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An Analysis of Attributes of Electric Vehicle Owners’ Travel and Purchasing Behavior:
The Case of Maryland
Hyeon-Shic Shin1; Z. Andrew Farkas2; and Amirreza Nickkar3
1Assistant Professor, City and Regional Planning, Morgan State Univ. E-mail:
hyeonshic.shin@morgan.edu
2Professor, Director of the National Transportation Center, Morgan State Univ. E-mail:
andrew.farkas@morgan.edu
3Ph.D. Student, Transportation and Urban Infrastructure Studies, Morgan State Univ. E-mail:
amnic1@morgan.edu
ABSTRACT
An emerging technology in transportation, electric vehicles (EVs) decrease reliance on
pollutant fuels and bring numerous environmental benefits. The current research investigated the
socio-demographic attributes that may contribute to EV ownership and owners’ purchasing
behavior. The goal of this study is providing public policies and recommendations to decision
makers to prompt equitable EV usage by identifying influencing socio-demographic attributes of
EV purchasing behavior. This study surveyed registered plug-in hybrid electric vehicle (PHEV)
and battery electric vehicle (BEV) owners in Maryland from May 28, 2015, to February 19,
2016. In total, 1,257 EV owners in Maryland completed usable surveys. A set of statistical
analysis methods was employed to analyze the data. Researchers constructed a multinomial
logistic model (MNL) to examine the associations between EV owner characteristics and their
reasons for purchasing/leasing the EV, and an analysis of variance test (ANOVA) was carried
out to explore possible relationships between EV owners’ travel behavior and their socio-
demographic features. Findings suggest that, first, age, education, income, household size,
number of vehicles in the household, marital status, and political affiliation play a significant role
in preferences attributes of participants for purchasing/leasing an EV. Second, environmental
issues are the main reason for purchasing/leasing EVs, but the EV owners who had longer
commutes were more concerned about the price and status of the EV owner and efficiency and
performance than were those with shorter commutes. Third, EV owners who are older and more
educated, drive less than other groups.
Keywords: Electric vehicle, Incentive policies, Equity, Willingness-to-adopt, Maryland
INTRODUCTION
Extending the use of electric vehicles (EV) has been considered in past decades in the US as
a solution for reducing the dependency on fossil fuels and preserving environmental resources.
Although the market share of EVs is much smaller than that of internal combustion engine
vehicles (ICEV), the EV has been accepted as an environmentally friendly transportation mode
that may meet the goals of sustainable development (Momtazpour et al., 2016; Ramalingam &
Indulkar, 2015).
According to the Maryland Motor Vehicle Administration (MVA), the total number of EVs
registered in Maryland increased from 609 in FY 2012 to 6,788 in FY 2016 (Maryland Electric
Vehicle Infrastructure Council, 2017). Battery electric vehicles (BEVs) accounted for 39% and
61% were plug-in hybrid electric vehicles (PHEVs). In addition to a federal income tax credit
ranging from $2,500 to $7,500 (Internal Revenue Service, 2017), Maryland EV owners are
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eligible for state excise titling tax credits of up to $3,000, depending on battery capacity and
funding availability (Maryland Department of the Environment, 2017). There is a cap of $60,000
on the vehicle purchase price for tax credit eligibility (Maryland Motor Vehicle Administration,
2017). Further, Maryland EV owners pay little or no motor fuel tax to the state’s transportation
trust fund when refueling the vehicle. However, the benefits of these incentive programs seem to
be enjoyed primarily by those with higher affordability. Research shows that for owners of high-
end luxury EVs, financial incentives are not important to the purchase decisions (Clinton et al.,
2015). Because of significant monetary incentives to households toward EV purchase, equity
issues result.
Several research questions arise from the state’s policies to spur EV ownership through
subsidizing purchase price and deploying public charging facilities at rail transit stations. Who
drives EVs and what are EV owners’ socioeconomic characteristics? What are the primary
reasons for EV purchase decisions and how are they related to owners’ attitude toward and
preferences for purchasing reasons such as environmental concerns, safety, gas prices, vehicle
performance, and others? Is EV purchase associated with political affiliation? To answer the
questions, this study posited whether, in addition to the net price of EVs, other top purchasing
reasons vary by EV owners’ socioeconomic characteristics and political affiliation. The current
study has two main objectives: first, to analyze—using a survey of EV owners—the relationships
between EV purchasing reasons and socioeconomic characteristics, and, second, to estimate
probabilities of EV purchase reasons as a function of participants’ socioeconomic attributes.
Finally, the study investigates possible relationships between commuting driving distance as an
index for travel behavior of the EV owners and their sociodemographic characteristics. The
results of this study could be utilized by transportation authorities, public transport investment
agencies, and collaborators in emerging transportation systems.
LITERATURE REVIEW
Liao et al. (2017) classified EV adoption studies based on studies’ considerations and
approaches into economic and psychological categories. According to this classification,
economic studies are looking for possible factors that may have some effect on users’ decisions
when they have to choose among a group of vehicle alternatives, while a psychological study is
seeking the reason(s) behind their decision to purchase an EV by assessing the role of
psychological socio-demographic characteristics. Most EV adoption studies used logistic
regression models as the mainstream choice model. Multinomial logit (MNL) models used in
most of past studies (Achtnicht et al., 2012; Mau et al., 2008; Musti & Kockelman, 2011). After
MNL, Mixed Logit models were the most frequently used modelling technique in past studies
(Helveston et al., 2015; Rasouli & Timmermans, 2016).
