CARSHARING’S IMPACT ON HOUSEHOLD VEHICLE HOLDINGS:
RESULTS FROM A NORTH AMERICAN SHARED- USE VEHICLE SURVEY
Elliot Martin, PhD
Post- Doctoral Research Engineer, Transportation Sustainability Research Center
University of California, Berkeley
1301 S. 46th Street. Bldg 190, Richmond, CA 94804- 4648
510- 665- 3576 ( O); 510- 665- 2183 ( F)
Email: elliot@ berkeley. edu
Susan A. Shaheen, PhD
Research Associate, Mineta Institute
Honda Distinguished Scholar in Transportation, University of California, Davis, &
Acting Co- Director, Transportation Sustainability Research Center ( TSRC)
University of California, Berkeley
1301 S. 46th Street. Bldg 190, Richmond, CA 94804- 4648
510- 665- 3483 ( O); 510- 665- 2183 ( F)
Email: sashaheen@ tsrc. berkeley. edu; sashaheen@ ucdavis. edu
Jeffrey Lidicker, M. A., M. S.
Graduate Student Researcher, Transportation Sustainability Research Center
University of California, Berkeley
1301 S. 46th Street. Bldg 190, Richmond, CA 94804- 4648
510- 295- 4411 ( O); 510- 665- 2183 ( F)
Email: jlidicker@ tsrc. berkeley. edu
March 15, 2010
Word Count: 7,493 words, including 3 figures and 4 tables
1
CARSHARING’S IMPACT ON HOUSEHOLD VEHICLE HOLDINGS:
RESULTS FROM A NORTH AMERICAN SHARED- USE VEHICLE SURVEY
ABSTRACT
Carsharing has grown considerably in North America during the past decade and has flourished
within metropolitan regions across the United States and Canada. The result has been a new
transportation landscape, which offers urban residents an alternative to automobility without car
ownership. As carsharing has expanded, there has been a growing demand to understand its
environmental impacts. This paper presents the results of a North American carsharing member
survey ( N = 6,281). The authors establish a “ before- and- after” analytical design with a focus on
carsharing’s impacts on household vehicle holdings and the aggregate vehicle population. The
results show that carsharing members reduce their vehicle holdings to a degree that is statistically
significant. The average vehicles per household of the sample drops from 0.47 to 0.24. Most of
this shift constitutes one- car households becoming carless. The average fuel economy of
carsharing vehicles used most often by respondents is 10 miles per gallon ( mpg) more efficient
than the average vehicle shed by respondents. The median age of vehicles shed by carsharing
households is 11 years, but the distribution covers a considerable range. An aggregate analysis
suggests that carsharing has taken between 90,000 to 130,000 vehicles off the road. This equates
to 9 to 13 vehicles ( including shed and postponed auto purchases) for each carsharing vehicle.
INTRODUCTION
The emergence of carsharing in North America has changed the transportation landscape of
metropolitan regions across the continent. Carsharing systems provide members with access to
an automobile for short- term daily use. Automobiles owned by carsharing providers are
distributed throughout a network of locations. Members can access the vehicles at any time with
a reservation and are charged per time and often per mile. They benefit by obtaining personal
automobility without the need to own a private vehicle; this can result in considerable monetary
savings.
Modern day carsharing began in North America during the mid- 1990s, starting in Canada
and then spreading to the United States ( U. S.). Carsharing has since grown to more than 20
major metropolitan regions throughout the U. S. and Canada. As of July 2009, carsharing as an
industry had more than 378,000 members served by 9,818 vehicles throughout North America.
As carsharing has gained prominence in North American cities, there has been an increasing
demand for knowledge about its environmental impacts and how regional policymakers might
react to its expansion.
This paper reports on carsharing’s impact on vehicle holdings among member
households. The study results are based on a survey of carsharing members within organizations
operating throughout North America during late- 2008. The survey was designed primarily to
2
evaluate the greenhouse gas ( GHG) impacts of carsharing. The evaluation of these impacts,
strictly related to household travel, are reported in Martin and Shaheen ( 2010) [ 1]. The survey
assessed several aspects of carsharing’s impact on households including changes in vehicle
ownership, vehicle miles/ kilometers traveled, carsharing use, and public transit shifts.
Carsharing can facilitate reductions in household vehicle ownership because the service
largely eliminates the need for a private vehicle to complete trips. In this way, carsharing can
provide a member with an automobile only when needed. Typically, several members throughout
the day access a shared- use vehicle. Vehicles are usually parked throughout an urban region in
areas where there is a large enough market to support it. Carsharing vehicles generally are not
used for commuting. Since members incur hourly and sometimes mileage charges, use of a
carsharing vehicle for a full day’s auto- commute could quickly become prohibitively expensive.
