IEEE Access 2015-000125

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Abstract— Plug-in hybrid electric vehicles (PHEVs) offer the

potential to significantly reduce greenhouse gas emissions, if

vehicle consumers are willing to adopt this new technology.

Consequently, there is much interest in exploring PHEV market

penetration models. In prior work, we developed an agent-based

model (ABM) of potential PHEV consumer adoption that

incorporated several spatial, social, and media influences to

identify nonlinear interactions among potential leverage points

that may impact PHEV market penetration. In developing that

model, the need for additional data to properly inform both the

decision-making rules and agent initialization became apparent.

To address these issues, we recently conducted and analyzed an

extensive consumer survey; in this work, we modify the ABM to

reflect the survey findings. A unique aspect is a one-to-one

correspondence between agents in the model and survey

respondents, thus yielding distributions and cross-correlations in

agent attributes that accurately reflect the survey population. We

also implement a used-PHEV market, and allow agents to

purchase new or used compact PHEVs or vehicles of their

current type. Based on our prior survey response analysis, our

modified model includes a PHEV-technology threshold

component, a multinomial logistic prediction of willingness to

consider a compact PHEV based on dynamically-changing

attitudes, and agent-specific delay discounting functions that

predict the amount agents are willing to pay up front for greater

fuel savings. We thus independently account for agents’

discomfort with the new PHEV technology, their desire to drive a

more environmentally friendly vehicle, and their willingness to

pay a higher sticker price for a PHEV. Results of 10 survey-based

ABM scenarios are reported with implications for policy-makers

and manufacturers. We believe close integration of the design of

consumer surveys and the development of ABMs is a key step in

This paper was submitted on 4/14/2015. This work was funded in part by

the United States Department of Transportation through the University of Vermont Transportation Research Center and a workforce development sub-

award from Sandia National Laboratories supported by the U.S. Dept. of

Energy through Inter-Entity Work Order M610000767. M.J. Eppstein is with the Department of Computer Science at the

University of Vermont, Burlington, VT 05405 (email:

Maggie.Eppstein@uvm.edu). D.M. Rizzo is with the School of Engineering, University of Vermont,

Burlington, VT 05405 (email: Donna.Rizzo@uvm.edu).

B.H.Y. Lee is with the School of Engineering, University of Vermont, Burlington, VT 05405 (email: bhylee@uvm.edu).

J.H.Krupa was with the School of Engineering, University of Vermont, but

is now with Roberge Associates Coastal Engineers, LLC, Stratford, CT 06615 (email: joseph.krupa@gmail.com).

N. Manukyan was with the Department of Computer Science, University

of Vermont, but is now with Faraday, Inc., Burlington, VT 05401.

developing useful decision-support models; this work serves as an

example of one way to achieve that.

Index Terms— Plug-in hybrid electric vehicles (PHEVs);

agent-based model; market penetration; electric vehicle

adoption; vehicle choice simulation, vehicle choice survey.

I. INTRODUCTION

urrently, transportation accounts for approximately one

third of greenhouse gas (GHG) emissions in the U.S., and

is its fastest growing source [1]. Plug-in hybrid electric

vehicles (PHEVs) offer the potential to significantly reduce

GHG emissions [2]-[4]. The degree to which this can occur

depends on current regional electric power generation sources

[5][6], smart grid technologies to improve grid efficiency and

reduce peak demands [7]-[9], and power generation shifts

from coal to cleaner sources, including natural gas and

renewables (e.g., wind, solar) [10][11]. Potential vehicle-to-

grid technologies could further reduce peak electricity

demands [12][13] and, therefore, have a positive feedback on

the grid efficiency. In addition, PHEVs have projected

lifecycle costs that are lower than internal combustion engines

[14]. From the consumer perspective, PHEVs can provide

large savings in fuel costs without the range limitations of all-

electric vehicles (EVs); the EPA/DOT sticker data for the

2013 Chevy Volt states that the vehicle “will save $6,850 in

fuel costs over 5 years compared to the average new vehicle.”

Realization of these potential PHEV benefits ultimately

depends on consumers’ willingness to adopt this new

technology. Although public awareness of PHEVs is growing

[15], consumers continue to have concerns regarding the new

battery technologies in electric drive vehicles (EVs, PHEVs,

and hybrid-electric vehicles) and the potential inconvenience

of recharging [16]-[22]. While previous work indicates

consumers are willing to pay a premium for greater fuel

efficiency [22][23], initial Chevy Volt sales have fallen short

of projections [24], and are outcompeted by the otherwise

similar gas-powered Chevy Cruze [25].

Consequently, there is significant interest in modeling the

potential market penetration of PHEVs (and other electric-

drive vehicles) under a variety of scenarios (e.g., [26]-[36]; for

a recent review see [37]) to understand the impacts of

potential policies, incentives, energy prices, and other factors

affecting consumer vehicle choice. However, despite this

progress, there exists a need for greater micro-data to properly

inform such models [30][38].

Using National Survey Respondents as

Consumers in an Agent-Based Model of Plug-In

Hybrid Vehicle Adoption

Margaret J. Eppstein, Donna M. Rizzo, Brian H.Y. Lee,

Joseph S. Krupa, and Narine Manukyan

C

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For example, in prior work [30] we developed an agent-

based model (ABM) of potential PHEV consumer adoption

that incorporated several spatial and social effects, including

threshold effects [39], homophily [40], and conformity

[41][42], as well as media influences, to identify nonlinear

interactions among potential leverage points that may impact

PHEV market penetration. In that work, the need for

additional data to properly inform both the agent decision-

making rules and agent initialization became apparent.

