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The rapid motorization of China raises questions about the potential for alternative mobility solutions, such as carsharing (short-term auto use), used in developing megacities such as Shanghai. Demand for motor vehicles is increasing rapidly, but many aspects of urban transportation in Shanghai and in China more broadly separate the city and the center from other urban environments in which carsharing has thrived traditionally. For example, the taxi is much more prominent in the transportation systems of Shanghai and Beijing than in most North American and European cities. Carsharing tends to thrive in environments in which the broad population has experience with driving and automobile ownership. This experience is lacking in Shanghai. To evaluate carsharing's potential in Shanghai, the size and competitiveness of the taxi systems of key carsharing cities in Europe, North America, and Asia were compared. The analysis illustrated core distinctions between Shanghai and other major cities in which carsharing has thrived. To explore further the potential response of Shanghai's citizens to carsharing, a survey was conducted (N = 271) of a subpopulation in Shanghai from November 2010 to February 2011. The survey analysis showed that those interested in carsharing were younger, more likely to be educated, had longer commutes, and owned fewer cars than those with no interest in carsharing. This paper concludes with a discussion of this study's implications for the development of a carsharing industry in Shanghai.
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Transportation Research Record: Journal of the Transportation Research Board,
No. 2319, Transportation Research Board of the National Academies, Washington,
D.C., 2012, pp. 86–95.
DOI: 10.3141/2319-10
M. Wang, Low Carbon City Research Center, Chinese Academy of Science, Shang-
hai Advanced Research Institute, Zhangjiang Hi-Tech Park, Pudong, Shanghai,
201203, China. E. W. Martin and S. A. Shaheen, Transportation Sustainability
Research Center, University of California at Berkeley, 1301 South 46th Street,
Building 190, Richmond, CA 94804-4648. Corresponding author: E. W. Martin,
China’s explosive economic growth has increased the demand
for urban automobility. Overall, however, vehicle penetration is still
low; China has 46 vehicles per 1,000 people versus the 800 vehi-
cles per 1,000 people in the United States (2–5). Even so, the rapid
pace of China’s growth portends considerable change with respect
to mobility. At the same time, the successful deployment of bike-
sharing (shared-use, public bicycles) in China and elsewhere has
raised new questions about the appropriate paradigm of shared-use
vehicle mobility that might evolve in cities, such as Shanghai (6).
Several societal distinctions could influence how carsharing might
operate and thrive in China. Historically, carsharing has been suc-
cessful in highly motorized societies. In China, however, carsharing
would have to grow in a fundamentally different environment within
a population that had not had experienced widespread vehicle own-
ership or auto access. Thus, if carsharing were to be successful in
China, its impact would likely be different in nature than elsewhere.
Research in Europe and North America has shown that carshar-
ing lowers vehicular emissions and ownership (1, 6–17). Because
there are fewer vehicles and miles driven to eliminate, carsharing
accelerates auto access in the short term. Nevertheless, China is rap-
idly building new cities and, in tandem with its built infrastructure,
could encourage shared-use vehicle mobility in lieu of personal auto
ownership. In the long term, this approach might alter the path of
traditional motorization if it were broadly adopted in urban China.
Thus China could become a country in which carsharing enabled
vastly more individuals to access automobiles for the first time and in
turn reduced the need (or desire) for personal auto ownership among
people that never had owned a vehicle in the first place.
Another distinction in China is the considerable competition that
carsharing would face from the well-established and inexpensive
taxi and public transit systems. Carsharing would also face chal-
lenges to overcome legal hurdles and to obtain governmental sup-
port. In addition, carsharing is a relatively unfamiliar mode to most
Chinese citizens. A recent survey in Beijing found that less than
40% of respondents were familiar with carsharing (15). Thus initial
advertising and educational efforts would likely be needed (as they
once were in the United States).
To evaluate the potential customer base of carsharing in Shanghai,
researchers conducted a survey of 271 respondents in Shanghai
from November 2010 to February 2011. The survey explored the
potential response and interest in carsharing among a Shanghai sub-
population. This research also explored exogenous factors in urban
regions that could influence the degree to which carsharing might
compete with the taxi.
This paper is organized into four sections. First, a review is pro-
vided of past carsharing programs in China and related research. Next,
the challenges faced by carsharing in China, as distinct from those in
Carsharing in Shanghai, China
Analysis of Behavioral Response to Local Survey
and Potential Competition
Mingquan Wang, Elliot W. Martin, and Susan A. Shaheen
The rapid motorization of China raises questions about the potential
for alternative mobility solutions, such as carsharing (short-term auto
use), used in developing megacities such as Shanghai. Demand for motor
vehicles is increasing rapidly, but many aspects of urban transportation
in Shanghai and in China more broadly separate the city and the center
from other urban environments in which carsharing has thrived tradi-
tionally. For example, the taxi is much more prominent in the transporta-
tion systems of Shanghai and Beijing than in most North American and
European cities. Carsharing tends to thrive in environments in which
the broad population has experience with driving and automobile own-
ership. This experience is lacking in Shanghai. To evaluate carsharing’s
potential in Shanghai, the size and competitiveness of the taxi systems
of key carsharing cities in Europe, North America, and Asia were com-
pared. The analysis illustrated core distinctions between Shanghai and
other major cities in which carsharing has thrived. To explore further the
potential response of Shanghai’s citizens to carsharing, a survey was con-
ducted (N 5 271) of a subpopulation in Shanghai from November 2010
to February 2011. The survey analysis showed that those interested in
carsharing were younger, more likely to be educated, had longer com-
mutes, and owned fewer cars than those with no interest in carsharing.
This paper concludes with a discussion of this study’s implications for the
development of a carsharing industry in Shanghai.
Carsharing provides individuals with short-term access to auto-
mobiles to complete personal trips within an urban region. In Europe,
North America, Australia, and parts of Asia, carsharing has emerged
as a means to facilitate temporary access to personal vehicles without
ownership costs (1). The neighborhood carsharing model, which stra-
tegically locates operator-owned or leased vehicles within residential
areas of urban environments, has been the most popular approach to
date. Typically, third-party operators target large, densely populated
areas with high parking costs and robust public transportation net-
works. Success has been achieved overwhelmingly within tradition-
ally industrialized societies with a history of motorization. Rapidly
motorizing economies, such as China, have not experienced major
initiatives in carsharing to date.