Socio-economic, financial and operational factors on EV purchasing behavior
The influence of demographic factors on EV purchasing behavior was extensive in most of
the past studies. Education was the most important attribute proven to have a positive significant
relationship with the EV preference (Bjerkan et al., 2016; Carley et al., 2013; Hidrue et al., 2011;
Kim et al., 2014; Moons & De Pelsmacker, 2012). Similarly the household size was also cited in
many studies as an important factor (Kim et al., 2014; Peters & Dütschke, 2014; Plötz et al.,
2014; Rasouli & Timmermans, 2016). Following these attributes, the level of income was
introduced as a strong influencing factor on EV purchasing behavior in recent studies (Qian &
Soopramanien, 2011; Rasouli & Timmermans, 2016; Sang & Bekhet, 2015; Sierzchula et al.,
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2014). Consequently the role of number of vehicles in the household has been proved in some
research to be positively significant (Peters & Dütschke, 2014; Zhang et al., 2011). Other socio-
demographic attributes were generally open to discussion; for instance, some studies explored
whether older people are more interested in purchasing an EV (Bjerkan et al., 2016; Carley et al.,
2013; Hidrue et al., 2011; Musti & Kockelman, 2011). On the other hand, some studies have
found an opposite relationship (Achtnicht et al., 2012; Hackbarth & Madlener, 2013; Plötz et al.,
2014). In like manner, some studies claimed that males are more attracted to purchasing EVs
(Bjerkan et al., 2016; Carley et al., 2013; Kim et al., 2014; Rasouli & Timmermans, 2016) while
other studies refuted the role of male as a dominate gender in EV purchasing behavior. (Jensen et
al., 2013; Peters & Dütschke, 2014; Qian & Soopramanien, 2011; Schneidereit et al., 2015).
The EV purchase price has been considered in most EV adoption studies. The results of most
of these studies revealed that the price of the EV may have less significant influence on higher
income people than on others (Bjerkan et al., 2016; Carley et al., 2013; Glerum et al., 2013;
Hoen & Koetse, 2014). Moreover, people with high incomes are less sensitive to fuel cost
(Helveston et al., 2015; Zhang et al., 2011). Although most of the reviewed studies pointed out
that EV battery re-filling time and driving range is one of the concerns when purchasing an EV
(Carley et al., 2013; Hackbarth & Madlener, 2013; Jensen et al., 2013; Plötz et al., 2014; Qian &
Soopramanien, 2011; Rasouli & Timmermans, 2016), Hess et al. (2012) claimed that EV driving
range may not significantly influence people who are willing to purchase EVs. Availability of
charging stations was one of the most important factors for purchasing an EV; most of the
studies showed that EV owners tended to buy EVs if there was a charging station near their
home and workplace (Achtnicht et al., 2012; Carley et al., 2013; Hackbarth & Madlener, 2013;
Rasouli & Timmermans, 2016).
Influencing psychological factors on EV purchasing behavior
Psychologically speaking, environmental concerns have been addressed in most of the past
studies as the dominate factor to purchase EVs (Achtnicht et al., 2012; Carley et al., 2013;
Hackbarth & Madlener, 2013; Kim et al., 2014; Plötz et al., 2014; Sang & Bekhet, 2015).
Following that, social aspects of the EV (Kim et al., 2014; Peters & Dütschke, 2014; Rasouli &
Timmermans, 2016; Sang & Bekhet, 2015), symbolic value (Helveston et al., 2015; Schuitema et
al., 2013), and as a sign of modern thinking (Rogers, 2010; Schuitema et al., 2013) have been
incorporated in some studies as motivating EV purchasing behavior. Carley et al. (2013) stated
that environmentally sensitive EV owners are most likely to be highly educated. Peters and
Dütschke (2014) pointed out people with higher socio-economic status are more likely to
purchase an EV. Schuitema et al. (2013) believed that symbolic and instrumental attributes have
a say on EV purchasing behavior of the majority of people.
Proposed policies to enhance EV purchasing
Most of the past studies proposed policies to promote EV adoption (Farkas et al., 2018; Shin
et al., 2019). Pricing has the main share among all suggested policies. Reducing purchasing,
usage and road taxes topped the list in most studies, and all of them pointed out that these
policies positively influenced EV purchasing behavior (Hackbarth & Madlener, 2013; Hoen &
Koetse, 2014; Peters & Dütschke, 2014). Other reviewed studies explained that some of the
incentive policies governments used to encourage people to purchase EVs, such as providing free
parking or access to HOV lanes, may not be effective (Hoen & Koetse, 2014; Qian &
Soopramanien, 2011). For recent extensive reviews of EV owners’ preferences and policies, we
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refer readers to Liao et al. (2017) and Li et al. (2017).