To address these issues, in a joint collaboration between the

University of Vermont and Sandia National Laboratories, we

performed an extensive online survey of 1,000 adult U.S.

residents using the Amazon Mechanical Turk (AMT) crowd-

sourcing platform [43] in July, 2011. AMT has emerged as a

reliable and relatively inexpensive survey tool [44]-[48]. Our

AMT survey, designed in large part to specifically inform and

improve the ABM first presented in [30], comprised 105

questions regarding consumer demographics, purchasing

decisions, factors influencing vehicle choice (including

PHEVs), attitudes toward the environment and energy, and

discounting questions that assessed how much a respondent

would be willing to pay up front for a vehicle that would

provide various levels of future benefits. After extensive

quality control (described in [49]), 911 out of the 1000

responses were deemed reliable and included in subsequent

analyses. Survey respondents were fairly representative of

national demographics (e.g., see Table 1 and Fig. 1 in [49]).

For example, the number of participants that responded from

47 states across the U.S. was proportional to each state’s 2010

census population (R2=0.91), and the distribution of vehicle

number per household was very similar to national data [50].

Although many survey respondents came from households

with multiple vehicles (they reported an average of 1.7

vehicles per household) and possibly multiple drivers, they

were directed to answer questions regarding their personal

driving habits and the vehicle (past, present, or future)

available for their primary use. Other than household income,

questions on demographics and attitudes related to the survey

participant, not the household. To minimize participation bias,

we specifically ordered the questions so that participants were

not aware that the survey was related to vehicle purchasing

attitudes until the third of six sections, and the PHEV focus

was not revealed until the fifth section.

Our AMT survey yielded several useful results. For

example, more AMT participants (86%) stated that financial

benefits due to fuel savings would be very important in

considering the purchase of a PHEV than those (55%) who

stated that reducing greenhouse gas emissions would be very

important; this is consistent with [17][51][52], who found that

most consumers prioritize financial benefits over

environmental benefits. However, our survey analysis also

showed that those who are most concerned about climate

change had 44.4 times greater odds of being willing to

consider purchasing a PHEV than those least concerned,

whereas those who felt that fuel savings were very important

had only 15.25 times greater odds to consider purchasing a

PHEV than those who ranked this as relatively unimportant.

Like other recent studies that found strong correlations

between pro-environmental attitudes and stated preferences

for alternative fuel vehicles [53][54], this implies that

environmental benefits may actually be a more powerful

motivator for PHEV adoption than financial benefits, for those

who place high importance on environmental concerns. These

findings provide motivation for properly modeling

dynamically changing environmental attitudes when

predicting PHEV market penetration. The complete survey

and all participant-level survey responses are available online

[55]. See [49] for distributions and correlations between

consumer attributes and attitudes, and an in-depth statistical

analysis on several factors we found most influential and

predictive of consumer willingness to consider PHEVs.

In this work, we used the AMT survey results to improve

the ABM decision-making rules for potential PHEV adoption,

and we populate the ABM with 911 agents, each based

directly on the individual responses of one of the 911 survey

participants. ABMs are typically initialized by drawing from

statistical distributions of attributes, informed by survey data

where known. However, data on cross-correlation of many

attributes are rarely available and, even if known, it is

challenging to create random populations that respect

distributions and cross-correlations between all attributes. By

assigning each agent with the attributes of an actual consumer,

distributions and cross-correlations of agent attributes

accurately reflect those in the survey population. We use the

modified ABM to study potential PHEV market penetration

under various scenarios with different incentives, fuel costs

and model assumptions, to illustrate how the improved model

can provide useful insights to policy-makers and

manufacturers. This close integration of consumer survey

design, analysis, and agent-based modeling is an important

advance in creating useful decision-support models for

planning in transportation and other fields.

II. AGENT-BASED MODEL

We adapted the ABM of [30] to closely reflect the findings

of the PHEV survey [49], both in terms of agent attributes and

agent vehicle-purchasing decision options and rules. In this

ABM, agents are models of vehicle consumers. To clearly

distinguish when we are referring to simulated vehicle

consumers in the model and actual vehicle consumers in the

AMT survey, we generally refer to the former simply as

“agents” and the latter as “AMT participants or respondents”.

Readers are referred to [30] for many of the details and

rationale behind the original ABM. Below, we focus on

relevant modifications to this model based on the AMT

survey.

A. Agent Initialization

We built a population of 911 agents directly modeled on the

911 survey respondents, thus obtaining data-driven

distributions and cross-correlations of all agent attributes.

After quality control, 3.8% of the 911 surveys had a few

missing values (specifically, for the attributes used here, there

were 31 respondents with 1 missing attribute, 3 respondents

with 2 missing attributes, and 1 respondent with 4 missing

attributes). Rather than discard these 35 surveys, we opted to

replace the missing values with the median response category

for that question. Most of the survey responses were ordinal or

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categorical. For survey responses selected from ordinal bins,

agents were assigned real values drawn from a uniform

random distribution over the selected answer’s bin range to

obtain more continuous distributions of attributes. For daily

per capita vehicle miles traveled (VMT), the bin ranges were

skewed slightly higher than those specified in the survey so

that the resulting distribution more closely matched U.S.

averages [50]. We felt the latter was important in

determining the distance agents might drive in the all-

electric range of a PHEV.

