Factors underlying vehicle ownership reduction among carsharing users: A repeated cross-sectional analysis

Transportation Research Part D 76 (2019) 123–137

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

Factors underlying vehicle ownership reduction among carsharing users: A repeated cross-sectional analysis Daejin Kima, Yujin Parkb, Joonho Koc, a b c

T

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School of Civil and Environmental Engineering, Georgia Institute of Technology, 790 Atlantic Dr NW, Atlanta, GA 30318, United States Department of City and Regional Planning, The Ohio State University, 275 W Woodruff Avenue, Columbus, OH 43210, United States Graduate School of Urban Studies, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul 04763, South Korea

A R T IC LE I N F O

ABS TRA CT

Keywords: Carsharing Vehicle ownership impact Vehicle disposal Repeated cross-sectional study Market growth

This study identifies and compares factors that drive carsharing users’ vehicle ownership changes, using repeated cross-sectional surveys conducted in the early (2014) and mature (2018) phases of the carsharing program in Seoul, Korea. Launched in 2013, the carsharing program has experienced a rapid growth in rental facilities and membership. Two types of mixed-effect logistic regression models are developed to predict the reduction in car ownership and the decision to defer a car purchase among carsharing members, respectively, for each phase, and the changes in estimated coefficients between two phases are evaluated statistically. Results show that about 31% of the members reduced their current or potential car ownership in favor of shared automobility in both phases, while the proportion of those who shed a private vehicle almost doubled from 2.3% to 4.3%. Carsharing’s impact on vehicle ownership reduction appears to be extended to more general cohorts of the membership (e.g., higher income, larger households) as the program expands and ages. While accessibility to carsharing services is consistently important for deciding against vehicle ownership, the relative importance of satisfaction with customer services, accident claims process, and rental charges increased significantly between 2014 and 2018. Those who choose shared vehicles for business or commute trips are more willing to decrease their vehicle ownership than those using the service for non-work trips. These findings help develop a clear understanding of carsharing users’ behavioral changes in terms of car ownership following the growth of carsharing services, with insights on the direction of further service improvement.

1. Introduction Since the launch of the first modern carsharing program in the mid-1990s, carsharing has become an emerging transportation mode in many cities as an important travel demand management strategy. As of 2010, carsharing was operating in more than 1100 cities, in 26 countries worldwide, with over 1.2 million members and 31 thousand shared vehicles worldwide (Shaheen and Cohen, 2013). As a shared automobility option, carsharing operations are likely to have profound impacts on personal mobility decisions, particularly decisions related to car ownership (Cohen and Shaheen, 2016). This notion naturally raises a question: how many carsharing users would be willing to forgo their possessed cars in exchange for shared automobility? Most previous studies have shown that people who have used a carsharing service are more likely to reduce their private vehicle

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Corresponding author. E-mail addresses: [email protected] (D. Kim), [email protected] (Y. Park), [email protected] (J. Ko).