Wang, Martin, and Shaheen 87
North America and Europe, are examined. Third, the study methodol-
ogy is introduced. A review of the results is set out in the fourth sec-
tion. To build on the insights from the analysis, the paper concludes
with a discussion of how carsharing may best be designed within
China to deliver the goals of efficient automobility and long-term
economic sustainability.
Carsharing began in Europe and dates back to the late 1940s. However,
it was not until the 1980s that modern carsharing began to take hold
in central Europe. The concept was then exported to North America,
where it arrived in Canada in the mid-1990s. Since then, carsharing
systems have continued to flourish across the continent (1).
As of October 2010, the carsharing industry was established in
26 countries and comprised 1,250,000 members, who were served
by 31,000 vehicles (18). Within that population, Asian organizations
contributed 77,817 members that shared 4,410 vehicles. By late
2011, there were 35 carsharing operators in Asia: one in Israel and
34 in four East Asian countries (i.e., Japan, Singapore, South Korea,
and now China). By comparison, in July 2011, North America had
more than 639,000 members, who shared more than 12,600 vehicles
(18). Researchers that evaluated the industry’s growth and its impact
on vehicle ownership and emissions found that, in general, carsharing
reduced both (12, 15–17, 19–21).
Carsharing systems in Asia have evolved separately from those
in Europe and North America. In Japan, many early systems were
project-based and characterized by advanced technology both in
operations and in the use of electric vehicles (20). Early business
models focused on service to downtown business customers. However,
the industry has since evolved toward the neighborhood business
model and drastically reduced its emphasis on electric vehicles. The
other major center of carsharing in Asia is Singapore. Singapore’s
carsharing programs were less directed to reduce auto ownership
and more oriented to provide access and mobility to residents inter-
ested in the use of a vehicle. Early programs in Singapore applied
advanced technology and experimented with one-way trips more
than with electric vehicles, but, like Japan, Singapore’s programs
later converged to provide more traditional carsharing services (8).
Outside of these two countries, carsharing has been slow to take hold
in Asia; it was introduced in South Korea only recently.
Although the carsharing industry has struggled to expand its foot-
print in Asia, bikesharing has flourished in several cities worldwide,
and quite prominently in Hangzhou, China. Unlike carsharing, bike-
sharing more readily permits one-way trips and overcomes last-mile
connectivity concerns often associated with public transit. Recent
research on bikesharing in Hangzhou found that 30% of respondents
used bikesharing in conjunction with a public transportation mode
as part of their commute (22). By March 2011, the Hangzhou bike-
sharing system had grown to 60,600 bicycles, with 2,416 fixed stations
in eight core districts (22). Although bikesharing has been found to
benefit the public through the augmentation of public transit, it is
subject to large operational costs and has not yet attained economic
self-sufficiency (23).
Carsharing in its traditional neighborhood form faces several unique
barriers that are less relevant to bikesharing. Similar to other Asian
cities, urban environments in China have expensive parking costs,
and urban highways already are congested with vehicles. Overall, the
body of literature devoted to carsharing in China is small. Although
recent research in China has focused on neighborhood carsharing
operations, analyses of consumer response, or of existing operations,
are limited (24, 25).
To evaluate how citizens in China might respond to carsharing,
Shaheen and Martin explored the concept in Beijing with a survey
of 840 respondents in 2006 (15). The survey results revealed that
more than 25% of respondents were highly interested in carsharing,
although only 40% of this group had been familiar with the concept
previously. Respondents interested in carsharing were more inclined
to take public transit, bicycle, and walk. They also had slightly higher
education levels, were less auto-reliant, and had some desire to pur-
chase a vehicle. Only 21% of respondents reported the ability to drive,
which indicated that driver education might be critical to the adoption
of carsharing. This and other challenges suggest that any carsharing
industry that emerges in China may evolve differently than it has in
Asia and elsewhere.
Despite considerable growth in motorization and demand for auto
ownership, only two carsharing operators—EdoAuto in Beijing
and Dazhong in Shanghai—existed in China as of 2011. EdoAuto
operates in the suburban regions of Beijing and advances a business
model similar to the neighborhood carsharing model (26). As of
July 2011, EdoAuto had 60 members and six vehicles located in
four parking lots. The center of the EdoAuto network is about 20 km
from the center of Beijing. As a private company, EdoAuto does not
operate in a direct relationship with the local government (26). The
Dazhong system in Shanghai is quite different, and it is currently the
closest model that Shanghai has to carsharing at this time. Dazhong
offers services that are probably better described as “taxi-sharing,”
in which the driver is supplied by the company. Consumers make
reservations online for trips that may be shared with strangers (27).
Dazhong is the biggest taxi and car-rental company in Shanghai and
possesses almost 20,000 vehicles that operate in the city. The com-
pany also is involved in a number of other industries, which include
bus transit and real estate development (27). Dazhong’s entry into
carsharing in 2011 was small and experimental, with only four taxi-
sharing vehicles (28). Nevertheless, Dazhong’s approach may rep-
resent a practical carsharing business model in China. Through the
combination of services with an existing taxi fleet, the company has
avoided the need to establish a network of vehicles in urban regions
with scarce parking and high land costs.
In this respect, a major challenge that carsharing faces in Chinese
cities (particularly in Shanghai) is the prominent role that taxis play
within the transportation system. Taxis in Chinese cities have a
competitive advantage because of their cost, the mobility they pro-
vide, and the limited access to personal vehicles and driving expe-
rience in the population. To evaluate this dynamic in more detail,
the study reported in this paper included a comparison of existing
taxi costs for major cities in Asia, the United States, Europe, and
China. In China, taxis have two price tiers, one for day and the other
for night, with an average rate per mile that is equivalent to about
US$0.8. Some of the more cosmopolitan cities in the United States,
such as New York City and Washington, D.C., charge about $2.50
for the initial fare and $2.00 per mile, with a time charge of $0.4
per minute, as well as peak and nighttime surcharges. Table 1 shows
Shanghai taxi rates in Central Shanghai versus the suburbs. To illus-
trate these differences on a normalized scale, Table 2 shows the
relative cost of an 8-km, 30-min taxi trip in nine major world cities,
alongside Beijing and Shanghai.