Each agent in the ABM has several associated

demographic attributes modeled directly after survey

responses of exactly one AMT participant, including

individual age, annual household income, residential

location (rural, suburban, urban), and location in the

political spectrum (11% far left, 32% left, 33% center, 19%

right, 4% far right). Each agent also has vehicle attributes

modeled after the responses of its corresponding AMT

participant, including:

(i) The typical number of years of car ownership;

(ii) Personal Daily VMT;

(iii) Age of the vehicle currently available for their primary

use;

(iv) Current vehicle class reported as either compact (24.1%),

midsize (29.5%), full-size (10.3%), minivan or SUV

(20.1%), full-size van or pickup (5.5%). There were an

additional 10.5% reported as no vehicle, motorcycle, or

other; rather than discard these surveys, we assigned them

to the compact vehicle class;

(v) The manufacturer suggested retail price (MSRP) of their

current vehicle;

(vi) The fuel type (in this study, recorded only as PHEV or

non-PHEV), all-electric range (if any) and miles per

gallon (MPG) when not in all-electric mode; and

(vii) Whether they purchased their most recent vehicle new

(32%) or used (60%); of the remaining 8% who said they

had never owned a vehicle, we assigned 4% to new and

4% to used.

Rather than asking AMT survey participants to self-report

the original MSRP and fuel efficiency of their current vehicles

(which we thought would likely be highly unreliable), we

approximated the values for vehicle MSRP and MPG from the

database located at www.cars.com, based on AMT

respondents’ self-reported make, model, and year. MSRP was

averaged over all styles brought to 2011 U.S. dollar

equivalents, assuming 3% annual inflation. MPG was

averaged over all driving modes. See Table I for initial agent

population means for several of the survey-based variables.

In addition, agents have survey-based attitudinal

characteristics representing their levels of concern regarding

GHG emissions, U.S. energy independence, transportation

fuel costs, the importance of projecting one’s environmental

image through vehicle choice, and the potential inconvenience

of recharging. All of these attitudinal characteristics (except

environmental image) are subject to change through social

and/or media influences. We inferred the degree to which

agents are susceptible to media influences (6% low, 39%

medium, 56% high) and social influences (59% low, 33%

medium, 9% high), based on survey responses to a series of

questions asking to what degree various factors had influenced

their current opinions regarding U.S. transportation energy

usage.

Agents were assigned a PHEV-comfort threshold T based

on survey participants’ responses to the question, “Assuming

you were buying a new vehicle, what is your comfort level in

purchasing/leasing the new PHEV technology rather than a

more established type of vehicle, assuming it had all the other

features you desired and was within your budget?” The

participants responded by selecting their comfort threshold,

phrased as a percentage range of PHEVs they would have to

see around them before they would be comfortable

considering a PHEV. The distribution of responses in each

range was similar to that of an identically binned Gaussian

distribution with a mean of 25% and a standard deviation of

48% [49], as shown in Fig. 1. In our initial agent population,

32% had a 0% threshold (innovators), another 4% had a

threshold 0 < T ≤ 5% (early majority adopters), and only 7%

would never consider a PHEV.

Agents were assigned a separate probability of “willingness

to consider a compact PHEV” attribute (W) reflecting whether

survey respondents stated they “would definitely” (28%),

“might” (51%), or “would not” (21%) consider purchasing a

compact PHEV, respectively. Agents corresponding to the

“would definitely” group were assigned W = 1, but for the

remainder we dynamically estimated this probability every

time step, based on a multinomial logistic model described in

the next section.

Finally, survey respondents indicated how much they would

be willing to pay up front for a vehicle (not necessarily a

PHEV) that achieves 6 different specified dollar amounts in

fuel savings per year. These delay discounting responses were

associated with individual agents and used to interpolate the

maximum price premium agents would pay for PHEVs to

achieve estimated annual fuel savings based on projected

gasoline and electricity prices over the agent’s expected years

of vehicle ownership and daily VMT.

TABLE I

MEAN VALUES OF SEVERAL SURVEY-BASED VARIABLES IN

THE INITIAL POPULAITON OF 911 AGENTS.

Agent Attribute Mean Value

Household Income $54,231

Age 34 years

Daily VMT 43.5 km

(27 mi)

Age of Initial Primary

Vehicle

8.5 years

Expected duration of car

ownership

7.0 years

Fuel Efficiency of Initial

Primary Vehicle

10.2 km/L

(24 mpg)

MSRP of Initial Primary

Vehicle

$28,585

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Fig. 2. Synthesized spatial correlations across the 38.9 km2 (15 mi2) simulated domain in normalized values of (a) household income, where red is highest

income and blue is lowest, (b) education level, where red is highest education and blue is lowest, and (c) political leaning, where red indicates far right and

blue indicates far left; (d) agent residence locations (white dots) are generated to be sparse in the rural region (red background), have intermediate spacing in

the suburban region (green background), and are dense in the urban region (blue background).

Fig. 1. Histogram of AMT survey responses of self-reported PHEV comfort threshold ranges, superimposed with a truncated normal distribution binned the

same way.

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All attributes described so far were, for each agent, based

directly on survey responses of one AMT participant, thus

respecting the distributions and cross-correlations of these

attributes as observed in the survey population. However,

since survey respondents came from 47 states across the U.S.,

and given our desire to model spatial and social effects of

consumers in a contiguous geographical region, we

synthesized agent locations in a simulated 38.9 km2 (15 mi2)

domain. To do this, we used 10 iterations of a Kohonen self-

organizing map (SOM) [56] to locate the 911 agents in space

based on the normalized values of 4 attributes. This was done

to generate spatial correlations in household incomes (Fig. 2a),

education levels (Fig. 2b), political leanings (Fig. 2c), and

appropriate spacing and grouping of agents based on

respondents from urban, suburban, and rural regions (Fig. 2d).