Within cities, personal vehicles are allocated a large amount of urban space in the form of
parking and roadways. This allocation is a costly component of infrastructure to the public and
private sector. Furthermore, vehicle ownership costs are predominantly fixed versus variable.
This means that if an automobile is absolutely necessary for either work or non- work trips, then
the household is likely to own a vehicle. With vehicle ownership and its prepaid costs, the
automobile quickly becomes a relatively competitive mode when based on its marginal costs in
contrast to alternatives. Carsharing, by facilitating shared vehicle use, eliminates the need for
fixed ownership costs. Car dependent urban residents can save money and adjust to a less car-dependent
lifestyle.
This paper has four main sections. First, the authors provide a review of the relevant
carsharing literature, focusing on previous studies that have evaluated vehicle- holding impacts.
Second, the study methodology is presented. Third, the authors discuss the survey results with
respect to vehicle holdings and carsharing’s aggregate impact on the vehicle population. Finally,
the authors close with conclusions and issues for future study.
LITERATURE REVIEW
Although carsharing did not take hold in North America until the late 1990s, the continent’s first
demonstration of carsharing was the Short Term Auto Rental ( STAR) program. Established in
1983 in San Francisco, STAR was a 55- vehicle pilot that terminated after 18 months of
operation. Walb and Loudon ( 1986) evaluated STAR and found that 17% of members sold a
vehicle, while 43% postponed a vehicle purchase [ 2]. Carsharing would not gain traction until
the launch of CarSharing Portland more than a decade later [ 3]. Similar to STAR, evaluations of
CarSharing Portland found that 26% of members sold a car, while 53% avoided a purchase [ 4].
Carsharing returned to San Francisco with the launch of City CarShare in March 2001. Cervero
( 2003) initiated a before- and- after study to evaluate the impacts of City CarShare on members
and nonmember ( control) travel behavior three months before the launch and nine months after
[ 5]. Interestingly, two thirds of members came from zero- car households, while 20% were one-car
households. Cervero’s early City CarShare results were consistent with past work in North
America; they found similar demographics among members and that changes in vehicle
3
miles/ kilometers traveled ( VMT/ VKT) were not substantial. Early carsharing adopters were
primarily carless and used it as a means to augment their mobility [ 5].
Lane ( 2005) administered a 500- person online and mail- in survey to members of
PhillyCarShare in November 2003. Roughly 60% of members who joined were from zero- car
households. Members were demographically similar to the early adopters of City CarShare. Lane
evaluated vehicles sold as a result of membership as well as vehicles not acquired. He estimated
that each PhillyCarShare vehicle removed roughly 23 cars from the road [ 6].
As carsharing evolved, researchers began to uncover more pronounced effects on
VMT/ VKT. City CarShare impacts were revisited by Cervero and Tsai in 2004 and Cervero et
al. in 2007 [ 7, 8]. By the third study, VMT/ VKT reductions attributable to carsharing were
becoming more evident as member VMT/ VKT was found to decrease relative to nonmember
VMT/ VKT. VMT/ VKT reductions among carsharing members appeared to occur during the first
two years, but large variations existed within the group. Overall mean mode- adjusted
VMT/ VKT, which accounted for occupancy levels, was found to drop 67% for carsharing
members, contrasted with a 24% increase for nonmembers [ 8]. For more history on the
carsharing industry, see Shaheen et al. ( 2009), Shaheen and Cohen ( 2007), and Shaheen et al.,
( 1998) [ 9, 10, 11].
Until now, most North American carsharing studies have focused on one organization
within a single city [ 12]. Many of these evaluations have occurred during periods in which the
organization was just starting. Finally, in most studies, vehicle impacts have been just one
evaluation component, and few studies have attempted to characterize the vehicles that have
been shed by members with respect to fuel economy, age, and annual miles/ kilometers driven.
This study addresses these gaps by focusing on carsharing’s impact on household vehicle
holdings.
METHODOLOGY
The authors generated this study’s data from an on- line survey of North American carsharing
members in late- 2008. Individual carsharing organizations directed their members to take the
survey through an email solicitation. The respondents completed a single survey. Researchers
designed the questionnaire to provide the data necessary for a “ before- and- after” analysis.
Respondents were asked key questions about their household’s travel lifestyle during the
year before they joined carsharing. This included parameters such as annual VMT/ VKT made on
personal household vehicles ( if any) and travel on non- motorized modes and public transit. The
respondents were then asked to evaluate the same annual parameters “ at present,” as this
permitted simpler recollection and prevented respondents from self- assessing the “ after”
timeframe in which they may have shifted to a new set of travel patterns. Not surprisingly,
carsharing used by a single household member can affect the travel patterns of other household
members. For example, a married couple may commute to jobs in different locations, both by
automobile. The husband joins carsharing and switches to a public transit commute, but the
household retains “ his” car because its newer, and they shed the wife’s vehicle. Because this and
4
many similar scenarios are possible, the unit of analysis of this survey is the member’s
household.