88 Transportation Research Record 2319
Table 2 presents all costs in U.S. dollars and shows that the cost of
taxi services in Beijing and Shanghai was nominally lower than else-
where. When adjusted for median income, however, the ratio between
trip cost and household income was similar (i.e., between .02% and
.03% across the range of cities). One notable difference between Asian
and Western cities was the relative size of the taxi modal share (for all
trips). The major Asian cities exhibited taxi modal shares of at least
3%, and up to 8%, whereas most Western cities generally did not
exceed 1%. Because taxis can service similar trips to carsharing vehi-
cles, the elevated role that taxis play in China and other Asian cities
portends an additional competitive obstacle that is not as prominent in
many Western cities. The size of Asia’s taxi fleet as compared with that
in the West also indicates the larger relative role of taxis. In 2011, more
than 65,000 taxis operated in Beijing, and 50,000 operated in Shanghai
(29, 41). Only Tokyo had nearly as many, whereas Singapore had half
as many. New York, arguably one of the most taxi-intensive cities in
North America, had only 13,000 (yellow cabs).
The far right column of Table 1 illustrates the approximate ratio
of taxi to carsharing cost, where carsharing cost is the approximate
hourly cost, and taxi cost is computed for the standardized trip indi-
cated previously. These ratios provide insight into the relative com-
petitiveness of carsharing with taxis. The highest ratios (in which
carsharing was relatively more competitive) were found in Europe
and North America, whereas Singapore and Tokyo had the lowest
ratios. For China, the current prices of EdoAuto and Dazhong were
used as proxies, even though these two systems were early models
of what carsharing could look like in China. The low ratios in both
Chinese cities suggested that carsharing, as priced here, was rela-
tively less competitive with the taxi than in Western cities. The range
of ratios provided perspective on hourly carsharing prices that would
position carsharing in Beijing and Shanghai competitively with taxis.
The median ratio was 1.5, which was reflective of the ratio in a typical
American city. If carsharing prices were to match that ratio in China,
then a competitive hourly rate would range between US$2.00 (¥12.9)
and US$2.35 (¥15.15). This rate was close to that offered by EdoAuto
(26). The company was able to achieve this rate through operation in
lower-density areas of the city, where parking was cheaper and fewer
taxis operated. However, such prices would be less competitive with
those of taxis in city centers, given the high cost to park downtown
(e.g., in Shanghai) (52). Such costs likely influenced the Dazhong
shared-taxi model of carsharing (27).
Although the rapid motorization of China may signal an oppor-
tunity for carsharing, the reality is that Chinese cities present key
obstacles, which include (a) high parking costs, (b) high traffic
congestion, (c) limited driving experience, (d) well-used taxi sys-
tems, and (e) little familiarity with carsharing (15). To evaluate these
macro scopic issues in greater depth, the study reported here explored
the response of Shanghai residents to the carsharing concept and
evaluated how certain factors influenced interest in carsharing. On
the basis of the survey results, the study profiled those interested in
carsharing and explored further how the availability of this service
might affect vehicle-purchasing behavior.
This discussion of methodology includes three main sections:
(a) survey design and administration, (b) carsharing definition, and
(c) study limitations.
Survey Design and Administration
The survey was implemented in Shanghai, China, between
November 1, 2010, and February 1, 2011. Respondents were chosen
randomly from the whole city, according to an address list from previ-
ous projects in Shanghai conducted through Tongji University. Each
respondent was provided a paper survey, along with the option to take
the survey online. A small incentive (i.e., US$3.00 gift card) was given
to respondents that completed the survey. The survey was pretested
at Tongji University, 4,000 surveys were mailed, 271 responses were
received, and the response rate was approximately 7%.
The survey was divided into several parts. The first section
asked basic questions about daily travel, commuting, personal
demographics, and household vehicle holdings. In addition, infor-
mation on work status, employment, age, gender, personal annual
income, and education level were collected. The survey also asked
stated-preference questions about how respondents might use
carsharing for specific trip purposes (e.g., shopping trips, airport
journeys, and weekend family travel).
Carsharing Definition
Respondents were not expected to have any prior exposure to car-
sharing, so explanatory materials were included to carefully explain
the concept. In line with the methodology adopted by Shaheen,
each survey was accompanied by an introductory letter and consent
form, which gave respondents the option to consent to participate
in the research (53). In addition, a two-page brochure was included,
which clearly described the carsharing concept in both visual and
text form. Figure 1 provides a summary of the brochure in English
and Chinese versions.
The survey focused on the neighborhood carsharing model, defined
earlier in the paper. The information presented in the brochure listed
carsharing advantages and disadvantages, including basic opera-
tions and cost parameters. The reservation system was shown as a
TABLE 1 Taxi Rate in Shanghai Metropolitan Area
Taxi Rate in Shanghai Regular Taxi in Central Shanghai Regular Taxi in Suburbs
Initial charge 1.76–2.35 1.47
Free kilometers (miles) with basic charge 3 km (1.87 mi) 3 km (1.87 mi)
Rate per kilometer (mile) $0.31/km ($0.56/mi)–$0.53/km ($0.85/mi) $0.31/km ($0.56/mi)
Rate over 10 km $0.53/km ($0.85/mi)–$0.69/km ($1.11/mi) $0.53/km ($0.85/mi)
Note: Comparison of costs for a taxi trip of 8 km within 30 min and a carsharing trip.
TABLE 2 Taxi Costs in World Cities (5, 7, 8; 26–27; 29–50)
Central City
and Metropolitan
Area Population
Central City
Car Ownership
Population with
Driver License
Shareb (%)
and Free
Regular Rate per
Kilometer (mi)dTotal Taxi
Hourly Coste
Taxi Cost
or Income
Taxi Cost or
Beijing 11.71 3.74 4.75 13,432 66,646 8.1 $1.50 $0.31/km 3.06 2.50f0.023 1.22
19.72 (32.0%) 3 km ($0.5/mi)
Shanghai 9.76 1.03 2.58 14,029 53,199 5.3 $1.76 $0.35/km 3.51 4.00f0.025 0.88
23.02 (10.5%) 3 km ($0.56/mi)
Singapore 5.07 0.61 4.03 75,597 25,176 5.3 $2.10 $0.5/km 5.60 15.00 0.007 0.37
5.07 (12.0%) 1 km ($0.8/mi)
Washington, D.C. 0.60 0.36 0.43 59,290 6,800 0.3 $4.00 $0.94/km 11.25 7.50 0.019 1.50
5.58 (60.0%) 0.27 km ($1.5/mi)
Chicago, Illinois 2.69 1.19 1.83 45,734 6,999 0.3 $2.25 $1.13/km 11.25 7.50 0.025 1.50
9.46 (44.3%) 0 km ($1.8/mi)
New York 8.17 3.86 5.67 55,980 13,087 2.5g$2.50 $1.25/km 12.50 8.50 0.022 1.47
18.97 (47.3%) 0 km ($2/mi)
San Francisco, 0.80 0.35 0.62 55,221 1,381 0.1 $3.10 $1.41/km 13.97 7.50 0.025 1.86
California 4.34 (43.4%) 0.27 km ($2.25/mi)
Paris 2.19 0.32 1.56 58,000 14,900 1.0 $3.00 $1.5/km 15.00 7.00 0.026 2.14
11.84 (14.8%) 0 km ($2.4/mi)
London 7.82 1.88 5.49 59,800 16,210 0.5 $4.40 $1.38/km 15.40 6.40 0.026 2.41
13.90 (24.0%) 0 km ($2.2/mi)
Berlin 3.45 1.43 2.48 67,500 7,000 1.0 $4.50 $2.19/km 22.00 7.50 0.033 2.93
4.43 (41.8%) 0 km ($3.5/mi)
Tokyo 13.01 2.71 10.41 58,000 60,000 3.1 $3.10 $3/km 25.10 20.00 0.043 1.26
35.68 (20.8%) 2 km ($4.8/mi)
aTop cell is population within the city limits; bottom cell is the metropolitan region; data from city websites or within country statistical agencies.