The resulting spatial correlations (Fig. 3a) do not alter the

observed cross-correlations between these 4 attributes (Fig.

3b), since individual agents were mapped to various locations

without changing their attributes.

Each agent computes a perceived PHEV market share given

vehicles owned by agents in its individual “spatial

neighborhood”. We defined the latter to include all agents

within each agent’s daily VMT/4, based on the assumption that

consumers who drive more perceive more vehicles. The

resulting spatial neighborhoods had a minimum of 31 agents, a

median of 476 agents, and a maximum of 910 agents.

Similarly, social influences were computed within an agent’s

“social neighborhood,” defined to include all agents in its

spatial neighborhood that have annual household incomes

within ±$10,000 and age within ±15 years of its own attribute

values, based on the principle of homophily [40]. The

resulting social neighborhoods had a minimum of 5 agents, a

median of 49 agents, and a maximum of 190 agents.

B. Agent Decision-Making

During each simulated annual time step, all agents are updated

asynchronously in random order. Prior to making any vehicle

purchasing decisions, certain agent attitudes are updated

subject to both external and internal influences. Specifically,

concerns regarding GHG emissions G and U.S. energy

independence E are updated by adding the product of the

average annual change in media coverage conveying the need

to reduce transportation energy and the agent’s susceptibility

to media influence SM. In our simulations, the level of media

coverage was stochastically increased from 0.05 to 0.3 over

the 14 years of the simulation, based on the assumption that

the increase in energy and climate-related impacts will be

reflected by an increase in media coverage. Similarly, concern

regarding fuel costs F is updated by adding the average annual

change in gasoline prices times SM, where gasoline prices are

stochastically increased from $0.911/L ($3.45/gal; the 2011

U.S. average) to various projected values in 2040 (EIA, 2013).

Electricity prices are assumed to increase linearly from

$0.099/kWh in 2011 to $0.108/kWh in 2040 [58]. Four of the

attitude attributes (political leaning, concerns regarding GHG

emission, concerns regarding U.S. energy independence, and

concerns regarding the potential inconvenience of recharging)

are also modified through social influences, subject to the

attitudes of other agents in their social network and their level

of social susceptibility using the social update procedure

described in [30].

Following its attitude updates, an agent probabilistically

decides whether to consider purchasing a new vehicle during

the current year, based on the age of its current vehicle and a

normal probability distribution centered on the agent-specific

number of years the agent expects to own the vehicle. The

agent then estimates the expected annual fuel-cost savings of a

PHEV relative to a vehicle of its current type over the

expected number of years of vehicle ownership, based on its

daily VMT and the projected gasoline and electricity cost (this

Fig. 3. (a) Spatial correlation (Pearson) of household income (I), highest education (E), and political leaning (P) as a function of distance; and (b) all pairwise

cross-correlations (Pearson) between these three attributes and region (R), as observed in the AMT survey.

I,E I,P I,R E,P E,R P,R-0.15

-0.1

-0.05

0

0.05

0.1

0.15

0.2

0.25

attribute pairs

cro

ss-c

orr

ela

tio

n

0 0.5 1 1.5 20.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

neighborhood size

sp

atia

l co

rre

latio

n

household income

highest education

political leaning

(a) (b)

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rational computation assumes that consumers will take

advantage of the increasing number of web tools (e.g.,

[59][60]) that provide easy access to this information), and the

maximum expected annual cost of ownership of these two

vehicle types, including loan payments after trading in their

current vehicle. The assumptions regarding vehicle financing

and depreciation are described in [30].

The agent next selects between five vehicle purchasing

options in the following order: (1) a new compact PHEV

similar to a Chevy Volt (with a pre-incentive sticker price of

$40,000, an all-electric range of 61 km (38 mi), an average of

16 km/L (38 mpg) when running on gasoline, and a 16 kWh

battery), (2) a new vehicle similar in type to its current

vehicle, (3) a used compact PHEV similar to a Chevy Volt

(that is not as old as its current vehicle), (4) a used vehicle of

the same type as its current vehicle (but not as old), or (5) no

vehicle purchase. We selected the Chevy Volt as

representative of a first-generation PHEV targeted toward the

general public and with a relatively high all-electric range,

sufficient for the U.S. average daily VMT of 47 km (29 mi)

[50].

Part of the agent decision-making process depends on

whether the agent is even willing to consider purchasing a

compact PHEV; this is considered true if either the agent has a

willingness value of W = 1 or if a uniform random number is

less than the probability predicted by a multinomial logistic

model estimating its likelihood of seriously considering a

compact PHEV, based in part on its dynamically changing

attitudes. Specifically, we used multiple ordinal logistic

regression to build a predictive model of survey respondents

who reported they “would definitely” or “would not” consider

a compact PHEV based on 7 attributes: the agent’s political

leaning, their attitudes regarding the environment and energy,

their concerns regarding fuel costs and recharging, their desire

to project an environmental image through vehicle ownership,

and their current vehicle class, the latter reflecting their

willingness to potentially switch vehicle classes to purchase a

compact PHEV (since most first generation PHEVs are

compact vehicles). In our previous PHEV survey analysis

[49], these 7 attributes were the most predictive set we found;

five-fold cross-validation yielded an average of 80% positive

predictive value when testing the survey data. The logistic

prediction was incorporated into agent decision-making as

described below.