To evaluate vehicle holdings, the survey collected the make, model, and year of each
vehicle within the household both before joining carsharing and at the time of the survey. The
make, model, and year of each vehicle were used to determine the vehicle’s fuel economy. Each
vehicle dating back to 1978 was linked to an appropriate entry in the Environmental Protection
Agency ( EPA) fuel economy database. Vehicles manufactured prior to 1978 are not listed in the
EPA’s database; these vehicles were given a standard combined fuel economy of 15 miles per
gallon ( 15.7 L/ 100km). In a small number of cases, vehicle information was partially complete,
and an average fuel economy factor from the year or model was assigned.
Other information collected included the make and model of the carsharing vehicle that
they drove most often. In addition, they were asked whether they would have purchased a car in
the absence of carsharing. This permitted an evaluation of whether or not members viewed
carsharing as a vehicle replacement/ substitute at the time of the survey.
Researchers also asked questions that would aid them in identify factors and events that
would confound the analysis. If a confounding factor was found, then the respondent would be
removed from the analysis. For instance, moving residential locations or changing jobs are fairly
common occurrences that correspond with many life events. Some moves are local or
unsubstantial, but others cause notable travel shifts. Respondents were asked whether they had
moved their home or work since joining carsharing. If either had changed, respondents were
asked whether their travel had changed more due to the move or carsharing. If a respondent
stated that the move had equal or dominant impacts on their driving, they were removed from the
final analysis.
Two key carsharing submarkets were not included in the analysis: college and exclusive
business/ government use. Respondents that identified themselves as part of these submarkets
were removed because the survey design was focused on assessing the impacts of the
neighborhood or residential carsharing model, which is the dominant model in the industry.
Finally, carsharing contains a subset of people who are members of the organization, but
otherwise do not regularly use the service. These members, termed “ inactive members,” exist for
several reasons. One reason is that some carsharing organizations have had zero cost
membership plans. Low or no fixed cost membership plans permit a person to be a carsharing
member much in the same way that one is a public library member. In evaluating the
environmental impacts of carsharing, it is questionable to consider changes from an inactive
member’s household as attributable to carsharing. Hence, respondents that identified themselves
as inactive members are assigned a zero impact.
Another reason for respondent removal was mis- answered questions, which made their
impacts incalculable. For consistency, the final dataset employed in this study is the same one
used in Martin and Shaheen ( 2010), which contains a more complete discussion of the data
processing methodology [ 1]. All respondents that completed the survey, regardless of the above
considerations, were entered into a drawing for a $ 100 credit to their carsharing account. The
participating North American organizations in the survey included: 1) AutoShare, 2) City
5
Carshare, 3) CityWheels, 4) Community Car Share of Bellingham, 4) CommunAuto, 5)
Community Car, 6) Co- operative Auto Network, 7) IGo, 8) PhillyCarShare, 9) VrtuCar, and 10)
Zipcar ( in the U. S. and Canada). The survey launched in early September 2008. Two reminders
were sent via each organization, and the survey closed on November 7, 2008. Most
organizations, which are located in a single city, distributed survey solicitations to all their
members. Because of Zipcar’s size and geographic distribution, the sample was capped at 30,000
members and targeted at specific markets. This included 5,000 each within New York City;
Boston; Washington, D. C.; Portland; and Seattle. An additional 2,500 ( each) in Vancouver and
Toronto also received survey invitations from Zipcar.
RESULTS
Study results are divided into four sections. The first describes the demographics and
circumstances of joining carsharing among the sample. In the next section, the authors describe
carsharing’s overall impact on household vehicle holdings. The third characterizes both shed and
added vehicles in terms of fuel economy, age, and miles/ kilometers driven. In the final section,
the authors present an analysis of carsharing’s aggregate vehicle impacts.
Sample Demographics and Circumstances of Joining
A total of 9,635 carsharing members completed the survey. After researchers removed
respondents due to confounding circumstances and mis- answered questions, the final dataset
contained 6,281 individuals. The balance of demographics and circumstantial categorizations
was not altered significantly due to filtering. Respondents were asked to characterize the
circumstances under which they joined carsharing. Table 1 shows the circumstantial categories
that were available to respondents in the survey. The table provides respondent percentage by
respective categories for the full and final dataset.