bTransportation data from city websites, accessed in 2011. American cities derived from travel survey data provided by local Metropolitan Planning Organizations.
cBeijing and Shanghai Statistics 2010; the residents who lived in the other cities were required to have driver licenses when they were 18 years old.
dTaxi rates from taxi provider websites, 2011.
eZipcar, 2011; Paris, Berlin, Tokyo, Singapore Carsharing, 2011.
fEdoAuto offered the carsharing vehicle for $2.50/h + gasoline, Dazhong sought to launch carsharing service at approximately $4.
gIncludes only yellow cabs and not car services or black cars.
90 Transportation Research Record 2319
graphic, and respondents were instructed how to reserve and access a
carsharing vehicle (Figure 1).
Study Limitations
Because the neighborhood carsharing model is still a new concept
in China and public exposure to it is limited, the survey could only
explore stated responses to the concept. In addition, the study evalu-
ated a respondent’s preference for carsharing without consideration
of variations in service level. The design attempted to counter non-
response bias through the inclusion of a gift card incentive. As men-
tioned earlier, the overall response rate was 7%, which was low by
traditional standards but reflective of recent survey response rates
achieved in Shanghai (54). Given budget limitations, respondents
were asked to take the survey only once. Several dynamics could
have introduced some selection bias into the data. For example, the
head of household typically completed mail surveys and was likely
to have the highest education in the household. It was also possible
that a survey focused on carsharing might have appealed only to a
FIGURE 1 Summary of information in brochure included with survey.
Wang, Martin, and Shaheen 91
subset of potential respondents. Finally, the survey responses were
stated, rather than revealed, preference, and thus indicative of how
people thought that they would respond to the service. As a result
of these dynamics, the results more likely reflected the views of
potential early adopters, who constituted a subset of the Shanghai
population, and were less generalizable to other regions in China.
This section discusses (a) survey respondent demographics relative
to the current Shanghai population, (b) potential carsharing impacts
on vehicle sales and planned purchases, and (c) results of the ordinal
regression model to further understand carsharing interest among
the survey population.
Respondent Demographics
Respondents exhibited a wide distribution of demographic charac-
teristics. The subjects were divided into subgroups characterized
by their expressed interest in carsharing. At the end of the survey,
respondents were asked: “On a scale of 1 to 10, where 1 is ‘definitely
not’ and 10 is ‘definitely,’ please indicate how likely it is that you
would join carsharing, if it were available to you?” Respondents
whose ratings were 6 or greater were considered to be “interested”
in carsharing (144 of the sample), and respondents whose answers
ranged between 1 and 5 on the rating scale were considered “not
interested” in carsharing (127 of the sample). This division was
relevant to understand how different people reacted to the carsharing
concept. Table 3 illustrates a breakdown of key demographic variables
of these subgroups, along with the overall sample.
TABLE 3 Demographic and Socioeconomic Attributes of Survey Respondents
(6 Preference 10
N = 144)(%)
Not Interested
(Preference 5
N = 127)(%)
Whole Sample
(N = 271)(%)
Less than ¥10,000 (US$1,540) 0 2 1
¥10,000–¥20,000 (US$1,540–US$3,080) 1 0 1
¥20,000–¥30,000 (US$3,080–US$4,620) 3 2 3
¥30,000–¥40,000 (US$4,620–US$6,160) 4 3 4
¥40,000–¥50,000 (US$6,160–US$7,700) 13 13 13
¥50,000–¥70,000 (US$7,700–US$10,780) 22 14 18
¥70,000–¥100,000 (US$10,780–US$15,400) 15 20 17
¥100,000–¥150,000 (US$15,400–US$23,100) 18 16 17
¥150,000–¥300,000 (US$23,100–US$46,200) 16 18 17
More than ¥300,000 (US$46,200) 8 13 10
Primary school 1 0 0
Middle school 3 6 4
High school 15 15 15
Technical or vocational college 15 24 19
University or college (undergraduate) 4 5 4
University or college (graduated) 41 35 38
Graduate/professional 21 16 18
Younger than 21 0 0 0
21–23 4 2 3
24–26 11 12 11
27–30 22 16 19
31–35 31 20 25
36–40 15 16 15
41–45 10 10 10
46–50 4 9 6
51–55 2 9 6
56–60 1 2 2
Older than 60 0 4 2
Household car ownership
0 50 43 47
1 43 46 45
2 6 8 7
3 1 2 1
4 0 1 0
More than 4 0 0 0
Commute mode
No commute 13 15 14
Taxi 8 3 6
Car 23 29 26
Bus 19 14 17
Metro 27 24 25
Cycling 6 6 6
Walking 6 9 7
92 Transportation Research Record 2319
Although population data in China are improving, they still are not
as comprehensive as the U.S. Census (i.e., full distributions of key
population parameters are not published). Still, population statistics are
produced in statistical yearbooks for cities and for the nation, which
provide benchmarks for comparative analysis with the sample. In terms
of income, the sample and population corresponded reasonably well.
The average household income for Shanghai residents was ¥92,170
(US$14,029), whereas the median income category of the sample was
¥70,000 to ¥100,000 (US$10,780 to US$15,400) (41). Variation in
within-sample income existed among the subgroups defined by car-
sharing interest. A Mann–Whitney test to evaluate the differences in
the household-income distributions between the subgroups did not
find a statistically significant difference, however (p = .165).