An agent will purchase a new compact PHEV if all of the

following are true:

(i) The price premium of a new PHEV (less any available

rebates) is within the agent’s willingness to pay extra

(linearly interpolated from their delay discounting

attributes based the amount of fuel costs they expect to

save annually),

(ii) The maximum annual cost of ownership is affordable

(defined as ≤ 20% of the annual household income, a

common rule of thumb [61]),

(iii) The agent is willing to consider a compact PHEV

(estimated from the logistic function as described above),

(iv) The agent is willing to consider buying a new car, and

(v) The number of PHEVs owned by agents within its spatial

neighborhood exceeds the agent’s threshold T (except in

Scenario 2 where we did not use the threshold).

If the agent does not purchase a new PHEV, it then decides

whether to purchase a new vehicle similar in type to its current

vehicle based on whether (a) the agent is willing to consider

buying a new car, and (b) the vehicle is affordable.

If the agent does not purchase a new vehicle, it then

assesses whether to purchase a used vehicle, either a compact

PHEV or a conventional vehicle of the same type as its current

vehicle. We assume that used vehicles of the current vehicle

type are available for all years under consideration, and the

agent considers the most recent used model that is affordable

(based on the vehicle depreciation formula in [30]). For used

compact PHEVs, we implement a used PHEV market; we

track which PHEV model years have been traded in and allow

only these years to be purchased as used PHEVs. The agent

considers the most recent used PHEV that is both available

and affordable.

The agent will purchase the used PHEV if all of the

following are true:

(i) The age of the used PHEV is less than the age of its

current vehicle,

(ii) The difference in price between the used PHEV and the

used vehicle of its current type under consideration falls

within the agent’s willingness to pay extra,

(iii) The agent is willing to consider a compact PHEV, and

(iv) The number of PHEVs owned by agents within its spatial

neighborhood exceeds its threshold T (except in Scenario

2 where we did not use the threshold).

If the agent does not purchase a used PHEV, it will

purchase the used vehicle similar in type to its current vehicle,

provided the vehicle age is less than the age of its current

vehicle. Otherwise, the agent does not buy any vehicle and

retains its current vehicle during this time step.

III. RESULTS

We report on 10 scenarios after 14 years of simulation,

starting from 0% compact PHEVs in 2011 to projected market

penetration in 2025 (Fig. 4, Table II). Similar to the findings

in [30], we did not observe qualitative differences in the

results of different stochastic runs (as evidenced here by the

relative smoothness of the curves in Fig. 4), so individual runs

of each of the 10 scenarios were used to illustrate the relative

model sensitivities to the different parameters in these

scenarios. Since, on average, agents consider buying a vehicle

every 7 years, this simulation period corresponds to

approximately 2 vehicle purchases per agent. In the base case

scenario, we assumed all agents will consider purchasing a

new car, agent thresholds T and initial vehicle classes are as

reported by survey participants; gas prices stochastically trend

upward toward the Annual Energy Outlook 2013 reference

gasoline price projection [58] of $1.14/L ($4.32/gal) in 2040

(in 2011 dollars); and we assume that PHEV incentives exist

for the duration of the 14-year simulation in the form of a

$7,500 federal tax rebate (as currently exists in the U.S.), a

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$4,000 manufacturer rebate (as existed for the Chevy Volt in

June, 2013), and a $1,500 state tax rebate (as currently exists

in California). Each of the remaining scenarios varies in

exactly one parameter, as follows. Scenario 2 omitted the use

of the threshold T in the agent decision-making process.

Scenario 3 assumed that agents who had purchased their most

recent vehicle used would not consider purchasing their next

vehicle new. Scenario 4 assumed that all agents were compact

Fig. 4. Market penetration of compact PHEVs for all 10 scenarios described in Table II.

TABLE II.

PERCENTABLE OF COMPACT PHEV’S IN THE SIMULATED FLEET AFTER 14 YEARS OF SIMULATION (STARTING FROM 0% IN 2011

THROUGH 2025) FOR EACH OF THE 10 SCENARIOS DESCRIBED IN SECTION III AND ILLUATRATED IN FIG. 4. VALUES IN BOLD

DIFFER FROM THE BASE CASE.

Scenario Use Thres-

hold T?

All will

consider

new cars?

All initially

compact

car owners?

Predicted

gasoline price

in 2040

Rebate

amounts

(Federal +Manuf.

+State)

Rebate

duration

% compact

PHEVs in

2025

% change

compared to

Base case

1) Base case Yes Yes No $1.14/L

($4.32/gal)

$7,500

+$4,000

+$1,500

NA 17.8% NA

2) No

threshold No Yes No $1.14/L

($4.32/gal)

$7,500

+$4,000

+$1,500

NA 33.9% +16.1%

3) Some used-only buyers

Yes No No $1.14/L ($4.32/gal)

$7,500 +$4,000

+$1,500

NA 8.0% -9.8%

4) All compact

buyers

Yes Yes Yes $1.14/L ($4.32/gal)

$7,500 +$4,000

+$1,500

NA 21.2% +3.4%

5) High gas

prices

Yes Yes No $1.64/L

($6.19/gal)

$7,500

+$4,000 +$1,500

NA 18.6% +0.8%

6) Very high

gas prices

Yes Yes No $2.11/L

($8.00/gal)

$7,500

+$4,000 +$1,500

NA 19.6% +1.8%

7) No state

rebate

Yes Yes No $1.14/L

($4.32/gal)

