Modeling electric vehicle adoption considering a latent travel pattern construct and charging infrastructure

Transportation Research Part D 72 (2019) 65–82

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Transportation Research Part D journal homepage: www.elsevier.com/locate/trd

Modeling electric vehicle adoption considering a latent travel pattern construct and charging infrastructure

T

Fatemeh Nazaria, , Abolfazl (Kouros) Mohammadiana, Thomas Stephensb ⁎

a b

Department of Civil and Materials Engineering, University of Illinois at Chicago, USA Argonne National Laboratory, USA

ARTICLE INFO

ABSTRACT

Keywords: Battery electric vehicle Plug-in hybrid electric vehicle Latent travel pattern Charging infrastructure Revealed preferences

This paper presents a behavioral model of public, revealed preferences (RP) for various types of electric vehicles (EVs) while accounting for a latent (green) travel pattern construct and charging infrastructure characteristics. Specifically, a two-level nested logit (NL) model is estimated to explain households’ fuel type choice among five alternatives and three nests: (1) battery electric vehicles (BEVs); (2) hybrid vehicles including hybrid electric vehicles (HEVs) and plug-in HEVs (PHEVs); and (3) conventional vehicles including gasoline and diesel vehicles. Further, a latent travel pattern construct which captures a week-long number of trips by non-vehicle travel modes as well as daily vehicle and tollway use is estimated in a structural equation setting and subsequently fed into the NL model. Using a recent RP dataset from the California Household Travel Survey, we identify market segments for alternative fuel types based on households’ socio-economic characteristics, built environment factors concerning public plug-in EV (PEV) charging infrastructure characteristics, latent and observable travel behavior factors of a household vehicle’s principal driver, and vehicle attributes. The results highlight that the number of public PEV charging stations is only significant for households choosing PHEVs and interestingly insignificant in the BEV utility. Furthermore, the sensitivity analysis of the findings reveals that PHEV users are elastic with respect to household vehicle ownership ratio and the latent green travel pattern construct, while BEV users are inelastic to any explanatory variable.

1. Introduction The U.S. is responsible for 20% of the world petroleum consumption, of which 19% is imported (Davis et al., 2018). With a share of 70% in 2017, the transportation sector is the major petroleum consumer in the U.S. (US DOE, Energy Information Administration, 2018). Transportation also accounts for 28% of the U.S. greenhouse gas (GHG) emissions (US EPA, 2018), 60% of which is contributed by light-duty vehicles. In view of the role of transportation and light-duty vehicles in reliance on oil imports and the associated consequences in energy security, climate change, and public health, policy makers, vehicle manufacturers, and technology developers are interested in advancing alternative fuel technologies such as electric vehicles (EVs). Initial attempts in the early 2000s introduced hybrid electric vehicles (HEVs), which combust liquid fuels (e.g., gasoline) while also recapturing the lost energy during braking into batteries. Released in 2010, plug-in electric vehicles (PEVs) draw energy from an electricity grid. PEVs include both plug-in hybrid electric vehicles (PHEVs), which use both liquid fuels and electricity, and battery electric vehicles (BEVs), which use only electricity.

⁎

Corresponding author. E-mail addresses: [email protected] (F. Nazari), [email protected] (A.K. Mohammadian), [email protected] (T. Stephens).

https://doi.org/10.1016/j.trd.2019.04.010

1361-9209/ © 2019 Elsevier Ltd. All rights reserved.

Transportation Research Part D 72 (2019) 65–82

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Realization of significant EV1 benefits requires its mass acceptance and adoption by the public. Despite the growing PEV stock in the U.S. since 2010 —with a greater BEV uptake than that of PHEV— and surpassing 0.6 million vehicles threshold in 2017 and 1.0 million in 2018 (US DOE, Office of Energy Efficiency & Renewable Energy, 2018a), the scale achieved so far in the U.S. is very small as evidenced by a PEV sales share of 1.65% of light-duty vehicles in 2018 (European Alternative Fuels Observatory, 2018; Auto Alliance, 2018). To enhance the context-dependent EV adoption in the U.S., one should develop a disaggregate model on Americans’ EV adoption behavior using a detailed dataset collected in a sufficiently large and heterogeneous region of the U.S. A disaggregatelevel model in fact brings the capability to characterize EV users by their socio-economic characteristics, demographic factors, attitudes, and attributes of their vehicles. Furthermore, these models are a tool for better assessing the effectiveness of polices aimed at removing barriers to PEV adoption2. As detailed in Section 2, there exist two main gaps in the disaggregate-level models of EV adoption, which are rare use of revealed preferences (RP) datasets and the lack of distinguishing PHEV users from BEV users. Motivated by these gaps, this empirical study makes an attempt to more realistically model public adoption of EVs3 using an RP dataset collected in the U.S. while distinguishing between PHEV and BEV users. Specifically, a nested logit (NL) model of households’ vehicle fuel type choice is estimated with a choice set consisting of five alternatives: gasoline vehicle, diesel vehicle, HEV, PHEV, and BEV. The explanatory factors include households’ socio-economic characteristics, factors of their surrounding built environment, and (alternative-specific) vehicle attributes. Besides, as a vehicle in a household is mostly driven by one of its members (i.e., the principal driver of the vehicle), the model also accounts for travel behavior factors of the principal drivers. In particular, we consider the principal drivers’ attitude toward green travel pattern through a latent construct, which is built based on the observed responses to attitudinal/perceptual questions of the survey (McFadden, 1986; Koppelman and Hauser, 1978; Ben-Akiva et al., 2002). We estimated the above model using the most recent database of California Household Travel Survey collected in 2012–2013 (California Department of Transportation, 2018). The results show that the individuals’ latent green travel pattern is mostly defined by bike trips while having a vehicle trip negatively affects this attitude. We found potential PEV adopters among high income (≥ $200 K), households with more educated members (bachelor’s degree or higher), and larger household vehicle ownership ratio (number of vehicles per adults). Furthermore, they probably reside in non-apartment houses which are close to level 2 charging stations. We also found the number of public PEV charging stations only significant for households choosing PHEVs and interestingly insignificant in the BEV utility. Finally, the sensitivity analysis of the findings revealed that the PHEV users are elastic with respect to the household vehicle ownership ratio and the latent green travel pattern construct, while the BEV users are inelastic to any explanatory variable. Further studies on Californians’ EV adoption behavior and usage could be found in Ji et al. (2015), Tal and Xing (2017), and Turrentine et al. (2018). The rest of the paper is organized as follows. The relevant literature is reviewed in Section 2. Section 3 presents the methodology. The dataset is statistically described in Section 4 followed by the estimation results and the sensitivity analysis in Sections 5 and 6. The paper concludes with the discussion of the findings in the last section. 2. Literature review Broadly speaking, disaggregate-level studies on EV adoption behavior view the problem from either a psychological or an economic perspective.4 The psychological studies reason that EV adoption behavior depends on individual-specific psychological constructs such as perceptions, attitudes, and emotions which are drawn from the related questions of questionnaire (Schuitema et al., 2013). The majority of these studies use stated preferences (SP) datasets as in Axsen et al. (2012) and Moons and De Pelsmacker (2012) among others described in a thorough review by Rezvani et al. (2015). A drawback of these studies is the neglect of other fuel type options (e.g., gasoline and diesel), which limits interpretation and application of the findings. The economic approach complements the first method by modeling the tradeoff between various vehicle fuel types to describe decision-makers’ choice as a function of their characteristics and vehicle attributes (Liao et al. (2017) presents a comprehensive review of economic-based studies). The focus of this paper is on the latter approach. Table 1 characterizes the relevant studies that used SP datasets by model, EV type(s) included in choice set, input variable(s), and a summary of highlighted finding(s). The literature review identifies two main gaps in the existing disaggregate U.S. studies of EV adoption. First, due to the recent advent of EVs, especially PEVs, most of them use SP datasets. However, there may be discrepancies between choices indicated by SP datasets and people’s actual choice in the market that is referred to as “hypothetical bias” (Beck et al., 2016). To estimate a more realistic model, one should use a RP dataset, which describes EV adoption behavior rather than intention to adopt EVs. To our 1