Table 1. Summary of the EV Owners’ Demographic Characteristics
Demographic Characteristic
1257 database
Frequency
Percent
Gender
Male
941
74.8
Female
316
25.1
Marital status
Single
186
14.8
Married or in domestic partnership
1061
84.3
Age
Under 20
2
0.2
20 to 24 years old
3
0.2
25 to 29 years old
19
1.5
30 to 39 years old
125
9.9
40 to 49 years old
274
21.8
50 to 59 years old
386
30.7
60 to 69 years old
329
26.2
70 and older
119
9.5
Household size
One
122
9.7
Two
563
44.8
Three or more
569
45.2
Children in
household
one
829
65.9
Two
359
28.5
Three or more
66
5.2
Vehicles in
household
One
112
8.9
Two
575
45.7
Three or more
570
45.3
Education
Some high school
3
0.2
High school diploma or GED
83
6.6
Associate degree
78
6.2
Bachelor’s degree
350
27.8
Master’s degree
381
30.3
Doctoral or professional degree
357
28.4
Income
Less than $50,000
21
1.7
$50,000 – $75,000
47
3.7
$75,000 – $100,000
137
10.9
$100,000 – $200,000
426
33.9
More than $200,000
440
35.0
Race/Ethnicity
White (non-Hispanic)
989
78.6
Hispanic
27
2.1
Black or African-American
47
3.7
Asian
76
6.0
American Indian or Alaska Native
6
0.5
Other
15
1.2
Political
Affiliation
Democrat
649
51.6
Republican
175
13.9
Independent
269
21.4
Not interested in politics
117
9.3
Overall, many studies reviewed EV owners’ preferences and reasons for purchasing and
driving EVs. However, the current study uses new variables such as commuting distance and
political affiliations as influencing factors on EV owners’ purchasing and driving behavior.
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METHODOLOGY
The online surveys, conducted among registered plug-in hybrid electric vehicle (PHEV) and
battery electric vehicle (BEV) owners in Maryland, investigated their attitudes toward vehicle
purchase, travel behavior, and mode choice for work trips before and after purchase. The
methodology of this study is based on statistical analysis and tests to find out possible
relationships and correlations among variables.
EV survey
The EV survey was delivered online using the Google Form and administered from May 28,
2015, to February 19, 2016, to registered EV owners in Maryland. As this study used a non-
random sampling method in selecting participants, EV questionnaires were mailed to all EV
owners’ addresses on their vehicle registration card. We received a total of 1,323 responses from
participants, from which, after a data assessment process, 1,257 usable records were collected for
the study. The EV survey questionnaire was divided into three main sections. In section one,
respondents were asked for their socio-demographic data to assess the composition of the EV
owner status. Section two consisted of information about EV owners’ mode choice behavior and
travel patterns by asking about accessing and using rail transit and reasons that encouraged or
discouraged them to use this mode. The survey also asked for their spatial data in terms of home
and workplace zip codes to find their travel patterns. The last section collected data about current
technologies on their EV vehicle and future technologies they wish to have in their next vehicle.
Multivariate statistical analysis and models
Our modelling approach is based on two different methods. In order to examine EV owners’
reasons for purchasing or leasing an EV, we used a multinomial logit model (MNL). To find
possible relationships between travel pattern behavior of EV owners and their socio-
demographic characteristics, a set of statistical analysis as analysis of variance (ANOVA) were
used. The MNL as a maximum likelihood estimator is appropriate to use when the dependent
variable can have multiple discrete outcomes and these outcomes are not ordered (McFadden,
1973). The ANOVA test is a useful statistical test for assessing the influence of two (or more)
categorical independent variables on a continuous outcome variable. All statistical analyses were
conducted using SPSS Version 24. Statistical significance was set at 0.1, 0.05, and 0.01
probability levels.
ANALYSES AND RESULTS
From 1,257 useable records, 816 included both homes and workplaces. The remaining ones
lacked home and workplace zip codes. We used these 816 filtered records for conducting
statistical tests mentioned in the methodology section.
Descriptive socioeconomic/demographic statistics of ownership
The sample was skewed toward the male population; 75% of the respondents were male, and
25% were female. The majority of respondents were married/in domestic partnership (84.3%);
thus households with two or more people are more interested in purchasing an EV than are those
with single people (see Table 1). Most respondents were 50–59 years old (21.8%); likewise, the
EV owning proportion was approximately three times greater among the older age group (60 and
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older: 36.7%) than the older drivers (39 and younger: 11.8%). In terms of vehicles in the
household, households with two or more vehicles represented 91% of EV owners, demonstrating
that for most of the EV-owning families in Maryland, the EV is not the only vehicle in the house.
The participants were also more educated, with 59% of the respondents holding an advanced
education degree (Doctoral or Master’s degree), as compared to the 13% with College or High
school degrees. Similarly, 74% of the respondents’ household income was more than $100,000.
Regarding ethnicity and political orientation issues, the highest portion of respondents were
White (non-Hispanic) (78.6%) and the overall results might be skewed by this group. Also, the
majority of respondents were Democrat (51.6%) or Independent (21.4%), compared to
Republican (13.9%).
Figure 1. Summary of participants’ reasons for purchasing/leasing an EV
Reasons for EV purchase and owner characteristics
The EV owners in Maryland were asked to select three top reasons that encouraged them to
buy or lease an EV. Nearly all, 96.8% of participants (1,217 out of 1,257), of whom 75% were
male and 25% female, gave three top reasons. Participants were asked to choose the top three
reasons for purchasing/leasing an EV from 11 options: (a) Environmental concerns, e.g., air
quality, pollution, (b) Price of electricity vs. gasoline, (c) Tax breaks and net price of vehicle, (d)
Single occupant access to HOV lane, (e) Advanced technology, (f) Safety features of vehicle, (g)
Status of EV ownership, (h) Available charging facilities, (j) Vehicle performance, (k) Reduce
dependence on petroleum, and (l) Make or model of vehicle. Figure 1 summarizes the results of
this part of the survey. Nearly 25% of the EV owners selected Environmental concerns as the
most important reason for buying or leasing EV, and in second place they chose Reduction in
dependence on petroleum (18%), followed by Price of electricity vs gasoline (46%). EV owners
have the least concerns about Status of EV ownership and Available charging facilities as
evidenced by their last-place finish (only 1%). According to the results of the survey, less than
2% of EV users in Maryland leased their EV vehicle and the rest, 98%, bought their EV vehicle.
