mathematically, we express CIAA as:

$\displaystyle CIAA_{i}^{c}$ $\displaystyle=\sum_{j\in S}\sum_{k\in
K^{c}}\mathbb{I}_{ij}\times\mathbb{I}_{kj}\times e^{-\beta t_{ij}}$ (4)

where $CIAA_{i}^{c}$ combines the physical impedance from CBG $i$ to the
nearest 20 PCSs and accumulates the number of POIs in category $c$ associated
with each PCS. As a consequence, this metric considers the scenario when an EV
user selects to charge at PCS $j$ and gauges the availability of engaging in
additional activities rather than waiting in the vehicle.

###  4.2 Counterfactual Analyses

Merely quantifying the accessibility score, regardless of whether considering
proximate opportunities or not, does not paint a complete picture that enables
the equality assessment of a PCS deployment plan. This must be supported by
comparing the current deployment plan with feasible alternatives so that both
quantitative and qualitative assessments can be made. In this regard, we
conduct counterfactual analyses to compare the existing PCS layout in major
metro areas with four alternative scenarios representing four different
planning philosophies using the above metrics. The four alternative scenarios
are as follows:

  1. 1.

Uniform: Under the uniform strategy, current PCSs are reassigned spatially
uniformly across the metro area. This approach allowed us to establish a
baseline for comparison, providing insights into the distribution of stations
when no particular factors other than a uniform spatial coverage were taken
into account.

  2. 2.

Population-based: In the population-based strategy, current PCSs are
redistributed proportional to the population density of CBGs, with more PCSs
in CBGs with higher population density.

  3. 3.

Profit-based: The profit-based strategy involves reassigning existing PCSs
based on the combination of median-household income and the population of
CBGs. By examining this scenario, we seek to investigate the potential
disparities that may arise from prioritizing economically advantaged areas.

  4. 4.

Equity-based: The equity-based strategy reallocates PCSs to make accessibility
to PCSs equitable across all CBGs. This approach aims to understand the gains
and losses of equity-driven charging station deployment that may arise because
of additional opportunity considerations. The equity-based reallocate is
achieved by solving the integer linear assignment problem with the objective
function being $\text{Max}\,\text{Min}_{i\in Z}A_{i}$ and is subject to the
total number of PCSs in the current plan.

In our counterfactual analysis, we make two adjustments to ensure the
accessibility outcomes across various scenarios are directly comparable to the
current configuration of charging infrastructure. Firstly, acknowledging that
the number of EVSEs can be different across charging stations, we equate the
number of EVSEs at all stations across the scenarios and the existing layout,
allocating one EVSE per station. Secondly, to have a realistic comparison of
distance and nearby opportunities between the scenarios that aligns with the
existing layout, we reposition the designated stations to the closest road
node within the transportation network.

##  5 Results

In line with our research questions, we present our results in the following
order: First, we examine CBGs’ accessibility within and between the metro
areas, focusing on gravity-based measures. Subsequently, we extend our
analysis to include the opportunity-centric metrics introduced in this study,
namely Charging-Induced Activity Accessibility (CIAA) and Activity-Induced
Charging Accessibility (AICA). Following this, we explore the association
between CBGs’ accessibility and income levels, highlighting the income-based
disparities in access to charging infrastructure within large U.S.
metropolitan areas. Concluding our results, we perform a counterfactual
analysis to evaluate the efficacy of the existing PCS deployment layout
against four alternative strategies, providing comprehensive insights into the
implications of different deployment approaches.