Throughout the paper, EVs include HEVs, PHEVs, and BEVs. A review of these policies (such as incentives, laws, and regulations), which are set by the U.S. federal, state, and local agencies, could be found in Jin et al. (2014), Tal and Nicholas (2016), Hardman et al. (2017), Zambrano-Gutiérrez et al. (2018), and Stephens et al. (2018). 3 Note that the RP dataset used in this study dates back to a few years ago though it is the most recent database from the California Household Travel Survey collected in 2012–2013 (California Department of Transportation, 2018). Therefore, the estimated model might capture behavior of innovators and early adopters. To understand adoption behavior of a wider spectrum of EV adopters, future research may use more updated databases as they become available. 4 In addition, two other approaches have been used to model market penetration of EVs. The first approach uses agent-based simulation frameworks (e.g., Eppstein et al. (2011) and Querini and Benetto (2014)). The second approach investigates market penetration of EVs at aggregatelevel using diffusion rate and time-series models (e.g., Centrone et al. (2007) and McManus and Senter (2009); see Gnann et al. (2018) for a review of diffusion rate models). 2

66

67 BEV

HCM

NL

Barter et al. (2013), U.S.

PHEV, BEV

HEV, PHEV, BEV

HEV, PHEV, BEV

HEV, BEV

HEV, PHEV, BEV

HEV, BEV

MNP

MNL

Achtnicht et al. (2012), Germany

PHEV, BEV

Daziano and Achtnicht (2013), Germany Jensen et al. (2013), Denmark

NL

Lin and Greene (2011), U.S.

HEV, BEV

MXL

MNL, NL

Qian and Soopramanien (2011), China

HEV, PHEV

Hackbarth and Madlener (2013), Germany

MNL

Musti and Kockelman (2011), U.S.

HEV, BEV

MNP

MXL

Mabit and Fosgerau (2011), Denmark

BEV

Ziegler (2012), Germany

LCMNL

Hidrue et al. (2011), U.S.

HEV

Cross NL

NL

Potoglou and Kanaroglou (2007), Canada

EV types

Hess et al. (2012), U.S.

Model

Study, country

Vehicle attributes: purchase price and running cost Incentives: cash subsidy, free parking, and access to priority lane Socio-economic characteristics Charging infrastructure: fuel-saving benefits, range anxiety, willingness to pay for workplace, and public charging Vehicle attribute: purchase price, fuel cost, engine power, emissions, driving range, and mileage Charging infrastructure: fuel availability/Socio-economic characteristics Vehicle attributes: body type, costs, performance, and efficiency Charging infrastructure: fuel availability Incentives: access to HOV lane, free parking, and tax credit Socio-economic characteristics Vehicle attributes: purchase price, power, fuel costs, emissions, age, body type, horsepower, driving range, mileage, and driving vehicle to work Charging infrastructure: fuel availability Socio-economic characteristics and environmentally-friendly purchase Vehicle attributes: purchase price, fuel cost, emissions, driving range, refueling time, battery recharging time, and body size Charging infrastructure: fuel availability and equipping parking Incentives: tax exemption, free parking, and access to bus lane Socio-economic, travel characteristics, and environmental awareness Vehicle attributes: purchase price, fuel costs, power, and CO2 emissions Charging infrastructure: fuel availability Vehicle attributes: purchase price, fuel cost, driving range, age, emissions, top speed, battery stations, battery life, and body size Socio-economic characteristics, latent environment attitude, charging at work/city centers/larger train stations, and home-work distance Vehicle attributes: pace of technological development Incentives: ownership cost, oil price, and battery performance

Vehicle attributes: acceleration time, costs, refueling frequency, driving range, purchase price, and repair service Socio-economic characteristics Vehicle attribute: purchase price Socio-economic characteristics and residential location

Vehicle attribute: price, fuel/maintenance cost, acceleration, and pollution Charging infrastructure: fuel availability Incentives: purchase tax, parking fees, and access to high occupancy vehicle (HOV) lane/Socio-economic characteristics and travel behavior Vehicle attributes: price, fuel cost, driving range, charging time, pollution, and acceleration

Explanatory variables

Table 1 Overview of studies with economic approach on electric vehicle adoption behavior using stated preferences dataset.

(continued on next page)

Reduction of greenhouse gas (GHG) emissions cannot occur only by widespread EV adoption and it requires efficiency improvement of conventional vehicles.

Increasing fuel availability could increase greater than threefold increase in HEV and BEV market penetration. Women in households with more vehicles are interested in BEVs. Individual preferences change significantly after a real experience with a BEV in a household. Environmental concerns have a positive effect on BEV preference.

EVs (HEVs, PHEVs, and BEVs) are embraced by younger, well-educated, and environmentally aware persons with many trips. People are willing to pay for greater fuel economy, emission reduction, improved driving range, and enhanced charging infrastructure, as well as EV incentives.

Younger and environmentally aware persons have positive intention towards HEVs, PHEVs, and BEVs.

Incentives including access to HOV lane, free parking, and reduced purchase price are insignificant in preferences for HEVs, PHEVs, and BEVs while tax credit incentive affects the utility of HEVs, PHEVs, and BEVs.

Preference for HEVs and BEVs decreases with age. BEVs are unpopular among Germans, even with a significant expansion of public PEV charging infrastructure.

Senior persons probably select HEVs and PHEVs. Women are interested in HEVs. Urban residents prefer PHEVs. Households with more vehicles do not prefer PHEVs. HEV and BEV adopters are among women and young persons who live in a household with large size, children, high income, and more vehicles. All three incentives are insignificants in preference for HEVs and BEVs. Three types of recharge enhancement increase sales of PHEVs and BEVs.

Main concerns about BEVs are range anxiety, long charging time, and high purchase price. BEV battery cost must drop significantly before its mass market without subsidy. Tax reduction increases HEV and BEV market share to conventional vehicle level.

Incentives including parking fees and access to HOV lane do not affect preferences towards HEVs.

Key finding(s)

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HCM

MXL

MXL

MXL

MXL

Kim et al. (2014), the Netherlands

Hoen and Koetse (2014), the Netherlands

Tanaka et al. (2014), U.S. and Japan

Valeri and Danielis (2015), Italy

Helveston et al. (2015), U.S. and China Rasouli and Timmermans (2016), the Netherlands

68

MNP

NL

Higgins et al. (2017), Canada

Nazari et al. (2018a), the U.S.

HEV, PEV

HEV, PHEV, BEV

HEV, BEV

HEV, PHEV, BEV BEV

HEV, BEV

PHEV, BEV

HEV, PHEV, BEV

BEV

EV types

Vehicle attributes: capital costs, operating costs, cruising range, time to change the battery, and maximum speed Charging infrastructure: distance to charging station Socio-economic characteristics and social networks Vehicle attributes: purchase price, recharging range, size, and fuel economySocio-economic characteristics and energy (electricity and gas) price Vehicle attributes: cost, performance, charging characteristics, warranty, and vehicle attribute importance Incentives: cash, free parking, free toll road use, and access to HOV lane Socio-economic characteristics Charging infrastructure: accessibility to public PEV charging stations Socio-economic characteristics, built-environment characteristics, and travel attitude factors

Vehicle attributes: driving range, recharging time, additional detour time, and vehicle brand Incentives: free parking, tax exception, and access to bus/taxi lanes Vehicle attributes: purchase price, fuel cost, driving range, and emissions Charging infrastructure: PEV charging station availability Vehicle attributes: purchase price, operating cost, acceleration, and driving range Charging infrastructure: refueling distance Socio-economic characteristics and presence of long-distance trips Vehicle attributes: price, powertrain, brand, cost, and performance

Socio-economic characteristics, social influence, environmental concerns, and technology acceptance

Explanatory variables

Households with higher income and education are more interested in PEVs. Accessibility to PEV charging stations is a critical factor in choosing PEVs.