$7,500

+$4,000 +$0

NA 14.8% -3.0%

8) No state or

manufacturer

rebate

Yes Yes No $1.14/L

($4.32/gal)

$7,500

+$0

+$0

NA 9.9% -7.9%

9) No rebates Yes Yes No $1.14/L

($4.32/gal) $0

+$0

+$0

NA 5.3% -12.5%

10) Rebates end after 5

years

Yes Yes No $1.14/L ($4.32/gal)

$7,500 +$4,000

+$1,500

5 yr 12.7% -5.1%

IEEE Access 2015-000125

8

car owners (as in the scenarios reported in [30]). Scenario 5

assumed a more rapid rise in gasoline prices based on a high

gasoline price projection [58]. Scenario 6 assumed an even

higher rise in gasoline prices (e.g., such as might occur with

an increase in gasoline tax). Scenario 7 assumed there was no

state rebate. Scenario 8 assumed no state rebate and no

manufacturer rebate; and Scenario 9 assumed no PHEV

rebates at all. Scenario 10 assumed that all rebates were

terminated after 5 years. PHEV market penetration for the 10

scenarios is shown in Fig. 4, with details summarized in Table

II.

IV. DISCUSSION

Market penetration of PHEVs (and other EVs) is still very

low and highly variable across the 50 U.S. states, ranging in

2013 from a low of essentially 0% of new car registrations in

Mississippi to a high of 1.6% in Washington and Hawaii [62].

Thus, proper model validation is impossible with such low

numbers, especially considering that agents are based on

survey respondents from 47 states. Furthermore, our ABM is

intentionally relatively simple; for example, we do not attempt

to model other types of EVs (or any other vehicles not owned

by our 911 survey respondents) or make any attempt to

account for future unknown changes in the technology or how

this might impact PHEV price or consumer willingness to

adopt. Our intent with this ABM is not to make accurate or

quantitative predictions, but rather to better understand

potential barriers and leverage points that may impact PHEV

market penetration by improving our originally proposed

PHEV model [30] using agent decision-making rules and

initialization based on data from the recently published PHEV

survey [49].

Since 911 surveys passed our quality-control standards, we

elected to limit our simulated populations to exactly 911

agents with a one-to-one correspondence between survey

respondents and agents, thus preserving distributions and

cross-correlations of respondent attributes. One could

conceivably scale-up this model using multiple copies of these

911 agents in different spatial locations. However, in our

previous work [30], we found no qualitative differences

between simulations with 10,000 agents and those with 1,000

agents, or between different stochastic runs with the same

parameters. Thus, for this study, we judged that 911 agents

were sufficient to identify trends and sensitivities. Ideally, the

model could be scaled up by conducting a larger survey within

a limited geographical region of interest; however, given our

choice to perform the survey using AMT, we could not limit

the geography of respondents beyond specifying they must be

within the U.S.

Of the parameters tested, the PHEV technology comfort

threshold T, reflecting consumer uneasiness with the new

PHEV technology, is shown to be the largest barrier to

potential PHEV market penetration, depressing potential

PHEV sales by nearly 50% in 14 years of simulation (Fig. 4

and Table II, compare Scenario 2 to Scenario 1). Note that our

implementation of this social thresholding effect is motivated

by the classic works of [39] and [62]. In [64] the authors took

an alternative approach by discretizing time into 3 sequential

phases and assigning innovators and early adopters to the first

phase (which assumed 0.1% EV market penetration), early

majority adopters to the second phase (5% EVs), and late

majority and traditionalists to the third phase (10% EVs). In

contrast, our implementation assigned percentage thresholds to

agents within the self-reported ranges of their corresponding

AMT survey respondents. Thus, agents naturally considered

whether to purchase a PHEV after market penetration had

exceeded their personal comfort threshold and the same agent

could make more than one vehicle purchase during the course

of a simulation. Although many of the survey-based agents

were willing to be early adopters of the PHEV technology,

after 14 years the total market penetration only exceeded the

thresholds for 47% of the agents in Scenario 1. In these

simulations, we assumed the heterogeneous agent-specific

threshold values were static; it is possible that as PHEV

technology becomes more widely advertised and familiar to

consumers, these threshold values may decline over time.

Furthermore, we did not model PHEV adoption by

commercial operators, such as taxis, company vehicles, etc. It

is possible that PHEVs may make more rapid inroads into

these commercial fleets and thus impact the market

penetration level perceived by consumers. Certainly, our

results indicate that aggressive steps are needed by

manufacturers (e.g., through educational advertising and

strong battery warrantees) and policy-makers (through public

service announcements, development of public recharging

infrastructure and policies, and through well-publicized

incentives) to help consumers feel more comfortable with the

PHEV technology.

If the PHEV market penetration exceeded an agents’

personal comfort threshold and the multinomial logistic model

predicted that the agent would seriously consider purchasing a

PHEV, we then assessed whether they were willing to pay the

PHEV sticker price, assuming it was within their budget.

Rather than inferring an agent’s willingness to pay extra for a

more fuel efficient vehicle from indirect indicators (e.g., as in

[64]), we used the individual delay discounting functions self-

reported by survey respondents. Thus, our model

independently accounts for agents’ discomfort with the new

PHEV technology, their desire to drive a more

environmentally friendly vehicle, and their willingness to pay

a higher sticker price for a PHEV.