The distribution of educational attainment indicated that the sam-
ple was more educated than the Shanghai population. Only 24% of
Shanghai residents had an undergraduate education or higher, whereas
23% attended high school, and the remainder had a middle school edu-
cation or lower (55). Within the survey sample, 56% of respondents
(n = 153) had an undergraduate degree or higher. The differences
between the subgroup distributions were nearly (but not) significant
(p = .086), with the distribution of those interested in carsharing
skewed toward higher education levels.
Within the Shanghai population that was 18 years of age or older,
27% were 18 to 34, 49% were 35 to 59, and the remaining 23% were
older than 60 (41). In general, the sample was younger than the popu-
lation: 59% (n = 160) were between the ages of 21 to 35. Furthermore,
the difference in the mean age in the subgroups of those not interested
(38) and interested (34) was statistically significant (p = .000), which
suggested that younger people might be more interested in carsharing.
Nearly 10 million people live in Shanghai, and 1 million private
vehicles are registered there. It is suspected that Shanghai also has a
sizable population of vehicles that are registered elsewhere in China
but driven within city boundaries because of the large, regional varia-
tion in licensing costs. People register a vehicle in one region but
park it in another. Thus the 10% ratio of vehicles to people listed in
Table 1 is considered a lower bound. If each vehicle was owned or
leased by a separate household, then an upper bound on the household
auto-ownership rate would be approximately 30%, given the average
household size of 2.8 in Shanghai (41). The sample showed that 53%
(n = 144) of the households owned a car, which was more than the
population average. About 86% of the sample (n = 234) commuted to
an employment site. The remaining 14% (n = 37) were homemakers or
unemployed. Although the sample was relatively automobile-adapted,
there was no statistical distinction across subgroups in vehicle owner-
ship (p = .488). As Table 1 shows, 26% of Shanghai residents had
a driver’s license, whereas in this sample the proportion was 60%
(n = 163). Thus one clear dimension of survey bias was auto owner-
ship and driving experience, which departed significantly from the
ownership and experience of the general population.
Vehicle Sales and Planned Purchases
In North America, carsharing has been found to reduce the need for
personal auto ownership. To evaluate how Chinese carsharing mem-
bers might alter vehicle ownership, respondents were asked directly:
“If you joined carsharing, do you think that you would sell any
vehicle that you currently own?” Only 11.1% of the respondents that
owned a vehicle in their household stated that they would dispense
with their automobile (16 of 144) if they joined a carsharing program.
This proportion was the same among those households interested
in carsharing (8 of 72). This proportion was smaller than it was in
research done in Europe and North America, which repeatedly found
that nearly 25% of carsharing members gave up a vehicle (10, 11, 13).
The present study reported here also explored whether the availabil-
ity of carsharing might change expected vehicle purchase plans over
the next 5 years. Respondents were asked: “If you joined carsharing,
do you think that you would still buy a car?” Within the entire sample,
32% (n = 87) of respondents were interested both in carsharing and in
planning a vehicle purchase within 5 years. Of those respondents, 51%
(n = 44) stated that they thought that they would give up their purchase
plans if they joined carsharing, which supported the idea that carshar-
ing might be more effectual in China because it obviated household
vehicle purchases.
Ordinal Regression Model: Carsharing Interest
The study evaluated interest in carsharing with an ordinal regression
model. A key advantage of ordinal regression is that the most influ-
ential variables can be isolated, which controls for the influence of
other variables. Table 4 presents the model estimation, with interest
in carsharing as the dependent variable. The question had a 10-point
response scale. However, it was rescaled to 5—two responses were
placed in each ordinal category—to reduce the number of threshold
variables (intercepts). For example, responses of 1 and 2 were rescaled
to 1, while responses of 9 and 10 were rescaled to 5.
Ordinal regression models have three main components: (a) thresh-
old coefficients, (b) covariate coefficients, and (c) factor coefficients.
The threshold coefficients are the constants that are estimated on
the individual logits, which pertain to each ordinal response of the
dependent variable. The covariates are ordinal or interval variables
that exhibit a definable scale. Factors are variables that in general
are categorical. Positive coefficients for covariates and factors
indicated that the variable increased interest in carsharing.
The covariates included seven: household income, education, age,
commute trip time, and three attitudinal questions. Household income
was statistically significant and negative, which indicated that, when
all else was equal, higher income reduced interest in carsharing. The
age coefficient had the same effect. Although age and income often
were correlated, age was not significant by itself. As education rose,
so did the appeal of carsharing. This last result was consistent with
findings in carsharing research done in North America and Europe,
which found that most members had a bachelor’s degree or higher
(9, 11, 13). In addition, a study in Beijing found that those interested
in carsharing were relatively more educated than those that were not
interested (15). Finally, the length of time it took to commute to work
had a positive influence on the interest in carsharing.
The survey in this present study asked respondents attitudinal ques-
tions to evaluate their opinions on climate change and energy secu-
rity, as well as their concerns and preferences with respect to their
use of carsharing. Respondents were asked on a 4-point Likert scale
whether or not they believed “energy security” was more important
than “climate change.” The negative coefficient indicated that respon-
dents that believed China’s energy security was more important than
climate change were less likely to be interested in carsharing. This
effect, however, was weak because the coefficient was not signifi-
cant. The model found that an individual with a high concern for
personal driving safety also was less likely to be interested in carshar-
ing. The final covariate pertained to the importance of carsharing-
vehicle proximity to public transit. Respondents that considered close
proximity important also were more interested in carsharing.
Wang, Martin, and Shaheen 93
Two categorical factors were included in the model. Respondents
were asked to identify the primary mode that they used to shop. The
most significant responses were metro and taxi, which suggested
that people that used the metro as their primary shopping mode
had a higher likelihood of interest in carsharing, whereas those that
identified taxis as their primary mode had less interest. The model
also included vehicle purchase plans. Compared with people with
no vehicle purchase plan, those with long-term purchase plans were
found to be relatively less interested in carsharing. This finding sug-
gested that a near-term plan to purchase a vehicle was not a large
deterrent to the exploration of carsharing.
To evaluate the validity of any ordinal regression model, a “test of
parallel lines” is required. This test evaluates whether the influence of
covariates and factors is appropriately specified by a single coefficient,
or if multiple coefficients for each ordinal response are required. The
analyst does not want to reject a null hypothesis, which was the case
for this model (p = .642). The nonrejection of the parallel lines test
confirms that the complementary log–log link function is the appropri-
ate specification, and a single coefficient value is sufficient to explain
its effect for all of the ordinal values of the dependent variable.