Highly educated women prefer HEVs whereas highly educated men are interested in BEVs. Young people more probably buy HEVs and BEVs, especially BEVs. PHEVs and BEVs are most attractive to households that are younger and highly educated. Those that care about fuel economy and reduced or zero emissions show much higher probabilities of selecting HEV, PHEV, and BEV options.

An increase in driving range does not increase BEV market share whereas a combination of changes such as introduction of a subsidy, decrease of purchase price, increase in battery range and fuel price increases BEV market share. Americans have low willingness-to-pay for BEVs and they prefer low-range PHEVs. Social network effects play a minor role on BEV adoption. BEV adopters are among persons with high income level.

Males and highly educated persons are interested in BEVs. Preference for a BEV is influenced by social influence, environmental concerns, and technology acceptance. Preference for an EV (HEV, PHEV, and BEV) increases with improving driving range, refueling time, and fuel availability. Increasing annual mileage leads to lower preference for EVs. PHEV and BEV consumers in the U.S. are sensitive to fuel cost reduction and public charging station availability.

Key finding(s)

MNL: multinomial logit, NL: nested logit, MNP: multinomial probit, MXL: mixed logit, LCMNL: latent class MNL, HCM: hybrid choice model. HEV: hybrid electric vehicle, PHEV: plug-in hybrid electric vehicle, BEV: battery electric vehicle, PEV: PHEV or BEV.

MXL

Cirillo et al. (2017), U.S.

MXL

Model

Study, country

Table 1 (continued)

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SEM with Latent variables Explanatory variables (individual-level) - Socio-economic characteristics

Indicators of green travel pattern (individual-level) - # walk trips - # bike trips - # public transit trips - Had a vehicle trip - Used toll for vehicle trip

Latent green travel pattern (of individuals)

Stage 1

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NL model of vehicle fuel type choice Utility

Stage 2

Explanatory variables (individual- and household-level) - Socio-economic characteristics - Built environment factors concerning charging infrastructure - Travel behavior of individuals (e.g., latent green travel pattern) - Vehicle attributes

Vehicle fuel type

Conventional vehicle (CV) Hybrid vehicle

BEV

Gasoline

BEV

Diesel

HEV

PHEV

Fig. 1. Modeling framework of vehicle fuel type choice with a latent variable.

knowledge, the only discrete choice-based study using RP data is Javid and Nejat (2017), who estimated a binary choice model to ascertain PEV versus non-PEV adoption behavior in the state of California. Second, most of these studies do not distinguish between the two types of PEVs, i.e., PHEVs and BEVs. Although both PHEVs and BEVs are charged by plugging to an electricity grid, they have several differences (e.g., charging requirements) and thus attract different consumers. This paper attempts to fill these gaps by estimating a NL model of vehicle fuel type which includes gasoline vehicles, diesel vehicles, HEVs, PHEVs, and BEVs using a RP database of the California Household Travel Survey collected in 2012–2013 (California Department of Transportation, 2018). 3. Methodology Mixed logit and NL models enhance multinomial logit models by relaxing the assumption of independence from irrelevant alternatives (IIAs). Mixed logit models further capture taste heterogeneity across individuals by assuming randomly distributed preference parameters, however, offer no explanation for the source of preference heterogeneity (McFadden and Train, 2000). To explain these sources, hybrid choice models (HCMs) introduce latent attitudinal and/or preferential constructs to discrete choice models by linking the latent constructs to observed indicators as well as explanatory variables (examples are Jensen et al. (2013), Vij and Walker (2016), and Nazari et al. (2018b)). HCMs further improve the choice model fit (Ashok et al., 2002; Raveau et al., 2010) and can be estimated either sequentially or simultaneously. In this paper, we estimate an HCM sequentially in two stages, where the first stage determines the latent green travel pattern construct and the second stage is a NL vehicle fuel type model. Fig. 1 delineates the suggested two-stage modeling framework. At stage 1, a structural equation model (SEM) with latent variables determines the latent green travel pattern of individuals using two sets of measurement and structural equations. The latent construct, from one side, is connected to the underlying observed indicators via the measurement equations shown by the dashed arrow in Fig. 1. These indicators include the number of trips by non-vehicle travel modes (i.e., walk, bike, and public transit) measured throughout one week, and daily use of vehicle and tollway. On the other side, the structural equation ties the latent construct with explanatory variables encompassing individuals’ socio-economic characteristics. Alternatively, one could directly use the observed attitudinal indicators in the NL model with an assumption that the indicators directly represent the underlying attitudes. However, this can cause a correlation between the indicators and the socio-economic characteristics and thus may lead to erroneous estimation results such as counter intuitive signs (a thorough discussion on approaches of treating latent constructs in choice models can be found in Bhat and Dubey (2014)). We avoid this issue by estimating the latent attitude of a vehicle’s principal driver using the SEM with latent variables. The estimated SEM with latent variables gives the expected value of the latent construct, i.e., green travel pattern of the principal drivers, to stage 2. At stage 2, an NL model describes vehicle fuel type choice (probability of owning a vehicle 69

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of a given fuel type) of the households as a function of their socio-economic characteristics, factors of their surrounding built environment, vehicle attributes, and travel behavior factors of principal drivers such as latent green travel pattern construct. From a theoretical point of view, the sequential estimation method could provide acceptable parameter estimates when the latent variables’ random error terms in the structural equations are small, since this would lead to sufficient reduction of the measurement errors for larger sample sizes (Ben-Akiva et al., 2002). The sample size of this research is relatively large and also the estimated variance of the structural equation’s error term is small (less than 1). Our choice of the sequential estimation method can be further justified by noting the empirical finding of Raveau et al. (2010), who showed a small improvement brought by the simultaneous approach compared to the sequential method, notwithstanding the higher computational burden. The rest of this section concisely presents the formulations of the SEM with latent variables (stage 1) and the NL model (stage 2) as delineated in Fig. 1. For brevity, we will suppress the index q {1, 2, , Q} for decision makers (i.e., individuals). In the SEM with latent variables, the connections among the observable attitudinal/preferential indicators and the underlying latent variables (the dashed arrow in stage 1 in Fig. 1) are characterized by the following measurement equations.

hr =

' rz

+

r

r

{1, 2,

(1)

, R} th

where r is the index for attitudinal/preferential indicator r {1, 2, , R} . hr signifies the r indicator variable. z is the L × 1 vector of latent variables z = (z1 , z2 , , zL )' and r is the associated L × 1 vector of latent variable loadings on the r th indicator variable. The error term of the r th indicator ( r ) captures the impact of unknown factors and is assumed to be standard normally distributed: [0, ], where indicates its covariance matrix. The model further ties the latent attitudinal/preferential variables to the observable explanatory variables, as shown by the solid arrow in stage 1 in Fig. 1, using the following set of linear equations.

z =

'

w +

{1, 2,

(2)