###  5.1 Accessibility to Public Charging Stations

#### Disparities in PCS Accessibility through Gravity-based Approach

We first present the results regarding the CBGs’ gravity-based accessibility
to PCSs in Figure 3. The figure visualizes the complementary cumulative
density function (CCDF) for the distribution of PCS accessibility across
twenty metro areas in a log-log scale, where each line visualizes the
proportion of CBGs, $P(x>A)$, that has an accessibility score greater than
$A$. As a consequence, the further each line stretches towards the right, the
greater the variance of accessibility across all CBGs. We observe a notable
difference in the accessibility of CBGs to PCSs both within and between metro
areas. For each metro area, we observe that the tail of the accessibility
distribution (top 10 percentile of CBGs’ accessibility scores) can be well
approximated by the power-law distribution, $y\propto x^{-\gamma}$, with
$\gamma$ in the range of 3.17 (San Jose, CA) to 13.52 (Phoenix, AZ) for the
selected metro areas. The estimated power-law exponents, $\gamma$,
characterizing the tail of the accessibility distributions, are depicted as
part of Figure 3. The power-law approximation signifies that a very small
proportion of CBGs have a high gravity-based accessibility score to PCSs,
whereas the majority of the CBGs have poor access to the PCSs [13]. For
instance, in San Jose, CA, the top 1% of CBGs boast a gravity-based
accessibility score that is over 3.55 times greater than 50% of the CBGs.

#### Charging Infrastructure Expansion and PCS Accessibility Disparities

We are further interested in checking whether the expansion of public charging
infrastructure is associated with a more gradual decline in the approximated
power-law distributions, thereby exacerbating the disparity in PCS
accessibility across CBGs. To explore this, we perform an Ordinary Least
Squares (OLS) regression, using the power-law exponent parameter ($\gamma$) as
the dependent variable and the number of PCSs (X) as the independent variable.
The results suggest that $ln(\gamma)\sim-0.49ln(X)$, with p-value=$0.001$ and
R-squared value=$0.49$. This indicates that a 1% increase in the number of
public charging stations will result in 0.49% reduction in the power law
coefficient $\gamma$. This statistically significant relationship, confirmed
at the 0.05 significance level, leads us to conclude that PCS network growth
tends to diminish the rate at which the CCDF of accessibility scores decays,
hence resulting in greater inequality for the PCS accessibility among all
CBGs. This is while CBGs in metros with a higher number of stations do not
hold a higher accessibility score on average (for the top 10 metros, that is
14.8% lower than those of the bottom 10).

Figure 3: Census Block Groups’ Accessibility to Public Charging Stations

###  5.2 Distribution of Opportunities around Public Charging Stations

#### Activities Attachment to Charging Infrastructure

Before delving into the opportunity-centric accessibility measures, it is
crucial to map out the extent to which POIs are linked to PCSs across various
place types and metropolitan areas. Thus, our initial analysis focuses on the
distribution of over 5.5 million selected POIs within metro areas, finding
that 1.4 million of these are located within a 250-meter radius of a PCS. In
Figure 4(a), we assess the spatial arrangement of POIs around PCSs in
comparison to their general distribution throughout the metro area. We further
categorize the POIs into two groups: ‘elastic’ activities, which are
associated with shorter dwell times and for which alternatives are readily
available (like dining places or fitness centers), versus ‘inelastic’
activities, characterized by longer dwell times and fewer alternatives (such
as churches, government offices, and workplaces).

We report that there is a notable difference in the presence of certain POI
categories near PCSs as opposed to their distribution metro-wide, with a
distinct preference for PCSs to be located near sites of elastic activities.
Specifically, categories such as “Accommodation & Food Services,” “Finance,
Insurance, and Information,” and “Professional, Scientific, Technical, and
Educational Services” are more densely populated around PCSs, exceeding the
metro-wide percentage for these categories by 22.2%, 26.0%, and 39.2%,
respectively. Within the “Accommodation and Food Services” category alone, the
variation spans from 7.2% for Snack and Nonalcoholic Beverage Bars to 58.2%
for Casinos. Conversely, inelastic activities like “Public Administration and
Other Services” and “Transportation and Warehousing” show a 19.8% and 39.2%
reduction in presence near PCSs, respectively. These results suggest that PCSs
are more commonly found near locations conducive to shorter, flexible
activities, offering users a variety of options to utilize their time
effectively while charging. However, those seeking PCSs near their less
flexible, inelastic activity locations may find fewer options available,
potentially narrowing the range of activities they can engage in during
charging sessions.