https://doi.org/10.1016/j.trd.2019.09.018

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

Transportation Research Part D 76 (2019) 123–137

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holdings or delay car ownership than those without a carsharing experience (Becker et al., 2018; Cervero et al., 2007; Giesel and Nobis 2016; Klincevicius et al. 2014; Ko et al., 2019; Martin et al. 2010). However, much less attention has been paid to the question of who is more willing to alter their vehicle ownership among carsharing members. Not all carsharing members decide to discard their personal car, while some users even tend to increase their motorized travel by carsharing for trips otherwise made by transit or active modes (Cervero and Tsai 2004; Firnkorn and Shaheen, 2016). Moreover, there is very limited research that explores the roles of carsharing usage patterns and service satisfaction in explaining carsharing users’ heterogenous car ownership decisions. This study examines different propensities of those who use carsharing services for shedding a private vehicle as functions of multiple factors, including individual demographic and socioeconomic traits, household status, carsharing usage and motivations, and service satisfaction. It is also important to consider the time dependency of the relationship between user attributes and car ownership to provide a better understanding of the situation (Firnkorn and Shaheen, 2016). Many existing studies on carsharing impact either dealt with relatively young systems or evaluated such impacts at a single point in time (Becker et al., 2018; Namazu and Dowlatabadi, 2018). In this case, the results may reflect the characteristics of a subset of potential users, for example, early adopters or carless people registered within a few years from program start (Firnkorn and Shaheen, 2016; Le Vine and Polak, 2019). Carsharing impacts may also vary across different stages of development, partly because carsharing services in the early stages tend to serve busy areas first, and gradually spread out to less prioritized areas. The present study attempts to shed additional light on carsharing members’ different car ownership behavior with a repeated cross-sectional survey approach. Based on two identical surveys implemented within a three-year gap for a city-wide carsharing program in Seoul, South Korea, the study identifies factors influencing vehicle ownership change of carsharing members, and compares the analysis results obtained from the relatively early and matured stages of the carsharing. For each of the periods, two statistical models are developed that predict whether the carsharing user has removed his/her own car (‘change in vehicle holdings’) and whether the user intends to forgo or defer a car purchase (‘change in vehicle purchase plan’). 2. Literature review Investigation of how carsharing affects changes in car ownership has been an important research topic in the field of carsharing, as carsharing services have often been introduced to curb private vehicle use. In one such early study, Cervero and Tsai (2004) found that about 29% of carsharing users in San Francisco shed at least one personal vehicle after carsharing participation, with about 67% being willing to give up a car purchase in the future. Based on an extensive case study of roundtrip-based carsharing programs in Europe and North America, Shaheen and Cohen (2007) estimated that the number of private cars removed per shared vehicle ranged between 6 and 23 (North America) and 4 and 10 (Europe), adding that such impacts on vehicle ownership may differ by urban context. The number of private vehicles removed off the roads per shared vehicle designed for round trips is estimated to be about 22.8 in Philadelphia, consisting of 10.8 shed cars and 12 deferred car purchases (Lane, 2005), and 28.4 in London, combining 8.6 cars removed and 19.8 cars avoided (Carplus, 2015). Among a few studies on carsharing impacts in Asian cities, Ko et al. (2019) found that a single vehicle shared for round trips in Seoul substituted for 3.3 private vehicles. In a stated preference study in Beijing, China, Yoon et al. (2017) found that about 25% of Beijing population are willing to postpone a new car purchase, and around 6% would abandon a car purchase plan if carsharing services are made available. Exploring the early effect of free-floating service1 on vehicle holdings in Ulm, Germany, Firnkorn and Müller (2011) reported that the service had the potential to replace 19.2 private vehicles per shared vehicle. A group of studies attempted to quantify the differences in car ownership behavior between carsharing members and nonmembers (i.e., the general population). These studies found that those who hold carsharing membership are, ceteris paribus, more likely to reduce household vehicle holdings than those who do not, based on a panel sample of two cohorts (Becker et al., 2017) or a pooled sample of carsharing members and non-members as a control group (Cervero et al., 2007; Firnkorn and Müller, 2011; Jorge and Correia, 2013; Shaheen and Cohen, 2013). Some studies also investigated the curbing effect of carsharing, showing that carsharing members are more likely to forgo a car purchase plan than non-members (Becker et al., 2017). While holding carsharing membership implies at least some level of carsharing experience, membership itself denotes a heterogenous group of people and not all of them are content with carsharing services and related system elements (Becker et al., 2017; Liao et al., 2018). However, the association between user satisfaction with system attributes and vehicle disposal decisions has rarely been analyzed in the literature (Table 1). Only a few studies examined the influence of user satisfaction levels on rental service components, using stated preference data (Kim et al. 2015) or revealed preference based on a young, round-trip carsharing service (Ko et al., 2019). Different carsharing service types, such as round-trip only versus free-floating services, may also affect vehicle ownership. Namazu and Dowlatabadi (2018) found that carsharing members who use both round-trip and free-floating services were more likely to dispose personal vehicles than those who only use free-floating services. A better understanding of service attributes and their association with behavioral change among carsharing users is needed to inform the design of effective carsharing programs. Because carsharing systems tend to evolve fast, some studies attempted to monitor the maturation of local carshare programs over time to understand the time dependency of their impacts on vehicle ownership (Becker et al., 2017; Bewick et al., 2016; Cervero et al. 2007). Cervero et al. (2007) examined the longer-term impacts of the City Carshare program in San Francisco on car ownership and 1 Station-based one-way service means that vehicles are returned to a reserved parking location different from the origin station. Free-floating service deploys vehicles that do not have to be returned to a specific station but rather parked anywhere within a certain zone.