,L} th

{1, 2, , L } . z refers to the where denotes the index for latent variables latent variable. w is a D × 1 vector of exogenous variables for the th latent variable and is the corresponding D × 1 vector of coefficients. is a random error term of the th [0, z ], where z denotes its correlation matrix. equation and is assumed to be standard multivariate normally distributed: The SEM with latent variables is estimated using the maximum likelihood estimation method. Similar to the related studies (such as Johansson et al. (2006), Yáñez et al. (2010), Daziano and Barla (2012), Bahamonde-Birke and Ortúzar (2014), and Gao et al. (2017), among many others), the non-continuous, binary and count, indicators are treated as continuous to reduce computational burden. This assumption might reduce goodness-of-fit of the model (Bahamonde-Birke and Ortúzar, 2017). A solution to handle this concern is using the method of Satorra and Bentler (1994), which corrects the estimated standard errors and the model chi-square statistic. In the second stage of the modeling framework in Fig. 1, we estimate a well-established relaxation of MNL models (McFadden, 1973), i.e., NL models (Williams, 1977). The model describes the choice of the j th alternative from a choice set including J alternatives as [c1, , cJ ] with the associated utilities described by Eq. (3). Uj =

' j xj

+

' j zj

+

j

j

{1, 2,

,J}

(3) th

where xj and j denote the vector of exogenous variables in the utility equation of the j alternative and vector of the corresponding estimated coefficients. z j indicates the vector of expected values of the latent constructs for the j th alternative and j is the associated vector of the estimated coefficients. The random error term of the utility function of the j th alternative is shown as j with an assumed extreme value distribution. Suppose that J alternatives are placed in B subgroups (nests). The choice set can then be written as [c1, , cJ ] = [(c1|1, , c J1 |1), (c1|2, , c J2 |2), , (c1| B, , c JB |B )]. The unconditional probability of choosing the j th alternative from the bth nest can be written as Eq. (4). (4)

Pjb = Pj | b × Pb th

where the first term, i.e., the conditional probability Pj | b , is expressed as Eq. (5). The probability of being in the b nest is determined by the second term Pb , which is written as Eq. (6).

exp( j' xj +

Pj | b =

Pb =

Jb exp( j' xj j=1

' j zj

+

) ' j zj

)

(5)

exp( b IVb ) B exp( b IVb ) b= 1

(6) th

where IVb is the inclusive value for the b nest defined as Eq. (7). nest.

IVb = ln

Jb

exp( j' xj +

b

' j zj )

denotes the estimated coefficient of the inclusive value for the bth

(7)

j=1

The logarithm of the likelihood function over Q individuals is expressed by Eq. (8), which is maximized using full information 70

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maximum likelihood. Q

lnL =

ln[(Pj | b )q × (Pb )q]

(8)

q= 1

4. Data We used the most recent database from the California Household Travel Survey (2012–2013) administered by the California Department of Transportation (2018). The sample contains 39,250 households who own 73,973 vehicles with 51,365 adult (age > 16 years) individuals. The SEM with latent variables is estimated using individual-level data, which is statistically described in Section 4.1. The NL model of vehicle fuel type choice is estimated using household-level data, as described in Section 4.2. For the estimation of both models, we used individual- and household-level weights to align the sample distribution to the population distribution and avoid biases (see Yeager et al. (2011) for comparing weighted and unweighted estimates and Stuart (2010) for a review on methods for matching sample and population). The database provides weights based on a finite population sampling theory to avoid the biases associated with sampling and the robustness of the data collection. The weights are calculated by two methods including sampling weights and ranking adjustment (more description on the calculated weights are presented in the final report of the California Household Travel Survey (California Department of Transportation, 2013)). 4.1. Dataset for the SEM with the latent green travel pattern construct 4.1.1. Indicators of the latent green travel pattern construct (individuals) The individuals’ attitude towards green travel pattern is built on the role of travel modes in their trip profile using three continuous and two dummy indicators. The continuous indicators are the number of trips by non-vehicle travel modes, i.e., walk, bike, and public transit, during one week. Two dummy indicators relate to vehicle travel mode: whether a person had a vehicle trip in the day of data collection and whether (s)he used a toll road for the vehicle trip. The related statistical distribution is presented in Table 2 based on individuals’ access to a vehicle in a household, i.e., whether an individual is a principal driver of a vehicle in the household or not. As expected, mean and standard deviation (SD) of the number of trips by non-vehicle modes for the principal drivers are less than those of the non-principal drivers. On the day of data collection, the majority of the principal drivers (78.61%) used vehicle mode for at least one trip, whereas almost half of the non-principal drivers (47.88%) made at least one trip by vehicle mode. In addition, the share of toll use on the day of data collection is small (less than 3%) for both groups. 4.1.2. Socio-economic characteristics (individuals) Fig. 2 depicts distribution of socio-economic characteristics over the individuals. Males and females are almost equally distributed in the dataset with a less than 1% difference which is consistent with the distribution of gender in California (U.S. Census Bureau, 2017a). Almost half of the individuals are at mid-age (46 < age ≤ 66) and 39.65% of them are young (16 < age ≤ 46). Moreover, share of the seniors (age > 66) is 11.88% which is reasonably close to the 13.9% share of them in California (U.S. Census Bureau, 2017a). 4.2. Dataset for the NL model of vehicle fuel type 4.2.1. Socio-economic characteristics and built environment factors (households) Socio-economic characteristics of the households and the factors of their surrounding built environment are distributed as shown in Table 3. The majority of households (74.98%) do not have child/children (age < 16) which is compatible with the 22.9% share of persons under 18 years in California (U.S. Census Bureau, 2017a). On average, the households have 1.988 vehicles and 2.161 members with the corresponding SD values of 0.921 and 0.910, respectively. The largest non-white race or ethnic group in the U.S. and in the state of California is Hispanic (Stepler et al., 2016) and thus it is important to note the Hispanic origin of households in the Table 2 Sample data for observed indicators of the latent green travel pattern construct at individual-level. Indicators

Principal driver

Non-principal driver

Continuous indicators # walk trips # bike trips # public transit trips

Distribution Mean = 5.008, SD = 5.646 Mean = 0.697, SD = 1.910 Mean = 1.776, SD = 5.550

Distribution Mean = 5.268, SD = 6.029 Mean = 1.453, SD = 3.512 Mean = 2.617, SD = 5.218

Dummy indicators

Category

# observations

Share (%)

# observations

Share (%)

Had a vehicle trip

Yes No Yes No

34,697 9,443 1,213 42,927 44,140

78.61 21.39 2.75 97.25 85.93

3,459 3,766 66 7,159 7,225

47.88 52.12 0.91 99.09 14.07

Used toll for vehicle trip Sample size

71

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100

11.88

90 80

Share (%)

70

9.60

20.35 36.92

50.29 48.47

60

25.33 86.33

50

90.40

40 30 20

49.71

39.65

10 0

63.08

54.32

13.67

Gender Male Female

Age Young Mid-age Elderly

Education

Student

< Bachelor’s Bachelor’s ≥ Graduate

Yes No

Employment Driver's license Paid employed Not employed

Yes No

Fig. 2. Distribution of socio-economic characteristics at the individual level (sample size = 51,365). Table 3 Sample data for socio-economic characteristics and built environment factors at household-level. Variables Socio-economic characteristics Presence of a child # adults # vehicles Hispanic Income

# educated members # bachelor’s degree holders # graduate degree or higher holders Built environment factors Residential type

Charging level of nearest PEV charging station # PEV charging stations in residential CT Sample size

Category

# observations

Share (%)

Yes No Mean = 2.161, SD = 0.921 Mean = 1.988, SD = 0.910 Yes No < $25 K $25 K ≤ < $50 K $50 K ≤ < $75 K $75 K ≤ < $150 K $150 K ≤ < $200 K $200 K ≤ < $250 K ≥ $250 K