To simplify and summarize final results, the top three reasons are classified into three main
categories: Environmental Issues (including a and k reasons), Price and Status of the EV Owner
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(including b, c, h, and l reasons), and Efficiency and Performance (including e, f, g, I, and j
reasons). According to the results, Environmental Issues was the first reason people in Maryland
purchased an EV (46%), followed by Price and Status of the EV owner (36%) while 47% chose
Efficiency and Performance as the third reason for purchasing EV.
Figure 2. Summary of classified participants’ reasons for purchasing/leasing an EV
Three multinomial logit regression models were constructed to determine the predictive
factors for EV owners’ purchasing behavior attributes. The dependent variables in these three
models are a three level top three reasons variable. Chi-Square and their statistical significance
derived from likelihood ratio tests of the logit regression models are reported in Table 2.
Statistically significant factors in the logit models were: gender, age, education, income, marital
status, race/ethnicity, political affiliation and household size.
All three models are statistically significant at the 0.05 level. Results from Table 1 indicate
that for the first model (p value<0.001), the variables income and political affiliation (p
value<0.001) play a statistically significant role in purchasing an EV based on environmental
issues as the reference category. Also, the role of race and age at a significance level of 0.05 are
considerable, while education and marital status could be counted significant at a significance
level of 0.1. In the second model (p value<0.05), again political affiliation (p value<0.001) has
played the most important role in purchasing an EV based on price and status of the EV owner as
the reference category; furthermore household size and income were statistically significant (p
value<0.10) at a significance level of 0.10. In the last model (p value<0.05), gender, age,
education and income were statistically significant (p value<0.05) at a significance level of 0.05.
The final logit analysis examined the characteristics of the EV owners associated with each
EV purchasing reason. The results of the models of the reasons why respondents purchased EVs,
by respondent characteristics responses are presented in Table 3. The statistically significant
responses (p < 0.1, p < 0.05, p < 0.001) are shaded in color.
In “Model 1,” people with lower education levels were more likely to purchase an EV
because of the price of electricity vs. gasoline and tax issues rather than environmental issues
(OR(odd ratio): 2.5, CI(95% confidence interval): 1.41-4.44). The results of this model also
show Republicans were more likely to purchase an EV due to concerns about the price of
electricity vs. gasoline and tax issues than environmental issues (OR: 5.97, CI: 2.63-13.57). They
also were more likely to purchase an EV for the efficiency and performance of the EV rather
than environmental issues (OR: 3.16, CI: 1.25-7.96).
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Table 2. Results of Likelihood Ratio Tests
Effect
Model 1 (1st Reason)
Model 2 (2nd Reason)
Model 3 (3rd Reason)
-2 Log
Likelihood
of Reduced
Model
Chi-
Square
Sig.
-2 Log
Likelihood of
Reduced
Model
Chi-
Square
Sig.
-2 Log
Likelihood of
Reduced
Model
Chi-
Square
Sig.
Intercept
959.261a
1157.253a
1153.202a
Gender
963.156
3.895
0.143
1158.580
1.327
0.515
1159.335
6.133
0.047**
Age
972.672
13.411
0.037**
1164.850
7.597
0.269
1167.492
14.290
0.027**
Household Size
964.270
5.009
0.286
1165.554
8.301
0.081*
1155.824
2.622
0.623
Children in
Household
961.351
2.090
0.719
1163.017
5.764
0.218
1155.229
2.027
0.731
Vehicles in
Household
961.046
1.785
0.775
1163.973
6.721
0.151
1160.372
7.170
0.127
Education
970.696
11.435
0.076*
1162.025
4.772
0.573
1173.150
19.948
0.003**
Income
977.950
18.689
0.001***
1165.511
8.259
0.083*
1163.956
10.754
0.029**
Marital status
964.962
5.701
0.058*
1157.814
0.561
0.755
1156.883
3.681
0.159
Race/Ethnicity
968.257
8.996
0.011**
1157.375
0.122
0.941
1154.670
1.468
0.480
Political affiliation
1016.863
57.602
0.000***
1176.392
19.139
0.004**
1156.196
2.994
0.810
* p ≤ .1 , ** p ≤ .05 , *** p ≤ .001
Younger EV owners are more likely to purchase an EV for efficiency and performance rather
than environmental issues (OR: 6.84, CI: 1.44-25.63). People with lower income levels were less
likely to purchase an EV for efficiency and performance or environmental concerns but were
more concerned with price and status. (OR: 0.16, CI: 0.05-0.54). Single respondents were less
likely to purchase an EV because of concern about the efficiency and performance of the EV
than environmental issues (OR: 0.27, CI: 0.06-1.17).