#### Equality of the Number of Opportunities at Charging Stations

Besides the differences in overall POI concentration, we also observe a
significant inequality in the associated number of POIs across all the PCSs,
as depicted by the Lorenz curves in Figure 4(b). The outcomes highlight a
pronounced inequality in the availability of opportunities at various PCSs, a
trend consistent across metropolitan areas for both categories of activities,
elastic and inelastic. In all 20 metro areas, a few numbers of PCSs are
associated with a very high number of POIs (more than 50% of the cumulative
share of POI scores corresponds to less than 10% of the PCSs). We can further
quantify the degree of inequality via Gini index [16], calculated as the
distance of each curve from the diagonal line (line of perfect equality). The
Gini coefficient ranges from 0 to 1, with 0 representing perfect equality and
1 denoting total inequality. The San Jose, CA metro area is found to be
associated with the highest level of inequality, with Gini coefficients of
0.86 for elastic activities and 0.80 for inelastic activities. San Fransisco,
CA, closely follows with Gini coefficients of 0.84 and 0.81 for elastic and
inelastic activities, respectively. The Gini coefficients for all other cities
are at least 0.59 (Miami). In conclusion, while having abundant activity
locations near PCSs is desirable, the existing public charging layout sees a
significant imbalance in access to opportunities. Such disparities motivate us
to take a more nuanced approach to evaluating accessibility by integrating the
traditional accessibility metrics with opportunity-based measures to
understand better and address the existing accessibility gaps in PCS
deployment.

(a) Spatial Arrangement of POIs

(b) Lorenz curve of POIs Associated with Public Charging Stations

Figure 4: POIs Associated with Public Charging Stations

###  5.3 Opportunity-centric Accessibility to Public Charging Stations

The findings discussed thus far offer an in-depth examination of the
disparities in access to PCS through the lens of a gravity-based approach.
Nevertheless, we recognize that traditional accessibility measures only
provide a snapshot of the performances of PCS infrastructure. Building upon
this foundation, we focus on uncovering if the issues identified through
conventional accessibility evaluations are mirrored or even magnified when
considering the availability of opportunities associated with PCSs.

#### Charging-induced Activity Accessibility and Activity-induced Charging
Accessibility

Similar to the results for the distribution of the accessibility scores in
Section 5.1, we visualize the complementary cumulative density function (CCDF)
for the distributions of Charging-induced Activity Accessibility (CIAA) and
Actiivity-induced Charging Accessibility (AICA) in Figure 5(a) and Figure
5(b). The results reveal that the tail of the CCDFs for CBGs (with top 10% of
scores) in both metrics can be well approximated by the power-law
distribution, which is indicative of inequalities of opportunity-based
accessibility for different communities with few CBGs having disproportionally
high opportunity-based accessibility scores. The observations hold for all
metro areas, and we hypothesize that such inequality issues, both in terms of
conventional accessibility and opportunity-centric measures, are likely
universal across major cities. Nevertheless, we observe different trends among
the distributions for AICA and CIAA. This is statistically verified by fitting
the power-law distributions to the CBGs with the top 10% of the scores. The
fitted results show the range of $\gamma$ between 1.30 (Austin, TX) to 3.77
(Albany, NY) for AICA and a range of 0.83 (Baltimore, MD) to 7.90 (Albany, NY)
for CIAA for the fitted distributions.

(a) Charging-induced Activity Accessibility (CIAA)

(b) Activity-induced Charging Accessibility (AICA)