9,818 29,431 — — 9,018 30,232 5,572 9,738 6,707 12,111 2,684 1,169 1,269

25.01 74.98 — — 22.98 77.02 14.20 24.81 17.09 30.86 6.84 2.98 2.23

Mean = 0.519, SD = 0.701 Mean = 0.402, SD = 0.651

— —

— —

Single-family detached house Single-family attached house Apartment Other Level 1 (110 V) Level 2 (220 V) Mean = 2.912, SD = 4.379

30,645 2,611 4,770 1,224 36,478 2,772 — 39,250

78.08 6.65 12.15 3.12 92.94 7.06 — 100

dataset. The share of Hispanic households is 22.98%. The distribution of income over the dataset shows that almost half of the households (47.95%) earn between $50 K and $150 K annually. In addition, 56.1% of households earn less than $75 K which is somewhat higher than the median income of Californians (i.e., $67,169 according to U.S. Census Bureau (2017a)). Since PEV, especially BEV, users prefer to recharge their PEV at home (US DOE, Office of Energy Efficiency & Renewable Energy, 2018b), it is important to examine the impact of residential type on a household’s desire to purchase PEVs. Most of the houses in the dataset (78.08%) are single-family detached, while less than one fifth of the households live in single-family attached houses or apartments. In addition to recharging PEVs at home or work, there are public PEV charging stations. Fig. 3 shows the spatial distribution of the charging stations in the state of California. The charging level5 of nearest charging station to the households is the second built

5

See ChargerHub (2019), for more details on EV charging locations and levels. 72

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Fig. 3. Plug-in electric vehicle charging stations in the state of California. Source: United States Department of Energy (US DOE, Office of Energy Efficiency & Renewable Energy, 2018c) and United States Census Bureau (2017b). Table 4 Travel behavior factors of principal drivers. Variables

Category

# observations

Share (%)

User of carsharing services

Yes No Yes No –

495 64,581 2,325 62,751 65,076

0.76 99.24 3.57 96.43 –

Used HOV lane Sample size

environment factor (Table 3). Most of the households have access to level 1 (110 V) and 7.06% of them can access level 2 (220 V). Moreover, we calculated the number of charging stations for each census tract (CT) using Geographic Information Systems (GIS) to define the last factor as the number of charging stations in CT of the residential location of the households. The average value of this factors is 2.912 with SD 4.379. 4.2.2. Travel behavior factors (principal drivers) The dataset provides information on the principal drivers of households. If a vehicle is not assigned to any member of a household, which is the case for less than 10% of the observations, the head of household is considered as the principal driver. The first travel behavior factor of the principal drivers is the latent green travel pattern construct, which is the output of the SEM with latent variables. In addition, less than 1% of the principal drivers use carsharing services and a small share of them (3.57%) used HOV lane on the day of data collection (Table 4). 4.2.3. Vehicle attributes Each vehicle is defined by two attributes including drive wheel and ownership type, which are distributed as Fig. 4 with respect to vehicle fuel type. Drive wheel describes the configuration of vehicle drive wheels and includes front-, rear-, and four-wheel (which includes traditional four-wheel drive and all-wheel drive, Fig. 3(a)). More than half of the gasoline and diesel vehicles are driven by front and four wheels, respectively. Similarly, more than 80% of non-CVs are front-wheel drive. In addition, the share of four-wheel drive PHEVs and BEVs are trivial. Fig. 3(b) shows that most of the vehicles in the dataset are owned by the households. In addition, BEVs have the largest share of leased vehicles (24.72%). Sample size:

# gasoline vehicles = 68,342 # diesel vehicles = 2,221

73

# HEVs = 3,155 # PHEVs = 77 # BEVs = 178

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100 90

58.36

80

83.49

88.31

88.76

23.28

70

Share (%)

21.16

60 50 40 30 20 10 0

55.56 22.10 6.49 19.54

5.19

8.84 7.67

Gasoline vehicle Diesel vehicle Four-wheel

8.99 2.25

HEV

PHEV

Rear-wheel

BEV

Front-wheel

(a) Drive wheel 100 90 80

18.18

24.72

94.64

81.82

75.28

HEV

PHEV

BEV

1.22 2.31

2.39 1.53

1.01 4.34

96.48

96.08

Share (%)

70 60 50 40 30 20 10 0

Gasoline vehicle Diesel vehicle

Owned

Leased

Other

(b) Ownership type Fig. 4. Distribution of vehicle attributes with respect to vehicle fuel type.

For further analysis, Fig. 5 shows distribution of vehicle fuel type for households with one vehicle and more than one vehicle. As seen, 16.69% of 68,342 gasoline vehicles belong to households with one vehicle, while the remaining 83.31% gasoline vehicles are observed in households with more than one vehicle. In addition, 93.26% of BEV and 87.01% of PHEV holders are among households with more than one vehicle. 5. Results Before estimating the SEM with latent variables and the NL model, we used principal component analysis (PCA) for preliminary selection of the exogenous variables that have high correlation with the dependent variables and low correlation with each other. Then, we selected them for both models based on intuition and statistical significance. 5.1. Estimated SEM with latent variables Five measurement equations define the latent green travel pattern of individuals, which is also related to their socio-economic characteristics through a structural equation. The associated system of linear equations is solved to yield weight of the latent variable 74

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100 90

83.31

93.88

80.67

87.01

93.26

80

Share (%)

70 60 50 40 30 20 10 0

19.33

16.69

12.99

6.12 Gasoline vehicle Diesel vehicle # obs: 68,342 # obs: 2,221

HEV # obs: 3,155

PHEV # obs: 77

6.74 BEV # obs: 178

Household with the specified vehicle fuel type has > 1 vehicle Household with the specified vehicle fuel type has 1 vehicle Fig. 5. Distribution of vehicle fuel type for households with 1 and > 1 vehicle.

in each measurement equation and coefficients of the explanatory variables in the structural equation. The estimation results are shown in Table 5. To avoid the identification problem, the estimated coefficients are normalized between −1 and 1 and are found to be significant at 95% level of confidence. Overall, the estimated model fits the data well according to different goodness-of-fit (GFI) criteria: GFI index = 0.997 (> critical value of 0.9 based on Gao et al. (2017)), adjusted GFI = 0.988 (> critical value of 0.9 based on Gao et al. (2017)), standardized root mean square residuals (SRMR) = 0.021 (< critical value of 0.05 based on Byrne (2016)), and Table 5 Estimation results of the structural equation model with latent variables. Variables Measurement equations Indicators of the green travel pattern construct # walk trips # bike trips # public transit trips Had a vehicle trip Yes = 1 Used toll for vehicle trip Yes = 1 Structural equation Socio-economic characteristics Female Age Young (16 < age ≤ 45) Elderly (age > 65) Education level Bachelor’s or undergraduate degree Graduate degree Student Yes = 1 Paid employed Yes = 1 Hold driver’s license Yes = 1 Goodness-of-fit measures # observations Goodness-of-fit (GFI) index Adjusted GFI Standardized root mean square residual (SRMR) Root mean square of error approximation (RMSEA)