In “Model 2,” younger EV owners are more likely to purchase an EV because of efficiency
and performance than price and status (OR: 5.59, CI: 1.15-27.30). Respondents who have fewer
people in their house were more likely to purchase an EV because of efficiency and performance
rather than price and status (OR: 5.29, CI: 1.32-21.3) while those who have fewer vehicles in
their house were less likely to purchase EV because of efficiency and performance of the EV
than price and status of the EV owner (OR: 0.61, CI: 0.37-1.0). The results of this model also
show EV owners with less income were less likely to purchase an EV because of efficiency and
performance than price and status (OR: 0.37, CI: 0.17-0.84). Republican EV owners also were
less likely to purchase the EV for efficiency and performance than price and status of the EV
owner (OR: 0.50, CI: 0.21-1.16).
In the last model, people with lower education levels were less likely to purchase an EV due
to environmental issues and more likely to do so for efficiency and performance of the EV (OR:
0.38, CI: 0.17-0.86). Respondents with lower education levels were also less likely to purchase
an EV for reasons about the environmental issues than for efficiency and performance of the EV
(OR: 2.17, CI: 1.06-4.46). The rest of the results of this model show males were less likely to
purchase an EV because of the price and status of the EV owner and more likely to do so for
efficiency and performance of the EV (OR: 0.56, CI: 0.36-0.89). Respondents who have fewer
vehicles in their house were less likely to purchase an EV because of the price and status of the
EV owner than efficiency and performance of the EV (OR: 0.61, CI: 0.40-0.93). People with
lower education levels were less likely to purchase an EV for reasons of price and status of the
EV owner and more likely to buy one for efficiency and performance of the EV (OR: 0.49, CI:
0.25-0.94). EV owners with lower income levels were more likely to purchase an EV for the
price and status of the EV owner rather than efficiency and performance. (OR: 2.24, CI: 1.14-
4.40). Finally, single respondents were less likely to purchase EVs because of the price and
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status of the EV owner and more likely to choose efficiency and performance of the EV (OR:
0.42, CI: 0.17-1.03).
Table 3. Reason why respondents purchased EV – relative odds of respondent
characteristics
Effect
Model 1
Model 2
Model 3
Price and Status of
the EV Owner vs.
Environmental
Issues
Efficiency and
Performance vs.
Environmental
Issues
Environmental
Issues vs. Price
and Status of the
EV Owner
Efficiency and
Performance vs.
Price and Status
of the EV Owner
Environmental
Issues vs.
Efficiency and
Performance
Price and Status
of the EV Owner
vs. Efficiency
and Performance
Adj. rel.
odds
95% CI
for odds
Adj. rel.
odds
95% CI
for odds
Adj. rel.
odds
95% CI
for odds
Adj. rel.
odds
95% CI
for
odds
Adj. rel.
odds
95% CI
for odds
Adj. rel.
odds
95% CI
for
odds
Gender
male vs. female
0.86
0.54-
1.38
1.71
0.89-
3.28
1.25
0.82-
1.90
1.27
0.74-
2.18
0.69
0.41-
1.15
0.56**
0.36-
0.89
Age
30 years old and younger vs. 60
years old and older
1.52
0.42-
5.56
6.84**
1.44-
25.63
2.98
0.70-
12.63
5.59**
1.15-
27.30
10.80
1.83 -
63.62
0.29**
0.12-
42.70
30 to 49 years old vs. 60 years
old and older
0.98
0.52-
1.82
1.87*
0.90-
3.86
1.12
0.65-
1.92
1.61
0.83-
3.12
1.85
0.97 -
1.73
1.73
0.89-
3.06
50 to 59 years old vs. 60 years
old and older
0.91
0.52-
1.61
0.80
0.39-
1.65
1.28
0.78-
2.08
1.23
0.65-
2.32
1.18
0.66 -
2.14
1.03
0.62-
1.72
Household size
One vs. Three or more
0.48
0.14-
1.64
4.02
0.64-
5.47
1.33
0.41-
4.25
5.29**
1.32-
21.3
2.39
0.62 -
9.16
2.48
0.72-
8.61
Two vs. Three or more
0.71
0.39-
1.32
1.00
0.45-
2.22
0.97
0.56-
1.69
2.16**
1.03-
4.54
1.06
0.54 -
2.07
1.10
0.61-
1.89
Children in household
None vs. Three or more
0.51
0.20-
1.33
0.71
0.22-
2.35
1.85
0.74-
4.58
0.90
0.30-
2.71
0.78
0.29 -
2.09
1.61
0.59-
4.40
One or two vs. Three or more
0.71
0.31-
1.61
0.92
0.34-
2.51
1.71
0.77-
3.83
1.71
0.67-
4.36
0.87
0.38 -
1.99
1.34
0.55-
3.30
Vehicles in household
None vs. Three or more
0.70
0.26-
1.82
0.92
0.27-
3.15
0.94
0.40-
2.21
0.55
0.20-
1.54
0.50
0.18 -
1.40
0.82
0.34-
1.98
One or two vs. Three or more
1.10
0.70-
1.72
0.82
0.49-
1.40
1.12
0.75-
1.67
0.61**
0.37-
1.00
0.65*
0.41 -
1.04
0.61**
0.40-
0.93
Education
College degree vs. Doctoral or
professional degree
2.16**
1.05-
4.41
1.77
0.76-
4.12
0.67
0.34-
1.29
1.19
0.55-
2.56
0.38**
0.17 -
0.86
0.49**
0.25-
0.94
Bachelor’s degree vs. Doctoral
or professional degree
2.50**
1.41-
4.44
1.60
0.83-
3.09
0.84
0.51-
1.39
0.97
0.52-
1.78
1.22
0.69 -
2.17
0.58**
0.34-
0.99
Master’s degree vs. Doctoral or
professional degree
1.56
0.89-
2.75
1.17
0.62-
2.21
0.74
0.46-
1.19
0.73
0.40-
1.32
1.36
0.78 -
2.37
0.80
0.48-
1.33
Income
Less than $100,000 vs. more
than $200,000
1.36
0.72-
2.58
0.16**
0.05-
0.54
1.03
0.56-
1.89
0.37**
0.17-
.084
2.17**
1.06 -
4.46
2.24**
1.14-
4.40