Figure 5: CCDF of CIAA and AICA Distributions Across CBGS in 20 Metro Areas

#### Disparities in PCS Accessibility through Opportunity-centric Measures

We note that the $\gamma$ value range for Activity-induced Charging
Accessibility (AICA) is consistently lower than that of both gravity
accessibility and Charging-induced Activity Accessibility (CIAA). This
suggests that the CCDFs for AICA decay slower, pointing to a broader disparity
where AICA suffers a greater inequality across CBGs than CIAA. We believe this
disparity is likely exacerbated due to the compounded inequality of access to
PCSs and the unequal distribution of opportunities (POIs) in each metro area.
Consequently, the placement of charging stations near (existing) major
activity locations will inevitably lead to inequalities in opportunity-centric
accessibility measures. Moreover, the $\gamma$ does not capture CBGs with low
AICA scores as it focuses on the tail of the CCDF. For areas with low AICA
scores, we note a significantly worse score compared to accessibility measures
of other areas since there is no PCS available near their top 20 activity
locations. The analysis further reveals that the disparity in AICA is more
pronounced than in CIAA, as evidenced by higher $\gamma$ values for CIAA,
suggesting a quicker CCDF decay and somewhat mitigated inequality. This
difference underscores two primary contributors to the disparities observed
with CIAA: (1) an inequitable PCS deployment that disproportionately affects
CBGs, particularly those with lower incomes who may have access to fewer
stations, and (2) a skewed distribution of POIs in proximity to PCSs, as
indicated by opportunity scores.

Regarding the category-wise distribution of opportunity-centric accessibility
for metro areas, we observe a distinct pattern regarding access to activities.
Notably, inelastic activities—those less substitutable and often essential,
like government services or workplaces—tend to have higher Activity-induced
Charging Accessibility (AICA) scores in 12 of the 20 metro areas studied,
indicating generally better access. This is underscored by a more marked
disparity in access to these inelastic activities, evidenced by lower $\gamma$
values for the top 10% distribution in 11 metro areas when compared to elastic
activities. This suggests that while access to inelastic activities is
generally higher when making charging infrastructure the secondary decision
after activity choice, the inequality in this access is also more pronounced.
On the other hand, when examining Charging-induced Activity Accessibility
(CIAA) scores, all 20 metro areas showed higher median scores for elastic
activities—like dining and shopping. Moreover, in 13 metros, we observe a
greater inequality in access when considering the tail of the scores with
lower $\gamma$ values. This trend is the reverse of our observation with AICA,
where elastic activities, being the secondary choice after deciding to charge,
have higher accessibility but with a significant variation in equality of
access. This analysis reveals a critical insight: focusing solely on one
dimension of charging station selection and associated activities does not
lead to an equitable distribution of charging access, as it underscores the
intricate relationship of first and derivative travel decisions with different
types of activities for EV users.

Finally, a positive correlation between CIAA and AICA scores across all metro
areas (with values as high as 0.41, 0.42, and 0.45 for New York, Miami, and
Kansas City, respectively) suggests that areas with lower AICA scores also
tend to exhibit poorer CIAA. This pattern highlights a critical concern for
future PCS development strategies, particularly for EV owners in
underprivileged communities. Individuals from these communities may encounter
difficulties in finding conveniently located PCSs, potentially incurring an
”invisible charging tax” through lost opportunities while waiting for their
vehicles to charge. In contrast, EV users from other communities might benefit
from the convenience of performing other activities while their vehicle
charges, thereby making more efficient use of their time.

###  5.4 Income-level Association with Accessibility Metrics

Our previous findings underscore the necessity of investigating CBGs’ income
levels’ association with gravity-based accessibility and the introduced
opportunity-centric measures. This is partly due to the observed disparities
in the opportunity-centric accessibility to the charging infrastructure,
particularly the broader inequality in AICA compared to CIAA and gravity
access, which points to a compounded lack of access to PCSs and an unequal
distribution of opportunities in the metro areas. Moreover, the pronounced
disparity in access to elastic activities as the primary travel choice and the
inelastic activities as the secondary travel choice when selecting a charging
station suggests potential structural barriers that may disproportionately
affect certain populations, presenting challenges under “invisible charging
barrier” due to inconvenient charging options.