When no consumer agents were willing to switch from

used-car buyers to new-car buyers, PHEV purchases dropped

by 55% (Fig. 4 and Table II, compare Scenario 3 to Scenario

1). Since PHEVs are likely to be scarce in the used-car market

for some time (and consumers may be even more

uncomfortable purchasing a used PHEV, due to a fear of

expected degradation in the battery capacity), and since

economic pressures are causing some U.S. consumers to

switch from new car buyers to used car buyers [65], this will

be a major impediment to increasing the proportion of PHEVs

in the U.S. light-vehicle fleet.

Interestingly, when we increased the number of agents who

initially owned compact cars from 35% to 100% (Fig. 4 and

Table II, compare Scenario 4 to Scenario 1), compact PHEV

market penetration only increased by 3.4% over 14 years. This

is because our agents were modeled after our survey

population, of which a surprising 18% of the survey

respondents indicated they would definitely, and another 39%

IEEE Access 2015-000125

9

stated that they might, consider switching car classes to

purchase a compact PHEV. On the one hand, these results

indicate the limited number of car classes in first generation

PHEVs may not, in itself, be a large barrier to PHEV market

penetration. On the other hand, they also imply that future

availability of PHEVs in more vehicle classes may not have a

huge impact on overall PHEV market penetration.

While we elected to use known attributes of the Chevy Volt

as representative of available PHEV technology throughout

each simulation, one could easily incorporate hypothetical

improvements in PHEV technology over time (e.g., as in [64])

and/or increased availability of PHEV models over time.

However, unless such changes succeed in dramatically

lowering the PHEV comfort thresholds of many consumers

(something we can only guess at), our results indicate that they

may have relatively little impact on predicted PHEV market

penetration. Thus, since we have no way of knowing what

those future changes might be or how they may affect

consumer purchasing decisions, we judged that adding such

assumptions (which are not founded on data) into the model

would introduce unnecessary complexity. We also assumed a

constant 2011 vehicle price throughout the simulations, a

standard assumption in engineering economics. In reality, one

would hope that as PHEV rebates are phased out there will be

a reduction in the MSRP of PHEVs relative to conventional

vehicles due to improvements in battery technology and

manufacturing and economy of scale.

Large increases in gasoline prices (Fig. 4 and Table II,

compare Scenarios 5 and 6 to Scenario 1) had surprisingly

small effects on PHEV market penetration in these

simulations. However, our model assumed that vehicle

consumers used rational computations of fuel costs savings

(such as those now easily obtained through various websites).

In reality, many consumers do not base vehicle purchasing

decisions on financially accurate assessments of alternatives

[23], so a more rapid rise in gasoline prices and/or higher

gasoline taxes, and associated press coverage, may cause a

disproportionately high psychological effect that stimulates

PHEV adoption at a greater rate than rational fuel cost savings

would warrant.

Our simulations illustrate the importance of governmental

and manufacturer rebates in making PHEVs competitive (Fig.

4 and Table II, compare Scenarios 7-10 to Scenario 1). Again,

we assumed that agents took all rebates into account when

making vehicle purchasing decisions. However, even when

these rebates are in place, many consumers may be unaware of

them, particularly manufacturer rebates (that change

frequently, vary with consumer credit ratings, and are often

poorly advertised), and state rebates (which many consumers

may not be familiar with). If PHEVs are to successfully

penetrate the U.S. transportation market to a large extent, it

may be necessary to continue and widely advertise such

rebates until the MSRP of PHEVs can become more cost-

competitive, unless improvements in PHEV technology and

consumer education efforts are successful in significantly

reducing consumers’ PHEV comfort thresholds.

V. CONCLUSIONS

Our prior PHEV market penetration work [30] left us

frustrated with the lack of sufficient data to properly inform

agent vehicle-purchasing decision rules or populate the ABM

with realistic distributions and cross-correlations of agent

attributes. For example, although we employed the well-

established sociological Gaussian distribution to model the

technology adoption lifecycle [66], we could not find data to

properly estimate the mean and standard deviation of this

distribution for new PHEV technology consumer adoption.

We also could not find quantitative data to help predict which

vehicle consumers were likely to seriously consider a PHEV,

especially if this meant switching vehicle class to obtain a first

generation PHEV. Additionally, even if consumers were

willing to consider purchasing a PHEV, we found little

information to help quantitatively predict how much extra they

might be willing to spend up front to obtain greater fuel

savings in the future.

To address these issues, we recently conducted and

analyzed an extensive consumer survey designed, in large

part, to specifically inform our PHEV market penetration

ABM [49]. In the current study, we modified our ABM to

reflect the survey results. Rather than generating a synthetic

population based on the statistical distributions and cross-

correlations found in the survey (a non-trivial task because of

the multitude of cross-correlated and non-linearly related

variables), we created an agent population with a one-to-one

correspondence between survey respondents and agents, with

each agent’s attributes based as directly as possible on the

responses of one survey participant, including a variety of

attitudes and susceptibility to social and media influences.

Survey questions were carefully phrased to tease out different

aspects of potential PHEV adoption barriers, including fear of

new PHEV technology independent of other vehicle features

or cost (and where consumers saw themselves on the PHEV

technology adoption lifecycle), willingness to pay more

upfront to achieve various savings in fuel costs down the road

(independent of the fuel type), attitudes regarding the

environment and energy, stated willingness to consider a first

generation PHEV with the understanding that they would only

come in compact models, etc. We then redesigned the ABM to

independently include these complementary aspects into agent

vehicle purchasing decisions. Our revised model includes a

PHEV-technology comfort threshold component, a previously

validated multinomial logistic prediction of willingness to

consider a compact PHEV [49], based on consumer attitudes

including environmental and energy concerns (which could

dynamically change in our model, subject to social and media

influences), and agent-specific delay discounting functions

that predict the amount agents are willing to pay up front for

greater fuel savings.