124

125

RT, OT

Mishra et al. (2015)

2001

RT (1997) OT (2011)

RT (March 2009), FF (June 2011)

December 2014

n/a

n/a

April 2009

Various

August 2014

2001

Launch

Cross-sectional survey in 2010–2012 California Household Travel Survey

Cross-sectional survey in December 2013

Cross-sectional survey in March 2014 and 2015

Cross-sectional survey in 2014 Cross-sectional survey in March 2015

Cross-sectional Survey in June 2016

Cross-sectional survey in 2009

Repeated cross-sectional surveys in 2001, 2003, 2005 Two-wave panel survey (6 weeks & 1-year after system launch) Cross-sectional survey in late 2008

Data

Note: RT = round-trip; OT = one-way trip; FF = free-floating; P2P: peer-to-peer.

RT, OT

RT, OT

Liao et al. (2018)

Namazu and Dowlatabadi (2018)

RT, OT, FF

Firnkorn and Müller (2011)

RT, OT, FF

RT

Martin et al. (2010)

Giesel and Nobis (2016)

RT, OT, FF

Becker et al. (2018)

P2P, RT, OT RT, OT, FF

RT

Cervero et al. (2007)

Nijland and van Meerkerk (2017) Le Vine and Polak (2019)

Service

Literature

Table 1 Studies investigating the impacts of carsharing participation on car ownership.

Observed vehicle disposal Observed differences in vehicle holdings

San Francisco, California, USA

Carsharing users (N = 241) & Non-users (N = 8299)

Observed & anticipated vehicle disposal

Observed & anticipated vehicle disposal Observed & anticipated vehicle disposal

Anticipated vehicle disposal

Observed & anticipated household vehicle disposal Anticipated vehicle disposal

Observed vehicle disposal Observed & anticipated vehicle disposal

Dependent variable

Vancouver, Canada

Berlin and Munich, Germany

London, UK

Netherlands

Netherlands

Ulm, Germany

Multiple cities in Canada and USA

San Francisco, California, USA Basel, Switzerland

Location

Carsharing users (N = 3040)

Carsharing users (N = 988)

Carsharing users (N = 363) Carsharing users (N = 298)

Prospective carsharing users & non-users (N = 308) Prospective Users (N = 1003)

Carsharing users & Nonusers (N = 530) Carsharing members & Non-members (N = 790) Carsharing users (N = 6281)

Respondents

Gender, college education, household vehicle holdings, income, presence of young children, private car usage patterns, carsharing usage patterns, transit use, trip purpose Use frequency of private car versus shared car, household size, high-school diploma, transit use, attitudes, home location Housing type, household size, vehicle holdings, membership duration, use frequency, trip purpose, home relocation, carsharing purpose Residential density, income, education, age, commute distance, gender, children, number of drivers, carsharing membership

Membership fee, rental fee, access time to rental stations, vehicle availability, vehicle fuel type, parking space type n/a

n/a

Age, accessibility to rental stations, young children, carsharing membership Household income, transit travel card, home location, college degree, household size, carsharing membership Gender, Age, Education, Income, vehicle age, vehicle mileage