Therefore, we assess the relationship between the median income levels of CBGs
within metro areas (X) and their accessibility scores to charging
infrastructure (Y) by performing an Ordinary Least Squares (OLS) regression,
expressed as $ln(\gamma)\sim\beta ln(X)$, with R-squared values up to 0.17
(Albany, NY). The results, depicted in Figure 6, showcase the standardized
coefficient ($\beta$) intervals for median income level (X) at 0.05
significance level across different accessibility metrics. We report distinct
disparities across accessibility metrics regarding the median income
coefficients. In 13 of the 20 metropolitan areas, a negative and statistically
significant relationship (p-value ¡ 0.05) is observed between income levels
and gravity-based accessibility to charging stations. For three metros-
Chicago, San Diego, and San Jose -income was positively associated with
gravity accessibility at 0.05 level. This implies that higher accessibility is
often associated with lower income levels when distance is the only criterion
for measuring access. We hypothesize this is due to the location of lower-
income CBGs near downtown areas and city centers and the pattern of higher-
income neighborhoods located in the suburbs. Moreover, when the focus shifts
to Charging-induced Activity Accessibility (CIAA), where charging station
access is the primary travel decision, the number of metros with a negative
income coefficient decreases to 10, and metros with a positive significant
relationship between income and accessibility increase to four (that is
Atlanta, Kansas City, San Jose, and Washington D.C.). Since the CIAA already
captures the distance impedance to the charging stations and only then adds a
layer of access to nearby opportunities, we expect this modest change in
coefficients to be a result of a high correlation between the gravity access
and CIAA, ranging from 0.22 for Washington D.C. to 0.69 for Phoenix. In
contrast, when examining Activity-induced Charging Accessibility (AICA), where
the availability of activities takes a selection priority over the location of
charging stations, a reverse trend is noted. Coefficients of income on AICA
for 12 metros are significant and positive at the 0.05 level, with only 3
metros (Austin, San Francisco, and Seattle) showing a negative and
statistically significant association at this level. These findings underscore
an underlying structural inequality within metro areas, which can be at least
partially linked to income distribution. Additionally, we highlight that
gravity-based metrics alone fail to fully capture the nuances of charging
station accessibility and disparities associated with it, emphasizing the
importance of considering a broader range of opportunities nearby and the
charging station selection process.

Figure 6: CBGs’ Income Association with Accessibility Metrics

###  5.5 Counterfactual Analyses

With the above discussions, we present our final results on the outcomes from
the counterfactual analyses. Figure 7 illustrates the results of comparing the
existing layout with alternative deployment strategies. We use radar plots to
show the median performances per each metric across all metro areas, and the
comparisons between the current deployment layout and four other alternative
strategies are shown in Figure 7(a). The results in the figure are scaled
performances compared to the uniform strategy, with a value of 0 being the
uniform baseline and the innermost ring representing 50% decrease, with each
outer ring representing an additional 100% improvement over the uniform
baseline. As an example, equity-based PCS deployment results in the worst CIAA
performance (68% lower than baseline), whereas the current PCS deployment
scores 85% greater median CIAA. Unsurprisingly, the equity-based approach
obtains the highest PCS accessibility (65% higher than the baseline), and
current layout distribution leads to the worst performances in this category,
but only with a 1.4% decrease from the baseline strategy. At the same time,
the current layout hold the highest POI scores and CIAA measures with levels
of AICA accessibility performances near the baseline strategy. The POI scores
for the current deployment are nearly 225% better than the baseline (Uniform).
Nevertheless, the AICA measure for the current deployment shows no improvement
compared to the baseline (Uniform), where they are both 35% lower than the
population-based AICA median scores. It is worth stressing that the Equity-
based approach, despite yielding the best accessibility measures, results in
the worst performances for the opportunity scores, AICA, and CIAA measures,
with over 67% decrease compared to the baseline in all these metrics. This is
key results as our addressing of equitable charging infrastructure deployment
still relies on a broad set of tools centered around the geospatial location
of underserved communities [4, 30, 64]. We note that in this case, promoting a
PCS deployment strategy that solely relies on equal distance to stations does
not lead to equitable outcomes if the need to participate in additional
activities while charging is not considered. We further observe while the
current deployment suffers apparent disparities in each city, it still
maintains a balance among all four measures. We conjecture that the
disparities observed for current deployment may be due to the structural
disparities that carry over from existing civil infrastructures (e.g., the
road network and community segregation), which will require further
investigation.