$100,000 – $200,000 vs. more
than $200,001
0.82
0.52-
1.30
0.53**
0.31-
0.90
1.13
0.75-
1.70
0.72
0.44-
1.19
1.13
0.71 -
1.81
1.68**
1.09-
2.60
Marital status
Single vs. Married
1.45
0.64-
3.27
0.27*
0.06-
1.17
0.78
0.35-
1.76
1.07
0.41-
2.79
0.66
0.26 -
1.67
0.42*
0.17-
1.03
Race/Ethnicity
White vs. Others
0.46
0.27-
0.76
0.80
0.41-
1.53
0.99
0.61-
1.61
1.10
0.60-
2.00
0.94
0.54 -
1.61
1.28
0.76-
2.15
Political affiliation
Democrat vs. Not interested in
politics
0.80
0.40-
1.63
0.56
0.25-
1.24
1.11
0.55-
2.21
0.39
0.19-
0.82
1.20
0.57 -
2.51
1.33
0.67-
2.66
Republican vs. Not interested in
politics
5.97***
2.63-
13.57
3.16**
1.25-
7.96
0.72
0.32-
1.60
0.50**
0.21-
1.16
1.64
0.70 -
3.84
1.26
0.56-
2.84
Independent vs. Not interested in
politics
1.35
0.63-
2.87
0.91
0.39-
2.12
0.77
0.36-
1.64
0.71
0.33-
1.53
1.19
0.54 -
2.63
1.07
0.51-
2.28
* p ≤ .1 , ** p ≤ .05 , *** p ≤ .001;
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Commute trip patterns and ownership characteristics
The State of Maryland has the second-highest commuting time in the US; the average time
spent commuting to work is 32.3 minutes (American Community Survey, 2015). It is
hypothesized that there is a relationship between EV owners’ travel pattern behavior, and,
therefore, in this section, a one-way analysis of variance was conducted to examine possible
relationships between socio-demographic characteristics of EV owners and their driving distance
by EV. The trip length was calculated as the average driving distance between stated home and
workplace zip codes using the Microsoft MapPoint 2013 and CDXZipStream Excel Zip Code
Add-in software. Values of F and their significance levels in ANOVA are summarized in Table
4.
Table 4. ANOVA on driving distance mileage among socio-demographic variables
Variable
Driving distance (miles)
F
Sig.
Gender
0.006
0.938
Age
1.734
0.059*
Household size
0.099
0.753
Children in household
0.984
0.322
Vehicles in household
0.837
0.361
Education
3.015
0.050**
Income
1.859
0.157
Household size * Vehicles in household
2.082
0.082*
Age * Education
2.812
0.025**
Household size * Education * Income
3.108
0.002**
* p ≤ .1 , ** p ≤ .05
According to the values in Table 4, the driving distance by EV significantly changes based on
age groups, education levels, and three combined variables of household size and vehicles in the
household, age and education, and household size, education and income. Driving distance
varied on the basis of age and education with a negative association. Older EV owners or those
with higher education levels drive less than other groups; likewise, the interaction of these two
variables is statistically significant at the 0.05 significance level. Although no statistically
significant income difference in driving distance was shown, this variable in interaction with
education and number of people in the family showed differently with a negative association that
revealed that more educated EV owners with higher income levels and bigger family size drive
less than other groups. However, household size has played a different role in the interaction
with number of vehicles in the household because this interacted variable has a positive
association with driving distance, which means EV owners with more people and vehicles in
their family drive more than other groups.
The results of analysis also show that the commute of EV owners is on average 17 miles. The
results also indicate that those EV owners who selected environmental issues as their first reason
for purchasing the EV had on average 15.25-mile commutes; those who selected price and status
of the EV owner as their first reason for EV purchase average 20.38 commuting miles; and those
who selected efficiency and performance as their first reason for purchasing an EV averaged
18.14 commuting miles.
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CONCLUSIONS
The study examined the influencing attributes of EV owners on their reasons for
purchasing/leasing an EV and assessed factors that contribute to EV adoption in Maryland. The
findings of the current research suggest new insights regarding socio-demographic characteristics
of the EV owners and dominate factors that may encourage people to purchase an EV as well as
environmental concerns and price and status of the EV owner that are associated with the
purchase. The results of this study show several findings, including, first, age, education, income,
household size, number of vehicles in the household, marital status and political affiliation
played a significant role in preferences attributes of participants for purchasing/leasing an EV.
Second, environmental issues are the main reason for EV owners purchasing/leasing an EV.
Those EV owners who had longer commutes were more concerned about the price and status
issues of the EV as well as the efficiency and performance of the EV than were those who had
shorter commutes. Third, EV owners who were older and more educated drive less than other
groups.