6
TABLE 1 Circumstances of Joining Carsharing
Percent of Respondents
Completing the Survey
( N = 9635)
Percent of Respondents
in Final Dataset
( N = 6281)
1
Owned at least one car, but needed an additional car for greater flexibility, and
joined carsharing instead of acquiring an additional car. 9% 8%
2
I am in college, and I joined carsharing to gain access to a vehicle while in
college. 6% 0%
3 Owned one car, but I joined carsharing and got rid of the car. 13% 14%
4
My household did not have a car, but joined carsharing to gain additional
personal freedom. 43% 51%
5
My household did not have a car, but changes in life required a car and I
joined carsharing instead. 6% 7%
6 My employer joined carsharing, and I joined through my employer. 5% 3%
7 A car of mine stopped working, and instead of replacing it I joined carsharing. 8% 8%
8 Owned more than one car. Got rid of at least one car and joined carsharing. 3% 3%
9
I live in an apartment building with a designated carsharing vehicle, and I
joined through its membership arrangement. 0% 0%
10 I joined carsharing for reasons other than those listed above. Please explain: 9% 7%
Question: Please select the statement that best characterizes the circumstances under which you joined carsharing.
Circumstantial Category
Table 1 demonstrates that the balance of respondents remained relatively stable across the
categories, with two exceptions: 1) college responses, representing 6% of the dataset, falls to
zero, and 2) the category “ My household did not have a car, but joined carsharing to gain
additional personal freedom” rose from 43% to 51% in the final dataset.
Demographics are similarly impacted. The distribution of income, education, and age
follow the same shape in the complete and final datasets. One distinction is that the final dataset
is slightly older and has a higher income and education. Table 2 illustrates the sample
demographics, split by the U. S. and Canada, as well as the complete and final sample. The
demographic distinctions between the countries are small. They exhibit a similar gender balance.
The age distribution shows that American members are relatively younger but have slightly more
education. The income distribution of respondents in both countries corresponds well with the
mode of U. S. and Canadian incomes between $ 40,000 to $ 60,000. Respondents in each country
answered income questions in their respective currencies, but at the time of the survey the
currencies of Canada and the U. S. were close to parity. Overall, sample divisions across
countries showed some nominal distinctions, but they also illustrated carsharing members share
7
very similar demographic distributions in the U. S. and Canada. The sample sizes across
demographics in Table 2 are different, as some respondents skipped or declined to answer certain
questions.
TABLE 2 Demographic Distributions by Country and Dataset
Demographic Attribute United States Carsharing Canadian Carsharing Total Final Total Complete
Gender N = 4229 N = 2024 N = 6253 N = 9578
Male 43.9% 46.3% 44.7% 43.4%
Female 56.1% 53.7% 55.3% 56.6%
Age Category N = 4201 N = 1996 N = 6197 N = 9482
Less than 20 0.1% 0.1% 0.1% 0.6%
20 to 30 37.6% 30.6% 35.3% 39.3%
30 to 40 29.5% 34.2% 31.0% 29.1%
40 to 50 16.0% 19.0% 16.9% 15.8%
50 to 60 11.2% 10.9% 11.1% 10.4%
60 to 70 4.9% 4.6% 4.8% 4.1%
70 to 80 0.6% 0.7% 0.6% 0.6%
80 to 90 0.2% 0.1% 0.1% 0.1%
Education N = 4235 N = 2028 N = 6263 N = 9591
Grade School 0% 0% 0% 0%
Graduated High School 2% 4% 2% 2%
Some College 10% 17% 12% 12%
Associate’s Degree 3% 5% 4% 4%
Bachelor’s Degree 43% 39% 42% 42%
Master’s Degree ( MS, MA, MBA) 28% 26% 27% 27%
Juris Doctorate Degree ( JD) 5% 1% 4% 4%
Doctorate ( PhD, EdD, etc.) 8% 6% 8% 8%
Other 1% 3% 2% 2%
Income ( HH, $ US) N = 4247 N = 2034 N = 6281 N = 9536
Under $ 20,000 6% 6% 6% 8%
$ 20,000 - $ 40,000 18% 16% 17% 18%
$ 40,000 - $ 60,000 19% 23% 20% 19%
$ 60,000 - $ 80,000 14% 17% 15% 14%
$ 80,000 - $ 100,000 11% 12% 11% 11%
$ 100,000 - $ 120,000 7% 7% 7% 7%
$ 120,000 - $ 140,000 4% 4% 4% 4%
More than $ 140,000 12% 6% 10% 9%
Decline to Respond 9% 10% 9% 10%
Carsharing’s Impact on Vehicle Holdings
The results show that carsharing lowers the total number of vehicles held by members, and this
shift is substantial. When changing vehicle holdings, there are four possible actions that a
household can take: the household can shed, add, retain, or replace a vehicle. Vehicle
replacement involves the shedding and adding of a vehicle within the same household. For
instance, in a household that sheds two vehicles and adds one, the added vehicle is counted as a
replacement. Similarly, in a household that sheds one vehicle and adds two, one of the added
vehicles is a replacement, and the other is an added vehicle. Figure 1 illustrates the breakdown of
the change in vehicle holdings across these four categories, as well as a t- test on the paired
sample mean. In addition, a bootstrap simulation of both “ before” and “ after” means is
8
presented. Bootstrap simulations replicate the repeated sampling of data, which in this case
illustrates that the sample mean is normally distributed given the sample size.