(a) Scenarios Performance Compared to Uniform Deployment

(b) Scenarios Performance Comparison for each Accessibility Metric

Figure 7: Performance Comparison of Different Deployment Scenarios

While the above results provide an aggregate view of the CIAA and AICA
performances, we further break them down into six individual POI categories as
shown in Figure 8(a) for the category-specific CIAA performances and in Figure
8(b) for the category-specific AICA performances. Similar to the overall
observations, we again confirm that the equity-based approach leads to the
worst AICA and CIAA performances in every category of POIs with a reduction of
at least 38% compared to the baseline strategy. Based on the results, one
important observation is that the current deployment strategy scores well in
CIAA and AICA measures for elastic activity categories but yields worse
performances for inelastic activity measures. We believe this is mainly due to
the current policy guidance that promotes public-private partnerships in
deploying PCSs and many local businesses investing in PCSs at their locations,
hence the significantly higher CIAA measures, ranging from 14% increase in
‘Transportation and Warehousing’ to 159% in ‘Accommodation and Food Services’.
Nevertheless, the outcomes do not necessarily align with real-world needs if
we consider the charging events being derived mainly from daily activities
where the population-based approach gives the best overall AICA performance
across the categories. This further highlights the disparities that arise from
existing opportunity distributions in major metro areas. As discussed earlier,
the nearby activity locations are more adjacent to charging opportunities for
the more economically advantaged communities in terms of physical distance and
the number of opportunities. This, together with the disparity in existing
civil infrastructure, renders the planning of PCSs a highly challenging
problem. Thus, we raise a concern that optimizing PCS deployment purely based
on distance-based accessibility or opportunity-based accessibility will lead
to undesirable outcomes that may either result in a waste of resources or
exacerbate existing barriers in the community. It is important to balance the
consideration across multiple dimensions and plan PCS proactively with the
consideration of existing disparities and societal barriers, as discussed in
this study, such that a PCS deployment plan for long-term community benefits
can be achieved.

(a) Charging Induced Activity Opportunities

(b) Activity Induced Charging Opportunities

Figure 8: Opportunity Measures Score of Different Deployment Scenarios

Finally, we summarize the performance of the current PCS infrastructure in our
counterfactual analysis, where we observe an increase in the median score
value by at least 63% for POI attachment and CIAA compared to all alternative
deployment strategies. This further underscores the significance of activities
nearby in locating the PCSs. However, as discussed earlier, a significant
disparity in access to nearby activities remains, with over 50% of cumulative
POI scores concentrated around fewer than 10% of the stations. Moreover,
despite a low variance in gravity access scores across different
strategies—excluding the equity-based approach—the current deployment yields
the lowest scores with a median score 6% lower than the population-based
scenario. This highlights an underlying preference for locating stations,
which results in reduced gravity accessibility (higher distance impedance)
even compared to a uniform plan. Finally, the current strategy’s AICA score is
marginally lower than that of a uniform deployment (0.8% lower) and 35% less
than the population-based deployment plan. This discrepancy is mainly due to
the distribution skew within metro areas, where a few communities have high
access to charging options when engaging in nearby activities while others
face lower AICA scores. We previously showed this relationship to be
significantly associated with income for 60% of the metro areas. Overall,
while the existing charging layout outperforms the alternative scenarios in
two of the metrics, it also points to a need for a more equitable PCS
deployment approach that ensures both the access to charging facilities and
associated activities are more evenly distributed across communities.

##  6 Discussion & Conclusion

In this study, we incorporate an opportunity-centric aspect to the traditional
view of civil infrastructure deployment, which enables us to connect the links
between communities, existing charging layouts, and the location of
activities. Our study allows for a thorough comparison of the existing
charging infrastructure with various alternative charging deployment
philosophies that might each prioritize a different aspect in addressing the
complexity of the multi-faceted charging deployment problem. Our comprehensive
analyses lead to four significant conclusions, outlined as follows:

  1. 1.