Political affiliation was revealed as an influencing factor in the intention to purchase an EV.
As was shown, EV owners with Republican political affiliation were more likely to consider
price and status than other reasons for purchasing an EV. This finding is in line with the findings
of Costa and Kahn (2013) that indicated that the effectiveness of energy conservation nudges
depends significantly on an individual’s political ideology. In the context of American politics,
Democrat, Peace and Freedom, and Green party members (liberals) are more likely to vote for
environmental causes than are Republican, American Party, or Libertarian party members
(conservatives). Environmental concerns influenced those who were Democrat or Independent
with higher incomes and urban orientation, the large majority of owners. These findings still
involve some generalities as there are, of course, exceptions to them, but they do suggest that
public information and marketing should be targeted to socioeconomic-political networks.
The results of this study present policy implications/suggestions for addressing the equity
issues resulting from government subsidy of EV ownership as well as charging facility
deployment strategies. Also, these findings provide insights for planners and policy makers in
developing more inclusive and equitable EV adoption strategies.
ACKNOWLEDGMENT
The authors thank the Mid-Atlantic Transportation Sustainability University Transportation
Center, led by the University of Virginia, and the U.S. Department of Transportation University
Transportation Centers Program for their financial support of this research. The authors also
thank the Maryland Motor Vehicle Administration for their cooperation with the survey of
electric vehicle owners.
REFERENCES
Achtnicht, M., Bühler, G., & Hermeling, C. (2012). The impact of fuel availability on demand
for alternative-fuel vehicles. Transportation Research Part D: Transport and Environment,
17(3), 262-269.
American Community Survey. (2015). Economic Characteristics. Retrieved from U.S. Census
Bureau:
Bjerkan, K. Y., Nørbech, T. E., & Nordtømme, M. E. (2016). Incentives for promoting battery
electric vehicle (BEV) adoption in Norway. Transportation Research Part D: Transport and
International Conference on Transportation and Development 2019
Downloaded from ascelibrary.org by University of Melbourne on 09/06/19. Copyright ASCE. For personal use only; all rights reserved.
International Conference on Transportation and Development 2019 88
© ASCE
Environment, 43, 169-180.
Carley, S., Krause, R. M., Lane, B. W., & Graham, J. D. (2013). Intent to purchase a plug-in
electric vehicle: A survey of early impressions in large US cites. Transportation Research
Part D: Transport and Environment, 18, 39-45.
doi:http://dx.doi.org/10.1016/j.trd.2012.09.007
Clinton, B., Brown, A., Davidson, C., & Steinberg, D. (2015). Impact of direct financial
incentives in the emerging battery electric vehicle market: A preliminary analysis. Retrieved
from
Costa, D. L., & Kahn, M. E. (2013). Energy conservation “nudges” and environmentalist
ideology: Evidence from a randomized residential electricity field experiment. Journal of the
European Economic Association, 11(3), 680-702. doi:doi:10.1111/jeea.12011
Farkas, A., Shin, H.-S., & Nickkar, A. (2018). Environmental and Safety Attributes of Electric
Vehicle Ownership and Commuting Behavior: Public Policy and Equity Considerations.
Mid-Atlantic Transportation Sustainability University Transportation Center University of
Virginia.
Glerum, A., Stankovikj, L., Thémans, M., & Bierlaire, M. (2013). Forecasting the demand for
electric vehicles: accounting for attitudes and perceptions. Transportation Science, 48(4),
483-499.
Hackbarth, A., & Madlener, R. (2013). Consumer preferences for alternative fuel vehicles: A
discrete choice analysis. Transportation Research Part D: Transport and Environment, 25,
5-17.
Helveston, J. P., Liu, Y., Feit, E. M., Fuchs, E., Klampfl, E., & Michalek, J. J. (2015). Will
subsidies drive electric vehicle adoption? Measuring consumer preferences in the US and
China. Transportation research part A: policy and practice, 73, 96-112.
Hess, S., Fowler, M., Adler, T., & Bahreinian, A. (2012). A joint model for vehicle type and fuel
type choice: evidence from a cross-nested logit study. Transportation, 39(3), 593-625.
Hidrue, M. K., Parsons, G. R., Kempton, W., & Gardner, M. P. (2011). Willingness to pay for
electric vehicles and their attributes. Resource and energy economics, 33(3), 686-705.
Hoen, A., & Koetse, M. J. (2014). A choice experiment on alternative fuel vehicle preferences of
private car owners in the Netherlands. Transportation research part A: policy and practice,
61, 199-215.
Internal Revenue Service. (2017). Plug-In Electric Drive Vehicle Credit (IRC 30D). Retrieved
from Internal Revenue Service, [Online] Accessible at : https://www.irs.gov/businesses/plug-
in-electric-vehicle-credit-irc-30-and-irc-30d:
Jensen, A. F., Cherchi, E., & Mabit, S. L. (2013). On the stability of preferences and attitudes
before and after experiencing an electric vehicle. Transportation Research Part D: Transport
and Environment, 25, 24-32.
Kim, J., Rasouli, S., & Timmermans, H. (2014). Expanding scope of hybrid choice models
allowing for mixture of social influences and latent attitudes: Application to intended
purchase of electric cars. Transportation research part A: policy and practice, 69, 71-85.