Vehicle Change Category
Zero
Car Households
One
Car Households
Two
Car Households
Three
Car Households
Four
Car Households
Five or more
Car Households
Vehicles Shed 0 1437 486 70 37 16
Vehicles Retained 0 480 340 68 15 19
Vehicles Added 219 21 5 1 0 0
Vehicles Replaced 0 187 122 19 10 1
Net Change
( Added+ Replaced‐ Shed)
219 ‐ 1229 ‐ 359 ‐ 50 ‐ 27 ‐ 15
Lower Upper
Vehicles After - Vehicles Before ‐ 0.233 0.559 0.007 ‐ 0.251 ‐ 0.214 ‐ 32.955 6280 0.00
‐ 1461
2047
Total
921
246
340
Paired Test Variables
Paired Differences t- test
Mean Std. Deviation Std. Error Mean
99% Confidence Interval
of the Difference
t df
Sig. ( 2-
tailed)
FIGURE 1 Profile and statistical evaluation of the change in vehicle holdings.
The columns show the action taken by households that held the stated number of vehicles
“ before” joining carsharing. Vehicles retained impose no change in the overall vehicle count.
The total number of vehicles held by households “ before” joining carsharing is the sum of those
shed and retained ( 2,968). This number amounts to just under one vehicle for every two
households and reflects that many households that join carsharing are carless. The net change in
vehicles is the sum of vehicles added and vehicles replaced ( as they are distinct) minus the total
number of vehicles shed. This net change across the sample is a reduction of 1,461, resulting in a
sample vehicle count “ after” joining carsharing of 1,507. Thus, the sample dropped the total
number of vehicles by about 50%. By virtue of its magnitude and the large sample size, this drop
is statistically significant ( p< 0.01). The average vehicles per household “ before” carsharing is
0.47, and the average vehicles per household “ after” carsharing is 0.24. The Canadian average
“ before” carsharing is 0.31 vehicles per household and 0.13 vehicles per household “ after.” The
U. S. average “ before” carsharing is 0.55 vehicles per household and 0.29 vehicles per household
“ after.” Both of these changes are statistically significant.
9
A fair number of the households that changed their vehicle holdings owned more than
one vehicle. In addition, some households increased their vehicle holdings, while others shed
only some of their vehicles. Table 3 presents a cross- tabulation of household vehicle holdings
“ before” and “ after” joining carsharing and shows how households within the sample
transitioned to new vehicle holding states.
TABLE 3 Transition of Household Vehicle Holding States Due to Carsharing
After Joining
Carsharing
Before
Joining Carsharing
Zero
Car Household
One
Car Household
Two
Car Household
Three
Car Household
Four
Car Household
Five or more
Car Household
Total
Zero Car Household 3686 182 14 3 0 0 3885 ( 62%)
One Car Household 1250 646 21 0 0 0 1917 ( 31%)
Two Car Household 68 228 112 5 0 0 413 ( 7%)
Three Car Household 7 11 8 19 1 0 46 ( 1%)
Four Car Household 3 2 3 3 2 0 13 ( 0%)
Five or more Car Household 2 1 0 0 1 3 7 ( 0%)
Total 5016 ( 80%) 1070 ( 17%) 158 ( 3%) 30 ( 0%) 4 ( 0%) 3 ( 0%) 6281
The total column at the far right of Table 3 shows the distribution of households by
vehicle holdings “ before” joining carsharing. That is, 62% of households joining carsharing
owned no vehicle when they joined, while 31% of households owned one vehicle. The bottom
row total shows the distribution of households by vehicle holdings “ after” joining carsharing.
The shift towards carless households is substantial, as they comprise 80% of the “ after” sample.
Most of this shift is comprised of one- car households becoming carless households. The second
largest shift in holdings involves two- car households transitioning into one- car households  4%
( n= 228). This is followed by two- car households transitioning into carless households  1%
( n= 68). The diagonal shows households that did not change the number of vehicles owned.
Given the large change in vehicles discussed earlier, a paradoxical but accurate observation is
that a majority of carsharing households do not change their vehicle holdings. However, this is
only true when including carless households, which have no vehicles to shed. Only 12% ( n= 782)
of households that had a vehicle “ before” carsharing maintained the same vehicle stock.