Overall, the survey results suggested that a subpopulation of Shang-
hai residents had an interest in carsharing but also that the inter-
est was governed by several key factors. Interest in carsharing rose
with education level and fell with age, a common pair of attributes
shared by carsharing members across the globe. At the same time,
interest in carsharing declined with higher household income. Other
aspects that drove interest in carsharing included travel patterns and
vehicle purchase plans. Those respondents that used the metro pre-
dominantly to shop were more likely to be interested in carsharing,
whereas those that used taxis were less likely to express interest.
Furthermore, a small share (11%) of households that owned a vehi-
cle stated that they would be willing to shed one, if carsharing were
made available. Those that planned to purchase a vehicle in the near
term (within 1 to 3 years) received the carsharing concept better.
These results, together with the broader, macroeconomic circum-
stances discussed earlier, did not indicate that carsharing would
unequivocally take hold in large Chinese cities were it made avail-
able, at least on the basis of the neighborhood model used in North
America and Europe. Rather, the results reinforced the perception
that neighborhood carsharing might face several challenges to the
attainment of a broad customer base and the rapid membership growth
experienced in the United States. The taxi, which plays a small role
in U.S. transportation systems, is a far more important component of
urban mobility in China. Taxis are able to supply automobility to a
population that does not have much driving experience. With abundant
taxis and rapidly developing public transit networks, it is not imme-
diately evident that widespread driving experience even is needed.
Furthermore, land use and parking costs are high in Chinese cities,
and thus the economics generally are stacked against a business
model that needs to deploy a large number of vehicles throughout a
TABLE 4 Ordinal Regression Parameter Estimates to Predict Carsharing Interest
Among Survey Respondents
Error Probability
Carsharing preference = 1.00 1.828 0.595 .002
Carsharing preference = 2.00 1.233 0.587 .036
Carsharing preference = 3.00 0.089 0.581 .878
Carsharing preference = 4.00 0.853 0.582 .143
Household income $10,000 0.024 0.008 .003
Education 0.095 0.053 .074
Age 0.011 0.008 .173
Commute single trip time 0.006 0.003 .085
Energy security versus global warming in China 0.123 0.083 .139
Worry about getting into an accident when driving 0.255 0.086 .003
Carsharing lots near transit stations and stops 0.148 0.031 0
Major shopping mode = taxi 0.957 0.568 .092
Major shopping mode = private car 0.206 0.186 .267
Major shopping mode = bus 0.247 0.232 .288
Major shopping mode = metro 0.568 0.238 .017
Major shopping mode = bike 0.287 0.237 .227
Major shopping mode = walk 0ana na
Car purchase plan = within 1 year 0.26 0.263 .322
Car purchase plan = within 1–3 years 0.204 0.196 .298
Car purchase plan = within 3–5 years 0.551 0.203 .007
Car purchase plan = within 5–10 years 0.459 0.253 .07
Car purchase plan = no plan within 10 years 0ana na
Note: Summary statistics: number of cases = 271; link function: complementary log–log; model fitting
information (2 log likelihood) = 762.831 (0.000); test of parallel lines, p = .624; prediction attitude
accuracy = 196/271 = 72.3%. na = not applicable.
aThis parameter is set to zero because it is redundant.
94 Transportation Research Record 2319
high-density, urban environment. Finally, Chinese urban highways
are highly congested with traffic, even with comparatively low vehicle
ownership rates. It is unclear for how long China’s existing infra-
structure can manage additional growth. For these and other reasons,
the model of carsharing that might emerge in China could look quite
different from neighborhood carsharing in Europe and North America.
Although carsharing was originally envisioned to get people out of
privately owned vehicles, carsharing in China would most likely get
more people into them.
Certain designs of the neighborhood model might be implemented
to overcome these obstacles, however. Carsharing vehicles might be
more readily deployed within parking garages accessible to residents
of large apartment buildings. This “closed” or “semi-open” neigh-
borhood model would restrict vehicle use to those with access to the
building. China is at a unique point in its industrial development; it is
building cities rapidly at a time when carsharing exists. Throughout
history, new cities have had the advantage (or, in some cases, the dis-
advantage) to form around the prevailing transportation technology of
the age, and carsharing may be more appropriately established in China
through integration with new infrastructure. In such a case, carsharing
could reduce the need for personal vehicles. Unlike elsewhere, how-
ever, the reduced need in China might express itself in fewer vehicles
acquired in the first place, rather than through more vehicles ultimately
shed. In established Chinese cities, the business model that emerges
may offer more value added through shared-mobility services, such as
ridesharing, as opposed to shared vehicles.
Thus it is probable that some form of carsharing will emerge
in China. It is not clear, however, that to copy the neighborhood
model, which has spread across Europe and North America, would
be the most successful approach. Rather, China, with its unique sta-
tus as a large but still-emerging economy, may need to develop a
unique style of carsharing that satiates the increasing demand for
vehicle ownership and mobility even as it complements existing
transportation system constraints.
The authors thank the Chinese Scholarship Council and the Yang
Dongyuan research group in transportation studies at Tongji Uni-
versity, Shanghai, for generous funding of this research through
the university’s endowment for energy-efficient vehicle research
in China. In addition, support was provided by the National Natu-
ral Science Foundation of China. The authors acknowledge Yang
Dongyuan, Hui Ying, Huang Yun, and other graduate students at
Tongji University for their valuable assistance with this study. The
authors also thank Madonna Camel of the Transportation Sustain-
ability Research Center at the University of California, Berkeley,
for her assistance with the human subjects review.
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The contents of this paper reflect the views of the authors and do not necessarily
indicate sponsor acceptance.
The Emerging and Innovative Public Transport and Technologies Committee
peer-reviewed this paper.