Some of the survey responses were contrary to our original

assumptions. For example, it is worth noting that, while our

survey results confirmed that the comfort threshold T could

reasonably be modeled with a truncated Gaussian distribution

(Fig. 1), the observed 48% standard deviation of this

distribution was much larger than the 20% standard deviation

assumed in our previous work [30]. The higher standard

deviation reflects many potential early adopters, but also

IEEE Access 2015-000125

10

implies that adoption rates will remain slow since many

consumer may not consider a PHEV until market penetration

is already quite high. Cross-correlations in many attributes

(e.g., Fig. 3b) were also lower than initially assumed in [30].

In this work we have avoided the necessity to make

assumptions on these distributions and cross-correlations by

modeling the attributes of individual agents on those reported

by individual survey respondents.

We report insights from several simulated scenarios. Our

results indicate the fear of new PHEV technology remains a

large barrier to widespread PHEV adoption among new car

buyers. To date, there has been relatively little advertising or

media attention designed to educate or allay consumer fears

regarding this technology. Since a growing majority of U.S.

vehicle purchasers elect to buy used rather than new cars, this

will greatly limit the rate of PHEV market penetration, since

PHEVs are not likely to become a large part of the used-car

market in the near future. Surprisingly, our agent population

was relatively insensitive to our restricting available PHEVs to

compact cars and was also relatively insensitive to rising

gasoline prices. However, our simulations confirm that

governmental and manufacturer rebates will remain necessary

to make PHEVs competitive until the sticker price of these

vehicles comes down.

ABM predictions will always be subject to a wide range of

uncertainties associated with various assumptions.

Nonetheless, since manufacturers and policy-makers are

required to make decisions in the face of such uncertainties,

models can be useful decision support tools, if adequately

grounded in data [67]. We believe that close integration of

consumer surveys and the design of agent-based models is a

key step in the development of useful decision-support models

in the transportation research community and beyond.

In their recent review of electric-drive vehicle market

penetration models, [37] recommend that there needs to be a

greater connection between consumer surveys and electric-

drive vehicle adoption rate modeling. This study illustrates

how one can make that connection.

ACKNOWLEDGEMENTS

This work was funded in part by the United States Department

of Transportation through the University of Vermont

Transportation Research Center and a workforce development

sub-award from Sandia National Laboratories supported by

the U.S. Dept. of Energy through Inter-Entity Work Order

M610000767. We thank D. Brad Lanute, Diann E. Gaalema,

Kiran Lakkaraju, and Christina E. Warrender for their

participation in the PHEV survey data collection. We also

thank cars.com (www.cars.com) for allowing us to

automatically scrape their website for MPG and MSRP values.

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IEEE Access 2015-000125

12

Margaret J. Eppstein is Professor and

Chair of the Department of Computer

Science at the University of Vermont.

She received the B.S. degree in zoology

from Michigan State University in

1978, and the M.S. degree in computer

science and the Ph.D. degree in

environmental engineering from the

University of Vermont in 1983 and

1997, respectively. She was the founding Director of the

Vermont Complex Systems Center from 2006–2010 and

became Chair of Computer Science in 2012. Her research

interests involve forward and inverse modeling and analysis of

complex systems in a wide variety of application domains,

including biological, environmental, technological, and social

systems.

Donna M. Rizzo, Professor in the

School of Engineering, holds

undergraduate degrees in Civil

Engineering from the University of

Connecticut, Fine Arts from the

University of Florence, Italy and a M.S.

and Ph.D. from the University of

California, Irvine, and University of

Vermont, respectively. Her research

focuses on the development of new

computational tools to improve the understanding of human-

induced changes on natural systems and the way we make

decisions about natural resources.

Brian H.Y. Lee is an Assistant Professor in Transportation

Systems at the University of Vermont; his primary

appointment is in the School of Engineering, Civil and

Environmental Engineering Program, and his secondary

appointment is in the Transportation Research Center. He

received his Doctorate degree from the

University of Washington in the

Interdisciplinary Ph.D. Program in Urban

Design and Planning in 2009, his M.S.

degree from Northwestern University in

Civil and Environmental Engineering in

2003, and his Bachelor of Applied

Science degree from the University of

British Columbia in Civil Engineering in

2001. He is an applied researcher with expertise in

transportation and land use analysis and empirical modeling.

He has keen interests in multimodal transport and a record of

interdisciplinary collaborations on policy-relevant projects.

Joseph S. Krupa earned his B.S. in Civil

Engineering and his MS in Civil &

Environmental Engineering at the

University of Vermont in 2009 and 2013,

respectively. He was a Municipal Civil

Engineer for the City of New Haven, CT,

from 2013-2014; since 2014 he is an

Engineer at Roberge Associates Coastal

Engineers, LLC, in Stratford, CT.

Narine Manukyan received a BS in

Applied Mathematics and Informatics at

Yerevan State University in Armenia in

2008, MS in Computer Science at the

University of Vermont in 2011, and PhD

in Computer Science at the University of

Vermont in 2014. Her research interests

include artificial intelligence,

evolutionary computation, complex

networks, data mining, machine learning

and modeling complex systems. Starting in 2015, she became

Lead Data Scientist at Faraday, Inc., Burlington, VT.