Characteristics of Vehicles Added and Shed
The analysis above illustrates carsharing’s impact on vehicle counts within the sample; however,
the vehicle characteristics are not revealed. This section reports on key attributes including fuel
economy, vehicle age, and miles/ kilometers driven of vehicles shed. Figure 2 presents three
graphs that outline fuel economy distributions. Two of these graphs show the fuel economy
distribution of vehicles shed and added by carsharing households. The third graph shows the fuel
economy distribution of the carsharing vehicles that respondents indicated that they used most
often.
10
0%
2%
4%
6%
8%
10%
12%
10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
Percent of Vehicles
Vehicles Shed
0%
2%
4%
6%
8%
10%
12%
10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
Percent of Vehicles
Vehicles Added
Average Fuel Economy = 25.2
Median Fuel Economy = 24
N = 585
0%
5%
10%
15%
20%
25%
30%
10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
Percent of Vehicles
Combined Fuel Economy ( miles per gallon)
Carsharing Vehicles
Average Fuel Economy = 32.8
Median Fuel Economy = 31
N = 6281
Average Fuel Economy = 23.3
Median Fuel Economy = 23
N = 2046
FIGURE 2 Fuel economy distribution of household vehicles shed/ added and carsharing
vehicles driven.
Figure 2 reveals several interesting qualities of the vehicle fuel economy distributions.
For vehicles shed, it is approximately normal with a mean of 23.3 mpg ( 10.2 L/ 100km). The
distribution of vehicles added ( which includes replaced vehicles) is characteristic of
concatenated normal distributions with two separate means. The overall mean is 25.2 mpg ( 9.4
L/ km), and the median is 24 mpg ( 9.9 L/ 100km). The smaller bell shape to the right indicates a
fair share of respondents adding vehicles with a fuel economy of about 30 mpg ( 7.9 L/ 100km).
Still further to the right is a spike of vehicles at 46 mpg ( 5.1 L/ 100km), and this represents
acquisitions of the second- generation Toyota Prius. A comparison of these two distributions
shows that the autos added are slightly more efficient on average, but there is still a notable share
of low fuel economy vehicles added by households. The distribution of carsharing vehicle fuel
economy looks very different in shape from the other two. To start, the scales of the percents are
different, as three fuel economy values represent nearly 60% of the distribution. Many carsharing
organizations offer a diversity of vehicles to members, but the majority are highly efficient
hybrids, sedans, and compact cars. The average fuel economy of carsharing vehicles is 32.8 mpg
( 7.2 L/ 100km) with a median of 31 mpg ( 7.6 L/ 100km). Hence, the average carsharing vehicle
11
used by the sample overall ( U. S. and Canada) is a full 10 mpg more efficient than the average
vehicle shed by members.
Age and Miles/ Kilometers Driven on Vehicles Shed
The survey data also allow for an analysis of the miles/ kilometers driven on shed vehicles. When
considering passenger cars, the nationwide average VMT/ VKT in 2007 is about 12,300
miles/ 19,800 kilometers per year in the U. S. [ 13]. In Canada, the average driving distance is
about 8,800 miles/ 14,200 kilometers per year [ 14]. The vehicles that are removed from the road
due to carsharing are typically driven less than average, but some are driven more. The data
show that nearly 75% of all vehicles shed are driven less than 10,000 miles/ 16,000 kilometers
per year. More than 90% of all vehicles shed are driven less than 16,000 miles/ 26,000 kilometers
per year. The average annual distance driven on a vehicle that is shed by a carsharing household
is 8,064/ 13,000 kilometers miles per year, and the median is close to 7,000 miles/ 11,300
kilometers per year. The average miles driven for vehicles shed by U. S. carsharing members is
8,200 miles/ 13,200 kilometers per year, and for shed Canadian vehicles the average is 7,700
miles/ 12,300 kilometers per year. These averages and distributional parameters are consistent
with the assumption that carsharing primarily targets lower mileage vehicles. But, it also
suggests that carsharing can facilitate some households to give up vehicles that are driven
distances that are well above average. The age of shed vehicles is another important factor,
which influences carsharing’s impact on the overall vehicle fleet. Figure 3 shows the distribution
of the production year of vehicles shed by carsharing households.
12
6
1
7
4 4
1 3 4 4 6 4 3
9
4
17
25
31
34
39
54
66
75
85 85
114
107
113
127
148
104
120
125
101 99
89
84
61
36
11
0
0
20
40
60
80
100
120
140
160
Older than 1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
Number of Vehicles
Model Year of Vehicle Shed
Total Sample
N = 2010
Average Age: 17.3 years
Median Age: 11 years
Mode Age: 10 years
U. S. Sample
N = 1511
Average Age: 16.4 years
Median Age: 10 years
Mode Age: 10 years
Canadian Sample
N = 499
Average Age: 20.1 years
Median Age: 12 years
Mode Age: 14 years
FIGURE 3 Distribution of vehicles shed by model year ( vehicle age).