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Car sharing has become a new mode of transport during the past two decades in the world. Its rapid growth in China has attracted a wide range of users and posed some problems. The main focus is on service efficiency and user satisfaction. To explore possible service enhancement and management intervention, this study aims at capturing the user characteristics according to different user types and scrutinizing their satisfaction with station-based one-way car sharing service. The study firstly illustrates descriptive statistics of user profile. This is followed by a study of user satisfaction influenced by user rates on staffs, the efficiency of rental process, vehicle situation, the use of credit card and their familiarity towards rental station. Furthermore, by clustering users according to the total travel time and distance during one rent, two different types of users are identified and defined as User Group A (UGA) and User Group B (UGB). To examine how fully do users utilize the shared cars, ANOVA was conducted implying family car ownership, total travel distance and main travel purpose have strong impact on total rental time for UGB, while for UGA, travel purpose and age have strong impact. Finally, ordinal logistic regression was introduced to find that for UGB, "shopping" is the main travel purpose with longer rental time, whereas for UGA, "out for business", "shopping", "visit friends" or "pick up others" are the main travel purposes with longer total travel time. Based on the findings, advices for operators on how to improve service quality and suggestions for government management strategy are discussed, respectively.
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This mixed-methods research provides insights about gender preferences of urban women regarding innovative urban mobility solutions analysing the use of and the attitudes towards free-floating e-carsharing in urban areas from a gender perspective. This work provides the answer to the research question, what motivates urban women to use free-floating e-carsharing, and what measures can help to overcome potential obstacles. Findings from previous literature showed that women take over more often household-related tasks besides accompanying family members partially in addition to making work-related trips. They, therefore, travel considerably more, but shorter trips on average in terms of distance and time. According to the current daily tasks of urban women, their mobility behaviour is more complex than that of men, and mode choice especially differs for new innovative mobility solutions. In addition, previous studies reported that several factors hint towards women showing a higher (at least higher than now) affinity towards sustainable shared e-mobility: This pragmatic, functional and less emotional anticipation of cars of women should, therefore, speak for a high potential of the use of electric vehicles in combination with carsharing. Nevertheless, these findings are not sufficient when it comes to the internationally visible phenomenon of early adopters of innovative and sustainable mobility services such as free-floating carsharing with battery electric vehicles (FFECS): Current users of these services are mostly male. This dissertation project introduces mobility planning as an enhanced form of transport planning due to a distinction between mobility and transport, according to Ahrend et al. (2013). Inline, this study uses a mixed-method approach of empirical social research combining qualitative and quantitative approaches to answer the question of how women can be addressed as a target group for multimodal or intermodal mobility in urban areas, with the focus on the use of free-floating carsharing in combination with electric vehicles. The first empirical part aims for understanding the picture of women who are already using e-carsharing at a very early stage of its market diffusion. A sample of users from Berlin is analysed to gain insights about whether female early adopters have the same characterisation as the internationally homogeneous groups of male early adopters. In a second empirical step, the resulting characterisation is then transferred to a group representative of urban dwellers from urban areas in Germany and differences between men and women, both with and without children, are examined with regard to access to resources, perception and use of different modes and attitudes towards different, but especially sustainable modes of transport. In the last empirical part, the use of modes of transport in a gender-appropriate research design combining qualitative interviews and GPS-tracking data. The corresponding requirements for e-carsharing are examined, and concrete advantages and disadvantages for women are worked out. The results of the quantitative and qualitative studies show the acceptance of women and their different attitudes towards mobility-related aspects. Four social constructs were identified that hinder women from adopting free-floating e-carsharing. These four social constructs build the foundation for developing recommendations for improving the services for urban women regarding policy and service model.
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Carsharing represents an alternative to private vehicles and is becoming internationally recognized as a method of sustainable transportation. Compared with the United States and countries in Europe, carsharing services in China started later and were initially underwhelming. With the revival and popularity of the sharing economy, carsharing has been thriving in China in recent years but remains in an initial stage. Understanding the determinants of people’s intentions to carshare is critical for the promotion of carsharing services. The theoretical framework of this research is an expanded version of the theory of planned behavior containing environmental concern. A questionnaire was created to empirically test the model and a total of 1165 valid surveys were collected in four new first-tier cities in China. The intention to use carsharing was found to be directly affected by attitude, subjective norm, and perceived behavioral control rather than environmental concern. However, people’s environmental concern was verified as indirectly impacting their intentions to use carsharing through attitude, subjective norm, and perceived behavioral control. In addition, this study also tested the moderating effect of car ownership, age, gender and income by adopting a multi-group analysis. The results confirm the moderating effect of car ownership and gender on people’s intention to use carsharing, revealing the differences that exist between people with private cars and those without as well as the differences between the male and female gender. The moderating effects of age and income on people’s intention to use carsharing were found to be insignificant. These findings provide practical insights for carsharing organizations and transportation departments. The limitations of this study and suggestions for further research are also discussed.
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Over the past 20 years, China has experienced a steady decline in bicycle use. To address this trend, China's central and local government for urban transportation created Public Transit Priority to encourage public transport initiatives. As part of this effort, the government of the city of Hangzhou launched Hangzhou Public Bicycle in 2008. This service allows members to access a shared fleet of bicycles. As of March 2011, Hangzhou Public Bicycle operated 60,600 bicycles with 2,416 fixed stations in eight core districts. To understand factors leading to bikesharing adoption and barriers to adoption, the authors conducted an intercept survey in Hangzhou between January and March 2010. Two separate questionnaires were issued to bikesharing members and nonmembers to identify key differences and similarities between these groups. In total, 806 surveys were completed by 666 members and 140 nonmembers. The authors found that bikesharing was capturing modal share from bus transit, walking, autos, and taxis. Approximately 30% of members had incorporated bike-sharing into their most common commute. Members indicated that they most frequently used a bikesharing station closest to either home (40%) or work (40%). These modal shifts suggested that bikesharing acted as both a competitor and a complement to existing public transit. Members exhibited a higher rate of auto ownership than nonmembers. This finding suggested that bikesharing was attractive to car owners. Recommendations for improving bikesharing in Hangzhou included adding stations and real-time bike and parking availability technologies, improving bike maintenance and locking mechanisms, and extending operational hours.
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Carsharing (or short-term auto use) organizations provide members access to a fleet of shared vehicles on an hourly basis, reducing the need for private vehicle ownership. Since 1994, 50 carsharing programs have been deployed in North America—33 are operational and 17 defunct. As of July 1, 2008, there were 14 active programs in Canada and 19 in the United States, with approximately 319,000 carsharing members sharing more than 7,500 vehicles in North America. Another six programs were planned for launching in North America by January 2009. The four largest providers in the United States and Canada support 99% and 95.2% of total membership, respectively. A 10-year retrospective examines North America's carsharing evolution from initial market entry and experimentation (1994 to mid-2002) to growth and market diversification (mid-2002 to late 2007) to commercial mainstreaming (late 2007 to present). This evolution includes increased competition, new market entrants, program consolidation, increased market diversification, capital investment, technological advancement, and greater interoperator collaboration. Ongoing growth and competition are forecast. Rising fuel costs and increased awareness of climate change likely will facilitate this expansion.