Li, W., Long, R., Chen, H., & Geng, J. (2017). A review of factors influencing consumer
intentions to adopt battery electric vehicles. Renewable and Sustainable Energy Reviews, 78,
318-328. doi:https://doi.org/10.1016/j.rser.2017.04.076
Liao, F., Molin, E., & van Wee, B. (2017). Consumer preferences for electric vehicles: a
literature review. Transport Reviews, 37(3), 252-275. doi:10.1080/01441647.2016.1230794
Maryland Department of the Environment. (2017). Maryland Clean Air 2017 Progress Report.
International Conference on Transportation and Development 2019
Downloaded from ascelibrary.org by University of Melbourne on 09/06/19. Copyright ASCE. For personal use only; all rights reserved.
International Conference on Transportation and Development 2019 89
© ASCE
Retrieved from Maryland Department of the Environment:
http://mde.maryland.gov/programs/Air/Documents/MDCleanAirProgress2017.pdf
Maryland Electric Vehicle Infrastructure Council. (2017). Interim report to the governor and
Maryland general assembly. Retrieved from Maryland Electric Vehicle Infrastructure
Council, Maryland Department of Transportation, Hanover
Maryland Motor Vehicle Administration. (2017). Titling-Excise tas credit for plug-in electric
vehicles. Retrieved from Maryland Motor Vehicle Administration, Accessible at :
http://www.mva.maryland.gov/about-mva/info/27300/27300-71T.htm:
Mau, P., Eyzaguirre, J., Jaccard, M., Collins-Dodd, C., & Tiedemann, K. (2008). The ‘neighbor
effect’: Simulating dynamics in consumer preferences for new vehicle technologies.
Ecological Economics, 68(1), 504-516.
McFadden, D. (1973). Conditional logit analysis of qualitative choice behavior.
Momtazpour, M., Bozchalui, M. C., Ramakrishnan, N., & Sharma, R. (2016). Installing Electric
Vehicle Charging Stations City-Scale: How Many and Where? Computational Sustainability
(pp. 149-170): Springer.
Moons, I., & De Pelsmacker, P. (2012). Emotions as determinants of electric car usage intention.
Journal of Marketing Management, 28(3-4), 195-237.
Musti, S., & Kockelman, K. M. (2011). Evolution of the household vehicle fleet: Anticipating
fleet composition, PHEV adoption and GHG emissions in Austin, Texas. Transportation
research part A: policy and practice, 45(8), 707-720.
Peters, A., & Dütschke, E. (2014). How do Consumers Perceive Electric Vehicles? A
Comparison of German Consumer Groups. Journal of Environmental Policy & Planning,
16(3), 359-377. doi:10.1080/1523908X.2013.879037
Plötz, P., Schneider, U., Globisch, J., & Dütschke, E. (2014). Who will buy electric vehicles?
Identifying early adopters in Germany. Transportation research part A: policy and practice,
67, 96-109.
Qian, L., & Soopramanien, D. (2011). Heterogeneous consumer preferences for alternative fuel
cars in China. Transportation Research Part D: Transport and Environment, 16(8), 607-613.
Ramalingam, K., & Indulkar, C. S. (2015). Overview of Plug-in Electric Vehicle Technologies.
In S. Rajakaruna, F. Shahnia, & A. Ghosh (Eds.), Plug In Electric Vehicles in Smart Grids:
Integration Techniques (pp. 1-32). Singapore: Springer Singapore.
Rasouli, S., & Timmermans, H. (2016). Influence of social networks on latent choice of electric
cars: a mixed logit specification using experimental design data. Networks and Spatial
Economics, 16(1), 99-130.
Rogers, E. M. (2010). Diffusion of innovations: Simon and Schuster.
Sang, Y.-N., & Bekhet, H. A. (2015). Modelling electric vehicle usage intentions: an empirical
study in Malaysia. Journal of Cleaner Production, 92, 75-83.
Schneidereit, T., Franke, T., Günther, M., & Krems, J. F. (2015). Does range matter? Exploring
perceptions of electric vehicles with and without a range extender among potential early
adopters in Germany. Energy Research & Social Science, 8, 198-206.
Schuitema, G., Anable, J., Skippon, S., & Kinnear, N. (2013). The role of instrumental, hedonic
and symbolic attributes in the intention to adopt electric vehicles. Transportation research
part A: policy and practice, 48, 39-49. doi:http://dx.doi.org/10.1016/j.tra.2012.10.004
Shin, H.-S., Farkas, A., & Nickkar, A. (2019). A Spatial Analysis of Commuting Trips of Electric
Vehicle Drivers: The Case of Maryland. Paper presented at the Transportation Research
Board 98th Annual Meeting, Washington DC, United States.
International Conference on Transportation and Development 2019
Downloaded from ascelibrary.org by University of Melbourne on 09/06/19. Copyright ASCE. For personal use only; all rights reserved.
International Conference on Transportation and Development 2019 90
© ASCE
Sierzchula, W., Bakker, S., Maat, K., & van Wee, B. (2014). The influence of financial
incentives and other socio-economic factors on electric vehicle adoption. Energy Policy, 68,
183-194.
Zhang, Y., Yu, Y., & Zou, B. (2011). Analyzing public awareness and acceptance of alternative
fuel vehicles in China: The case of EV. Energy Policy, 39(11), 7015-7024.
International Conference on Transportation and Development 2019
Downloaded from ascelibrary.org by University of Melbourne on 09/06/19. Copyright ASCE. For personal use only; all rights reserved.