The shape of the distribution is negatively skewed with the mode at the 1998 model year.
Thus, the mode and the median age of the vehicles are 10 and 11 years old, respectively. The
average vehicle age is closer to 17 years as a result of the long tail extending back towards very
old vehicles. In considering the differences between Canada and the U. S., shed Canadian cars
were slightly older. Given the unique shape of the distribution, the median age in both cases is
more representative of the typical car shed than the average. The sample size of 2,010 is slightly
smaller than the total number of vehicles shed because some respondents provided incomplete
vehicle information. The distribution shows that the overwhelming majority of vehicles lie
between the years of 1984 and 2008, bounding a normal- shaped distribution. A fair number of
the vehicles shed ( 41%) are younger than ten years old. The range of years within the normal-shaped
distribution is well within the typical vehicle lifespan. This suggests that a large number
of carsharing members may enter carsharing when their vehicle is at an age close to the point at
which it would be retired.
Respondents were asked whether in the absence of carsharing, whether they would buy a
car. The available responses ranged from “ definitely not,” “ probably not,” “ maybe,” “ probably,”
and “ definitely” buy a car. This question generated insight into the degree to which carsharing
was, at the time of the survey, substituting for a vehicle not acquired. The results suggest that
about 25% of the total sample indicated that they maybe, probably, or definitely would buy a car
13
in the absence of carsharing. Only the responses of households that did not shed a vehicle were
considered for this estimate ( due to double counting otherwise).
Aggregate Carsharing Impacts
Overall, the sample shows that people who joined carsharing made significant cuts to aggregate
vehicle holdings. While it is clear that these cuts are substantial within the sample, it is not yet
evident how these results scale to the carsharing industry. That is, while members shed vehicles,
carsharing organizations also add vehicles to urban areas, so the degree to which this substitution
reduces overall vehicles is not immediately clear. To gain insight into this issue, several factors
and assumptions are key.
While the sample of carsharing households is random among active members, several
cohorts were excluded from the sample, including college students and business/ governmental
fleet users that do not use vehicles for non- work trips. The share of these cohorts within the
sample is 6% and 2%, respectively. Their exclusion does not imply a zero impact, but the survey
design was not targeted at the analysis of these cohorts. Nevertheless, the sample of these shares
within the population is applied as an approximation of the population share. Another cohort,
inactive members, was excluded from the analysis. Inactive members constituted a share of
approximately 8% of the complete sample. This share is likely subject to a non- response bias
( i. e., inactive carsharing members are less likely to take the survey than active members). Thus,
the sample share of 8% is likely a lower bound. By definition, inactive members have a zero
impact because they continue their travel lifestyles irrespective of their carsharing membership.
Nevertheless, it would be a mistake to scale the results of any carsharing sample to an industry
level without acknowledging that a share of the industry membership does not use the service.
The uncertainty of the inactive member share is addressable with a sensitivity analysis, and their
impact on the aggregate results is important.
As shown earlier, a net of 1,461 vehicles were shed across 6,281 households. As of mid-
2009, the carsharing industry had 378,000 members within North America. However, as this
population includes college members, business users, and inactive members, the active member
population using the neighborhood model is smaller. The authors scaled the population to “ active
members only” by subtracting college members ( 6%) and strictly business members ( 2%)  an
8% from the total population  to obtain the 347,390 members using the neighborhood
carsharing model. The uncertainty of the inactive member share is treated through a sensitivity
analysis.
In addition, some households contain more than one member. Since the unit of analysis in
this study is the respondent household, questions were inserted to detect duplicate respondents
from different members within the same households. In searching for duplicate responses, the
survey asked questions about joint membership. The survey found that 81% of the 6,281
respondents were the sole carsharing members within the household. The remaining 19% of
respondents were members living in households with someone else that was a carsharing
member. The share of respondents with more than two members per household was negligible.
14
This membership balance implies that about 19% of the population has two carsharing members
within one household. Thus, translating the 347,390 carsharing members to carsharing
households is computed as ( 347390 (. 81) + 347390(. 19)/ 2), which roughly equals 314,390
households using carsharing. The sensitivity analysis varying the inactive share is presented in
Table 4.