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Four years after the introduction of City CarShare in the San Francisco, Bay area in California, 29% of carshare members had gotten rid of one or more cars, and 4.8% of members’ trips and 5.4% of their vehicle miles traveled were in carshare vehicles. Matched-pair comparisons with a statistical control group suggest that, over time, members have reduced total vehicular travel. However, most declines occurred during the first 1 to 2 years of the program; 3 to 4 years after City CarShare’s inauguration, earlier declines had leveled off. Because many carshare vehicles are small and fuel-efficient but can carry several people, the trend in per capita gasoline consumption also is downward. Mindful of the cumulative costs of driving, carshare members appear to have become more judicious and selective when deciding whether to drive, take public transit, walk, bike, or even forgo a trip. Coupled with reduced personal car ownership, these factors have given rise to a resourceful form of automobility in the San Francisco Bay area.
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Carsharing has grown considerably in North America during the past decade and has flourished in metropolitan regions across the United States and Canada. The new transportation landscape offers urban residents an alternative to automobility, one without car ownership. As carsharing has expanded, there has been a growing demand to understand its environmental effects. This paper presents the results of a North American carsharing member survey (N = 6,281). A before-and-after analytical design is established with a focus on carsharing's effects 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 number of vehicles per household of the sample drops from 0.47 to 0.24. Most of this shift constitutes onecar households becoming carless. The average fuel economy of carsharing vehicles used most often by respondents is 10 mi/gal 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 and 130,000 vehicles off the road. This equates to 9 to 13 vehicles (including shed autos and postponed auto purchases) taken off the road for each carsharing vehicle.
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Rising auto ownership in China brings significant urban and environmental challenges. Since China is still in the early stages of motorization, there are opportunities to introduce alternatives to personal vehicle ownership. The authors conducted a survey with 800 Beijing residents, collecting data on transportation patterns, automobile ownership, environmental attitudes, and carsharing response. Fifteen of those participants were selected to complete an in-depth questionnaire discussing how they would use carsharing services. This paper assesses the potential for carsharing systems within Beijing, China, based on this exploratory study. While the results suggest that carsharing models integrated into existing transit networks could become an important mobility option within China's rapidly growing cities, further study is recommended.
In recent years, there has been significant worldwide activity in shared-use vehicle systems (i.e., carsharing and station cars). Much of this activity is taking place in Europe and North America; however, there has also been significant activity in Asia, primarily in Japan and Singapore, with some planned activity in Malaysia. The latest shared-use vehicle system activities in Japan and Singapore are examined, beginning with a historical review followed by an evaluation of their current systems. Overall, there are several well-established systems in Japan (18 systems having approximately 176 vehicles and 3,500 members) and Singapore (four systems having approximately 432 vehicles and 12,200 members). A new program was planned to launch in spring 2006 in Kuala Lumpur, Malaysia, with 10 vehicles. In contrast to most European and North American cities, Japan and Singapore already have a wide range of viable public transportation modes. The primary carsharing focus in Japan is on business use, and in Singapore, on residential–neighborhood use. This likely is because of limited vehicle licensing and high car-ownership costs in Singapore. Further, systems in Japan and Singapore have a high degree of advanced technology in their systems, making the systems easy to use and to manage. The member–vehicle ratios in Asia appear to be approximately the same as in Europe and Canada and less than in the United States. It is expected that Asian shared-use vehicle systems will continue to have steady growth in number of organizations, vehicles, and users.
Nine months into the introduction of car sharing in San Francisco, California, an estimated 7% of members' trips and more than 20% of vehicle miles traveled were by shared-use vehicles. Evidence suggests that access to shared cars is stimulating motorized travel. Most members do not own cars, and many appear to be leasing vehicles in lieu of walking and biking. Car-share vehicles are used more for personal business and social-recreational travel than for nondiscretionary, routine travel such as to work or school. Shared cars are generally not used during peak periods or to dense settings well served by transit, such as downtown. In this sense, car sharing appears to be stimulating a resourceful form of judicious automobility. Users are accruing substantial travel-time savings and willingly pay market prices for these benefits.
The Transportation Energy Data Book: Edition 30 is a statistical compendium prepared and published by Oak Ridge National Laboratory (ORNL) under contract with the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Vehicle Technologies Program. Designed for use as a desk-top reference, the Data Book represents an assembly and display of statistics and information that characterize transportation activity, and presents data on other factors that influence transportation energy use. The purpose of this document is to present relevant statistical data in the form of tables and graphs. The latest edition of the Data Book is available to a larger audience via the Internet ( This edition of the Data Book has 12 chapters which focus on various aspects of the transportation industry. Chapter 1 focuses on petroleum; Chapter 2 energy; Chapter 3 highway vehicles; Chapter 4 light vehicles; Chapter 5 heavy vehicles; Chapter 6 alternative fuel vehicles; Chapter 7 fleet vehicles; Chapter 8 household vehicles; Chapter 9 nonhighway modes; Chapter 10 transportation and the economy; Chapter 11 greenhouse gas emissions; and Chapter 12 criteria pollutant emissions. The sources used represent the latest available data. There are also three appendices which include detailed source information for some tables, measures of conversion, and the definition of Census divisions and regions. A glossary of terms and a title index are also included for the reader s convenience.
The Transportation Energy Data Book: Edition 11 is a statistical compendium prepared and published by Oak Ridge National Laboratory (ORNL) under contract with the Office of Transportation Technologies in the Department of Energy (DOE). Designed for use as a desk-top reference, the data book represents an assembly and display of statistics and information that characterize transportation activity, and presents data on other factors that influence transportation energy use. The purpose of this document is to present relevant statistical data in the form of tables and graphs. Each of the major transportation modes - highway, air, water, rail, pipeline - is treated in separate chapters or sections. Chapter 1 compares U.S. transportation data with data from seven other countries. Aggregate energy use and energy supply data for all modes are presented in Chapter 2. The highway mode, which accounts for over three-fourths of total transportation energy consumption, is dealt with in Chapter 3. Topics in this chapter include automobiles, trucks, buses, fleet automobiles, Federal standards, fuel economies, and household data. Chapter 4 is a new addition to the data book series, containing information on alternative fuels and alternatively-fueled vehicles. The last chapter, Chapter 5, covers each of the nonhighway modes: air, water, pipeline, and rail, respectively.