Coefficient

T-statistics

0.105 0.145 0.067

9.942 11.301 6.716

−0.713

−40.044

−0.132

−34.266

−0.054

−8.789

0.005 0.052

0.764 7.152

−0.036 −0.035

−5.690 −5.503

−0.067

−9.500

−0.198

–23.275

−0.337

–32.642

51,365 0.997 0.988 0.021 0.038

75

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the root mean square of error approximation (RMSEA) = 0.038 (< critical value of 0.05 based on Steiger (1990) and Browne and Cudeck (1992)). In addition, the model chi-square is significant at a p-value < 0.001 (Golob, 2003). Furthermore, we found a significant variance captured by the latent construct as evidenced by the estimated error component of the structural equation (Section 3, Eq. (2)), which is 0.792 and significant at 95% level of confidence. 5.1.1. Estimated measurement equations As shown in the first panel of Table 5, we found a positive weight of the latent variable on the number of trips by non-vehicle travel mode, i.e., walk, bike, and public transit. The largest positive coefficient belongs to bike mode, which reveals its highest impact on one person’s attitude towards a green travel pattern. In contrast, the latent construct is negatively signed in the equation of having vehicle trip and using toll. Comparing the absolute values of the estimated coefficients shows that one person’s green travel pattern is mostly determined by having a vehicle trip than other indicators. 5.1.2. Estimated structural equation Focusing on the second panel of Table 5, females are found to have a negative latent green travel pattern compared to males. It shows females’ higher reliance on private vehicles in their trips than on non-vehicle modes. Young adults (16 < age ≤ 46) and the elderly (age > 65) have a positive latent green travel pattern. The negative coefficient of the higher (≥Bachelor’s degree) education levels reveals the negative impact of education on the latent green travel pattern. Studentship is reflected by a dummy variable which equals 1 for students and 0 otherwise. Results indicate that students do not show a green travel pattern. Individuals’ latent green travel pattern is also a function of their employment status owing to the fact that a considerable share of daily trips are work and work-related trips (Santos et al., 2011). This factor enters the model, as a dummy variable with value 1 for paid employed persons and 0 for others, with a negative coefficient. This is probably caused by the fact that 9 out of 10 employees commute with private vehicles (US DOT, Bureau of Transportation Statistics, 2013). It can be concluded that employed persons follow their commute travel pattern in their non-commute trips. In other words, being employed not only adds two trips (each way of home-work trip counts as one commute trip) to daily trips of a person, but it also affects his/her overall travel pattern in a larger scope. The last factor is a dummy variable of holding a driver’s license, i.e., 1 for individuals with a driver’s license and 0 otherwise. As expected, this factor has a negative impact on the latent construct. 5.2. Estimated NL model of vehicle fuel type with a latent variable We tried different nesting structures and found the best one to be a two-level NL as depicted in Fig. 1. There are five fuel type alternatives in the lower level of the nesting structure: gasoline vehicle, diesel vehicle, HEV, PHEV, and BEV. At the upper level, gasoline and diesel vehicles are grouped as CVs. Vehicles that can use both electric and gasoline fuels, i.e., HEVs and PHEVs, are placed in the hybrid vehicles group. BEVs, which use only electric fuel, build the degenerated nest. In addition to consistency of the nesting structure with common sense, the estimation results (Table 6) also supports this as reflected in the statistically significant coefficients of inclusive values of the nests of CVs and hybrid vehicles which take values between 0 and 1 (note: inclusive value of the BEV nest as a degenerated nest is fixed at 1.00). As shown in Table 6, almost all estimated coefficients are significant at a 95% confidence level, and the model has an acceptable goodness-of-fit. The constant terms indicate that, assuming all conditions are equal, people prefer gasoline vehicles to other fuel types. They also have negative tendencies towards HEVs, BEVs, and PHEVs in an ascending order. 5.2.1. Effect of household socio-economic characteristics on EV adoption Impact of a household’s children on its vehicle fuel type choice appears as a dummy variable with value 1 for presence of a child in the household and 0 otherwise. This factor causes positive utility for both plug-in alternatives, especially BEVs. In contrast, households with child/children probably have a negative tendency towards gasoline vehicles and HEVs with less desire for HEVs. This behavior may be caused by technology awareness brought to the households by their child/children. For Chinese households, Qian and Soopramanien (2011) found a positive impact of children on households’ tendency towards HEVs and BEVs rather than petrol fueled vehicles. The estimated model signifies Hispanic origin as a dummy variable, which equals 1 for the households with Hispanic origin and 0 otherwise. The Hispanic households prefer gasoline vehicles to the other alternatives and their tendency towards BEVs is negative. This behavior may be due to lower median income of Hispanic households compared to Californians (California Senate Office of Research, 2017), which makes it difficult to afford the higher purchase price of BEVs. As expected, higher income levels (≥ $150 K) are positively signed in the utility functions of non-CV alternatives. However, the choice of high-income households among non-CV alternatives depends on the income level. Specifically, households who earn between $150 K and $200 K are more likely to choose HEVs than other alternatives. The most preferred non-CV option of households earning more than $200 K is PHEVs, with greater preference for households with income higher than $250 K. Households with the highest income level (≥ $250 K) probably select BEVs as the second option. This behavior may be caused by the limitations of BEVs, e.g., limited driving range, which degrades its utility to the second preferred alternative even for people with the highest income level. Similarly, Qian and Soopramanien (2011) found a positive influence of higher income levels on choosing HEVs and BEVs in China and Rasouli and Timmermans (2016) showed popularity of BEVs among Dutch persons with higher income. While number of a household’s vehicles can affect its decision on vehicle attributes, it is also important to consider number of potential drivers, i.e. adults, in the household. In fact, there is a difference in travel behavior and thus vehicle decisions of two 76

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Table 6 Estimation results of the nested logit model with a latent variable. Explanatory variables

Constant Socio-economic characteristics of household Presence of a child in household Yes = 1 Hispanic household Yes = 1 Household income $150 K ≤ < $200 K $200 K ≤ < $250 K ≥ $250 K Household vehicle ownership ratio Household with 1 vehicle Yes = 1 Education of household members # bachelor’s degree holders # graduate or higher degree holders Built environment factors Residential type Apartment (yes = 1) Charging level of nearest PEV charging station Level 2 (yes = 1) # PEV charging stations in residential CT Travel behavior factors of principal driver Latent green travel pattern User of carsharing services Yes = 1 Used HOV lane Yes = 1 Vehicle attributes Drive wheel Four-wheel drive (yes = 1) Rear-wheel drive (yes = 1) Ownership type Leased (yes = 1)

Gasoline vehicle

HEV

PHEV

BEV

Coef.

T-stat

Coef.

T-stat

Coef.

T-stat

Coef.

T-stat

4.379

43.87

−3.153

−14.108

−8.071

−9.687

−5.756

−8.226

−0.099

−1.70

−0.273

−5.40

0.333

1.19

0.670

2.58

0.637

8.26

—

—

—

—

−1.515

−2.55

— — — −0.252

— — — −6.14

0.452 — — 0.088

7.17 — — 1.41

— 0.628 1.348 0.816

— 1.51 4.05 2.68

— — 0.729 0.784

— — 2.29 3.06

0.564

5.78

—

—

—

—

−0.765

−1.08

— —

— —

0.038 —

1.34 —

0.438 0.274

2.37 1.35

0.151 0.587

0.83 3.21

—

—

−0.093

−1.24

−2.797

−1.63

−1.515

−1.38

— —

— —

— —

— —

0.775 0.035

1.93 1.93

2.442 —

9.39 —

−0.797

−6.51

−2.394

−13.55

−3.304

−2.83

0.173

0.21

−0.907

−4.04

—

—

—

—

2.008

3.69

—

—

—

—

0.602

1.28

1.409

3.89

−1.934 −1.018

−30.21 −14.23

−1.605 −1.252

−13.16 −13.90

−2.323 −1.984

−4.10 −3.45

−4.251 −1.124

−2.80 −2.74

0.410

2.11

—

—

—

—

0.583

1.89

Inclusive value CV = 0.190 (4.72) HEV = 0.639 (21.26) BEV = 1.000 (fixed) Goodness-of-fit measures # observations = 73,973 Log-likelihood with constants = -18,881.892 Log-likelihood at convergence = -17,614.796