TABLE 4 Sensitivity Analysis of Industry- Wide Carsharing Impacts on Vehicle Holdings
Inactive
Share
Active Carsharing
Household Population
Total Vehicles
Shed
Vehicles Shed
Per Carsharing Vehicle
Vehicles
Avoided
Total Vehicles Removed
Per Carsharing Vehicle
0% 314,390 73,129 7.4 78,598 15.5
5% 298,671 69,473 7.1 74,668 14.7
10% 282,951 65,816 6.7 70,738 13.9
15% 267,232 62,160 6.3 66,808 13.1
20% 251,512 58,503 6.0 62,878 12.4
25% 235,793 54,847 5.6 58,948 11.6
30% 220,073 51,190 5.2 55,018 10.8
35% 204,354 47,534 4.8 51,088 10.0
40% 188,634 43,877 4.5 47,159 9.3
45% 172,915 40,221 4.1 43,229 8.5
50% 157,195 36,565 3.7 39,299 7.7
The left column describes the percentage of inactive members. The top row shows
carsharing’s impact on total vehicles shed assuming that all households are active. But as the
sample revealed a share of ~ 10% inactive members, it is probable that the share of inactive
members is between 15% and 40% across the entire population. The table illustrates the
estimated total number of vehicles shed with each assumption. The fourth column to the right
shows the vehicles shed per carsharing vehicle, which is the third column divided by 9,818. This
result suggests that between 4 to 6 vehicles were shed per carsharing vehicle. The vehicles
avoided as a result of carsharing are computed separately, as this 25% share did not shed any
vehicles, but did not purchase any vehicles due to carsharing. When vehicles avoided are
considered in conjunction with vehicles shed, the likely estimates suggest that carsharing has
removed between 90,000 to 130,000 vehicles from the road or between 9 to 13 cars for each
carsharing vehicle. This estimate is consistent with the carsharing literature [ 10].
It is important to recognize that the estimated share of inactive members is a population
estimate. But this does not imply that the share is evenly distributed across all organizations.
Indeed, significant variation of the true share across organizations is likely. A major factor
impacting the share is pricing plans, and plans that have no or low fixed cost are the most likely
to contain inactive members. Not accounting for inactive members could result in an
15
overestimation of aggregate impacts. Finally, inactive membership proportions are likely to
change in the future as the industry evolves.
CONCLUSION
Evidence from this North American carsharing member survey demonstrates that carsharing
facilitates a substantial reduction in household vehicle holdings, despite the fact that 60% of all
households joining carsharing are carless. Households joining carsharing held an average 0.47
vehicles per household. Yet the vehicle holding population exhibited a dramatic shift towards a
carless lifestyle. Based on assumptions with respect to the active member population, it is
estimated that carsharing has removed between 90,000 to 130,000 vehicles from the road ( 9 to
13 vehicles per carsharing vehicle, including shed and postponed car purchases) in North
America to date. The vehicles shed are often older, and the carsharing fleet average is 10 mpg
more efficient than the fuel economy of vehicles shed. Inactive memberships reduce the
forecasted aggregate impacts, but it is worth noting that even if every other household of the
population were inactive, carsharing would still be effective in reducing the overall number of
household vehicle holdings.
Additional research is warranted in several areas. Shifting demographics and urban
environments will demand continual future study, along with VMT/ VKT impacts due to
carsharing. While this study’s instruments were not designed to evaluate carsharing’s impact on
the college or business/ governmental submarkets, both of these markets are expanding and
targeted evaluations are needed. Further exploration of inactive membership shares is also
important. Though it is clear that they are a factor, this study does not posit a formal definition of
inactive members. Such a definition would be useful for future policy development.
As carsharing continues to grow, it is possible that its relative impact may expand.
Carsharing represents an attractive alternative to carless households, but such households are a
minority in North America. In the future, as carsharing networks become denser and more
complete, their attractiveness to vehicle- holding households may increase. Further, carsharing
may expand into lower density communities ( e. g., suburbs), and impacts could expand as well.
Thus, while carsharing already has an impact in many metropolitan regions, considerable
environmental could expand in the future.
16
ACKNOWLEDGMENTS
The Mineta Transportation Institute, the California Department of Transportation, and Honda
Motor Company, through its endowment for new mobility studies at the University of California,
Davis, generously funded this research. The authors would like to thank the carsharing programs
in North America that participated in the survey. Thanks also goes to Caroline Rodier, Adam
Cohen, Denise Allen, Melissa Chung, and Brenda Dix of the Transportation Sustainability
Research Center and the Innovative Mobility Research group at the University of California,
Berkeley for their assistance with the literature review and survey development. Neil Weiss of
Arizona State University provided some very useful consultation. The authors also would like to
thank Asim Zia and Alexander Gershenson of San Jose State University, as well as Dave Brook,
Clayton Lane, and Kevin McLaughlin for their assistance with survey development and review.
The contents of this paper reflect the views of the authors and do not necessarily indicate
acceptance by the sponsors.
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