There are notable disparities in how accessible charging stations are for
different communities. We observe that the tails of the CCDF for accessibility
distributions across communities can be well approximated by the power-law
distribution. This is indicative of a few communities having significantly
better access to PCS than the majority of the communities. For instance, in
San Jose, the PCS accessibility in the top 1% of CBGs can be 3.6 times better
than in the bottom 50%. Meanwhile, we observe a highly skewed PCS score in the
current layout, with less than 10% of the PCSs accounting for more than 50% of
the cumulative share of POIs nearby across all 20 metro areas. This raises a
critical concern where few PCSs are adjacent to the majority of the accessible
activities, potentially exacerbating the inequalities in access when
considering the activities.

  2. 2.

Opportunity-based accessibility reveals more severe inequality issues for
current PCSs. When we consider the intertwined relationship between activities
and choices for charging, we notice that disparities in opportunity-based
access within and between metropolitan areas are made worse. This underscores
the inherent biases in where we position charging stations relative to
activity hubs, which typically lean toward favoring specific areas. As a
result, we see a more intense inequality in accessibility when activity
locations are the primary decision factor, and charging stations are
secondary. This outcome is particularly highlighted where, in the majority of
the metro areas, CBGs with higher income are associated with better charging
accessibility, 60% being significant. This motivates us to reassess the
quality of the existing charging infrastructure plan, as well as compare
scenarios that prioritize different planning philosophies.

  3. 3.

Equitable deployment without considering the activities is not necessarily
equitable. Based on the results from counterfactual analyses, we report that a
PCS deployment strategy that prioritizes an equitable conventional
accessibility measure will result in the least equitable deployment outcomes
with the worst opportunity-based accessibility metrics (at least 67% decrease
from a uniform deployment scenario). This finding is contrary to commonly
recommended practices in other civil infrastructure deployments when distance-
based accessibility is often used to evaluate the quality of the
infrastructure deployment. This outcome highlights the complexity of planning
PCSs as compared to other public infrastructures and urges the adoption of
multidimensional metrics to guide the deployment of PCSs.

  4. 4.

PCS deployment extends the existing inequalities in our cities. The current
PCS deployment strategy achieves a POI attachment and CIAA score improvement
across all 20 metros over alternative strategies, with a median score increase
of at least 63%. Nevertheless, the existing layout results in the lowest
gravity access scores, 6% below the population-based scenario, indicating a
preference for station location that compromises broader accessibility.
Furthermore, the AICA score under the current deployment is slightly lower
than uniform deployment by 0.8% and significantly lower by 35% compared to a
population-based plan, reflecting a skewed distribution that favors a minority
of higher-income communities for access to charging options while engaging in
nearby activities, across 60% of the metro areas. Overall, proactive planning
of PCS and activity locations is essential to overturn this process,
emphasizing distributing new PCSs more evenly rather than excessively
attaching new PCSs to already-popular locations.

Lastly, we suggest two future research directions that could enhance our
study’s findings. First, our study is able to link the disparities in
opportunity-centric accessibility with variations in income levels across
metropolitan areas. However, we hypothesize income is not the sole factor, and
other potential barriers, including race and education, should also be taken
into account to fully understand the complexities of accessibility issues and
reveal if consistent patterns exist across the major metro areas. Second,
while our study employs accessibility as a static metric to explore the
spatial relationship between communities and public infrastructure,
incorporating data on population movements could offer more detailed insights.
This could help us better understand location-specific measures of PCSs and
potentially link to EV ownership across various communities. The results of
such research could then be employed to jointly analyze and craft policies
that promote long-term sustainability, considering both EV users and PCSs
concurrently.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or
personal relationships that could have appeared to influence the work reported
in this paper.

## Acknowledgement

The authors are grateful to the National Science Foundation for the award BCS
#2323732 to support the research presented in the paper. However, the authors
are solely responsible for the findings presented in this study.

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