Note: The base alternative is diesel vehicle

households with the same number of vehicles but different number of adults. A household who assigns one vehicle, or even more, to each of its adults exhibits a different travel behavior and in turn different vehicle decisions from a household with the same or smaller number of vehicles assigned to a larger number of adults. In view of this, we defined the variable household vehicle ownership ratio as the number of vehicles per each adult in a household and found it significant in the estimated model. Larger values of this variable indicate higher preferences for non-CVs, especially PEVs. Findings of a vehicle fuel type choice model in China also showed larger utilities of HEVs and BEVs for households with more vehicles (Qian and Soopramanien, 2011). In addition, BEVs are of interest to Danish households with more vehicles (Jensen et al., 2013). Furthermore, we found that households with more vehicles per adults have negative tendency towards gasoline vehicles. The next factor shows whether the household has one vehicle or more by a dummy variable. Households with positive answer to this question more likely prefer gasoline vehicles and less likely choose BEVs. This finding is in line with the previous studies (e.g., Qian and Soopramanien (2011), Ziegler (2012), Jensen et al. (2013), Helveston et al. (2015), and Cirillo et al. (2017)), which found BEVs in households with larger number of vehicles than households who only own one vehicle. Finally, household education is defined as the number of household members at each education level. The larger the number of educated (≥bachelor’s degree) members, the greater the probability of preferring non-CVs over other options. Furthermore, households with more highly educated (≥graduate degree) persons probably opt for BEVs. This variable can be a proxy of technology awareness and therefore we can expect more openness to non-CV alternatives among educated and highly educated households. 77

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These results are in line with the study of Cirillo et al. (2017) who found HEVs and BEVs among highly educated Americans. Furthermore, findings of studies in other countries verify that well-educated persons are attracted to EV alternatives. For instance, all three EV types are embraced by well-educated Germans (Hackbarth and Madlener, 2013). In addition, highly educated Dutch persons prefer BEVs to conventional vehicles (Kim et al., 2014). Finally, higher education levels increase Canadians’ preferences for PHEVs and BEVs (Higgins et al., 2017). 5.2.2. Effect of built environment factors on EV adoption Residential type is significant as a dummy variable with value 1 for apartments and 0 otherwise. The apartment residents are less inclined towards non-CVs, especially PEVs. This can be explained by noting that apartments may not necessarily provide garage or exclusive parking space to their residents, thereby making it impossible to install PEV charging equipment. This becomes more evident by considering that PEV users prefer to charge their vehicles at their homes according to the conclusions of a review study by Hardman et al. (2018) and the report of US DOE, Office of Energy Efficiency & Renewable Energy (2018b). Charging level of nearest PEV charging station to the households appears as a dummy variable with value 1 for charging level 2 and 0 otherwise. We found that access to charging level 2 increases the utility of both PEV alternatives. However, it is interesting to note that the estimated coefficient of BEVs is three times larger than that of PHEVs. This can be justified by considering the longer electric driving range of a BEV, which results in its longer recharging time than that of a PHEV. Based on this finding, one suggestion for relieving the concerns about charging PEVs can be equipping parking lots, especially at apartments, with fast-charging facilities. The last built environment factor is the number of PEV charging stations in CT of residential location of households (as defined in Section 4.2.1). Even though this variable may be expected to have a positive coefficient in the utility equations of both PEV alternatives, the estimated model signifies a positive coefficient only for PHEVs. In other words, households who live in neighborhoods with more charging stations prefer PHEVs to other alternatives. This variable, however, is interestingly not significant in the utility equation of BEVs. It may be further emphasized that the preferred charging location of BEV users is their houses similar to findings of Hardman et al. (2018) and US DOE, Office of Energy Efficiency & Renewable Energy (2018b). This can be explained by long recharging time of BEVs at charging stations regardless of the presence of high-voltage charging equipment. To tackle this, a remedy could be equipping charging stations with superfast charging systems or battery swaps, and/or subsidizing charging cost. Another possible explanation is that the existing charging stations are not well spatially distributed across the state of California in that their locations may not be conveniently accessible to BEV users. Most fast charging stations are located on highways, whereas stations built in cities, at shopping malls, and workplaces are usually lower-power (slower) charging stations. This problem can be addressed by efficiently locating future charging stations in the vicinity of BEV users’ neighborhoods. PEV charging infrastructure is also insignificant in the utility of BEVs for Germans in one study (Achtnicht et al., 2012) whereas another study found its positive influence on both PHEVs and BEVs (Tanaka et al., 2014). 5.2.3. Effect of principal drivers’ travel behavior factors on EV adoption The first factor is the latent green travel pattern construct. Those drivers with greener travel pattern in terms of using travel modes, have the least interest in both hybrid alternatives with a lower tendency towards PHEVs. This factor also has a negative impact on the utility of gasoline vehicles, however the absolute value of the associated coefficient is less than those of the hybrid vehicles. We also found a positive sign for this factor in the utility of BEVs, yet the estimated coefficient is not significant at an acceptable level of confidence. Overall, it can be concluded that persons who drive more by their vehicles (i.e., those with a less green travel pattern) are more inclined towards hybrid vehicles, especially PHEVs, and gasoline vehicles. By noting the socio-economic characteristics of the persons with a green travel pattern (Section 5.1), we can conclude that young or elderly males with lower education levels show less preference for PHEV, HEV, and gasoline vehicles in a descending order. The lower interest of young and elderly persons in PHEVs and HEVs may be due to their lower affordability considering their lower income level (Fontenot et al., 2018), compared to mid-aged persons, and high price of PHEVs and HEVs. Most of studies found that younger persons are interested in PHEVs and HEVs (Qian and Soopramanien, 2011; Achtnicht et al., 2012; Ziegler, 2012; Hackbarth and Madlener, 2013; Cirillo et al., 2017; Higgins et al., 2017), while Musti and Kockelman (2011) showed the tendency of senior Americans towards PHEVs and HEVs. Our findings on gender is compatible with the previous studies which showed the higher preferences of women for HEVs than men (Musti and Kockelman, 2011; Qian and Soopramanien, 2011; Cirillo et al., 2017). Moreover, the lower interest of low-educated persons in PHEVs and HEVs can be verified by the findings of other studies such as Cirillo et al. (2017), Hackbarth and Madlener (2013), and Higgins et al. (2017). The second factor concerns the use of carsharing services as a dummy variable that takes 1 for users of carsharing services and 0 otherwise. Persons answering positively to this question use shared vehicles by renting them for only a short period of time compared to continuously incurring vehicle ownership costs. The results show that such persons likely opt for BEVs and have a negative tendency towards gasoline vehicles. By noting the low market penetration of carsharing systems due to their recent emergence, in addition to the small number of carsharing members in the dataset (< 1% of the principal drivers, as noted in Section 4.2.2), it can be inferred that the higher interest in such new technologies may lead to more tendency towards BEVs. The last factor is defined by a dummy variable which equals 1 for HOV use and 0 otherwise. Those principal drivers who use HOV lanes more probably prefer plug-in alternatives, especially BEVs. This shows the effectiveness of the incentive policy of HOV lane access for EV users in the state of California (US DOE, Office of Energy Efficiency & Renewable Energy, 2018d). 5.2.4. Effect of vehicle attributes on EV adoption One of the distinctive features of non-CVs is the limited drive wheel options, which was shown to be significant in the estimated 78

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Table 7 Elasticity of explanatory variables of the nested logit model with a latent variable. Explanatory variables Socio-economic characteristics of household Presence of a child in household Yes = 1 Hispanic household Yes = 1 Household income $150 K ≤ < $200 K $200 K ≤ < $250 K ≥ $250 K Household vehicle ownership ratio Household with 1 vehicle Yes = 1 Education of household members # bachelor’s degree holders # graduate or higher degree holders Built environment factors Residential type Apartment (yes = 1) Charging level of nearest PEV charging station Level 2 (yes = 1) # PEV charging stations in residential CT Travel behavior factors of principal driver Latent green travel pattern User of carsharing services Yes = 1 Used HOV lane Yes = 1 Vehicle attributes Drive wheel Four-wheel drive (yes = 1) Rear-wheel drive (yes = 1) Ownership type Leased (yes = 1)

Gasoline

HEV

PHEV

BEV

−0.008

−0.058

−0.113

0.156

0.031

—

—

−0.315

— — — −0.086

0.026 — — 0.077

— 0.035 0.082 1.349

— — 0.022 0.727

0.020

—

—

−0.111

— —

0.017 —

0.391 0.183

0.074 0.211

—

−0.007

−0.379

−0.116

— —

— —

0.086 0.156

0.150 —

−0.111

−0.822

−2.297

0.066

−0.003

—

—

0.011

—

—

0.029

0.037

−0.170 −0.050

−0.257 −0.244

−0.727 −0.667

−0.747 −0.221

0.004

—

—

0.004

model using two related dummy variables: four- and rear-wheel drive. These two factors have negative signs in the utilities of all fuel types except for that of diesel vehicles. It means that if a decision-making unit decides on purchasing a four- or rear-wheel drive vehicle, the probabilities of choosing non-CVs and gasoline vehicles are less than that of diesel vehicles. Further, the vehicle ownership type appears as a dummy variable with value 1 for leased vehicles and 0 otherwise. Households who lease a vehicle prefer BEVs and gasoline vehicles to other fuel types. Their highest interest in BEV alternative may be due to its higher ownership cost, especially depreciation cost, than other alternatives (American Automobile Association, 2017; Guo and Zhou, 2019). This can also be verified by the observed share of leased BEVs in the dataset which has the largest share of leased vehicles with respect to fuel type. 6. Sensitivity analysis To further analyze the estimated model, we computed the direct elasticities of the alternatives with respect to the exogenous variables. The direct elasticity of an alternative with respect to an exogenous variable is defined as the percentage change in the choice probability of the alternative caused by a percentage change in the desired exogenous variable while keeping all other exogenous variables constant. The direct elasticities of CVs and hybrid vehicles are calculated according to Eq. (9) (Wen and Koppelman, 2001). For the degenerated alternative, i.e., BEVs, Eq. (10) computes the direct elasticity (Wen and Koppelman, 2001). Table 7 presents the values of the calculated direct elasticities.

(1

(1

Pjb) +

1 b

1 (1

Pj | b )

j xj

(9)

Pjb ) j xj

(10)

Among the socio-economic factors, household vehicle ownership ratio was found to cause the greatest elasticity. Specifically, PHEV alternative is elastic with respect to this variable, whereas other alternatives are inelastic with BEVs showing close to 1 (0.727) elasticity. This suggests that as the number of household vehicles for its potential drivers increases, one can expect a greater change in the probability of choosing PHEV adoption. Finding PHEVs among households with larger vehicle ownership ratio may be influenced by PEVs’ limited driving range and long recharging time. 79

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Looking into the built environment factors, we found that the probability of choosing PHEVs is inelastic to the number of public PEV charging stations. In other words, constructing more charging stations will likely have less influence on the PHEV adoption. This is also probably rooted in one of PEV drawbacks, i.e., long recharging time. It can be inferred that PEV users likely prefer to charge their vehicles at home overnight instead of spending their day time at a charging station in spite of the existence of fast-charging equipment at public stations. The largest elasticity among all variables belongs to that of the latent green travel pattern construct on PHEV adoption. Specifically, a percentage change in the latent green travel pattern construct reduces the probability of PHEV adoption by 2.297%. Recalling that a greener travel pattern highlights the role of non-vehicle travel modes for one person, we can expect more PHEV adoption for persons who rely more on their vehicles in their trips than on non-vehicle modes. Consequently, PHEV adoption could be more influenced by changing this factor than other ones. However, it should be noted that a person’s travel pattern is a habit and thus it is not an impulsive reaction of the person in a travel decision setting. Rather, it forms over time and therefore it takes a long time to influence an established behavior. Other vehicle fuel types are inelastic to this variable, however, with the corresponding elasticity for HEVs being close to 1 (-0.822). 7. Conclusions Advanced vehicle technologies such as electric vehicles (EVs), which include hybrid EVs (HEVs), plug-in HEVs (PHEVs), and battery EVs (BEVs), offer potential economic, environmental, and health benefits, but realization of these benefits requires considerable public EV adoption. However, the small market share of EVs in the U.S. calls for research on EV adoption behavior in order to identify current EV users based on their characteristics and attitudinal factors while also considering the competing alternatives, i.e., gasoline and diesel vehicles grouped as conventional vehicles (CVs). In light of this need, this paper investigates vehicle fuel type choice at the household-level by estimating a nested logit (NL) model. The model further accounts for individual-level characteristics by considering travel behavior of a household vehicle’s principal driver. Of note among these travel behavior factors is the latent (green) travel pattern of a person, which is obtained by estimating a structural equation model (SEM) with latent variables. Using the revealed preferences database of California Household Travel Survey (2012–2013), our empirical analysis shows that more biking leads to greener travel pattern while having a vehicle trip negatively affects it. Persons with a green travel pattern are mostly young or senior males with lower education levels. We also found that persons with a less green travel pattern probably prefer hybrid vehicles (HEVs and PHEVs) and also have propensity for gasoline vehicles, while this factor is insignificant in the utility equation of BEVs. By noting the above socio-economic characteristics of the persons with a green travel pattern, we can conclude that young or elderly males with lower education levels less prefer PHEVs, HEVs, and gasoline vehicles in a descending order. Moreover, users of carsharing services and high occupancy vehicle (HOV) lanes are more probably principal drivers of BEVs. We also noted that households with PEVs (both PHEVs and BEVs) are more likely to have a higher income level (≥ $200 K), more educated members (≥bachelor’s degree), and larger vehicle ownership ratio (number of vehicles per adults). Furthermore, they probably reside in nonapartment houses which are close to level 2 charging stations. The number of public PEV charging stations is significant only on choosing PHEVs and interestingly is insignificant in the utility of BEVs. Lastly, the sensitivity analysis of the findings revealed that PHEV users are elastic with respect to household vehicle ownership ratio and the latent green travel pattern construct, whereas BEV users are not elastic to any explanatory variable. In this study, we focused on vehicle fuel type choice of households who hold (own/lease) vehicles. One suggestion for extending this line of research is to include households without vehicle in the modeling framework by jointly estimating decisions of vehicle ownership (whether a household holds a vehicle or not) and vehicle fuel type. The joint model therefore could yield findings on the impact of travel pattern on the interest in holding a vehicle as well as choosing vehicle fuel type. Acknowledgement This work was funded by the Vehicle Technologies Office of the U.S. Department of Energy’s Office of Energy Office of Efficiency and Renewable Energy and performed under a collaborative effort of University of Illinois at Chicago and Argonne National Laboratory (Argonne). Argonne is a U.S. DOE laboratory managed by UChicago Argonne, LLC under contact DE-AC02-06CH11357. The authors are solely responsible for the findings of this research. The authors are grateful to the California Department of Transportation for data provision. 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