Explanatory variable

D. Kim, et al.

Transportation Research Part D 76 (2019) 123–137

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vehicle miles traveled (VMT) with repeated cross-sectional surveys conducted in 2001, 2003, and 2005. The study was intended to examine whether the patterns found in earlier studies that dealt with short-term impacts of the program were still valid 4 years after the program’s inauguration. They found that carsharing stimulated travel among members at the beginning, but steadily reduced motorized travel 2 years into the program, and after 4 years, members’ VMT decreased noticeably from earlier levels. As carsharing operation evolved over time, members were 12% more likely to dispose of a car than non-members. Other than this study, repeated cross-sectional surveys have been rarely utilized, as can be seen in Table 1, which presents previous research efforts that investigated the impacts of carsharing participation on car ownership. This study adds to the literature with a case study of Seoul, Korea, applying two repeated cross sectional data collected 2 years and 5 years after service launch. This study employs random-parameter logistic regression that incorporates random utility functions into discrete choices to account for individual heterogeneity in car ownership behavior. This is needed because the carsharing membership in Seoul has significantly diversified in recent years, with over one million members registered as of March 2018. 3. The carsharing program in Seoul As a way to address increasing demand on private vehicle trips and provide an alternative transportation mode for people without cars, the government-led carsharing program in Seoul began its first operation in February 2013. Five private companies agreed to participate in the program, providing station-based round-trip carsharing services. Service users had convenient access to shared cars through mobile, web, and smartphone reservation systems. The rental stations were strategically located in the vicinity of subway stations and commercial/business districts. The Seoul Metropolitan Government (SMG) allowed the private operators to use cityowned parking lots as their rental locations at a 50% discounted rate. Membership is open to anyone with a driver’s license at no fee. Rental rates vary depending on mileage and time. The minimum rate of around USD 0.2 per km and USD 1.0 per 10 min is applied, and the charges are in most cases cheaper than a taxi fare for a trip with the same distance. Since the launch of the program, the carsharing service has rapidly expanded, serving most areas of Seoul as of January 2018 (Fig. 1). Starting with 492 operating vehicles at 292 rental locations and a membership of 58,869, the program has scaled up its size significantly over the past five years: 1922 shared cars at 895 stations in November 2014, and 4259 cars at 1366 stations in January 2018 (Table 2). As of January 2018, the program claimed approximately 1,933,000 (1.9 million) registered members, representing a 452.3% increase from November 2014 (349,999 members). In line with this expansion, about 6200 daily rentals were made in January 2018, representing a 106.7% increase from 3000 daily rentals in November 2014. The program’s member-vehicle ratio was 454:1, indicating around 454 customers were being served per vehicle, with a 150.8% increase from November 2014 (181:1). In the meantime, the service flexibility was also greatly improved as the operators started one-way trip services as of October 2015, with an

Fig. 1. Carsharing stations in Seoul, Korea, in 2014 and 2018. 126

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Table 2 The Carsharing Service Growth in Seoul, Korea, from February 2013 (launch) to January 2018. Time

Stations

Vehicles

Membership1

Daily rentals

Service2

February 2013 November 2014 July 2015 December 2016 January 2018 % Growth (2014–2018)

292 895 1052 1438 1366 52.6%

492 1922 2356 4003 4259 121.6%

58,869 349,999 655,000 1,348,402 1,933,000 452.3%

349 3000 3900 5956 6200 106.7%

RT RT RT RT, OT RT, OT –

1 The numbers include duplicate members across the operators. If adjusted using the estimated share of duplicate subscribers, the size of membership is estimated to be about 264,000 in November 2014 and 1.3 million in January 2018. 2 RT = round trip; OT = one-way trip.

additional charge estimated according to the drop-off location. In comparison, the US carsharing market has 1,405,447 members as of January 2017, sharing 17,178 round-trip and one-way vehicles among 21 operators (Shaheen et al., 2018). This rapid growth of the carsharing program in Seoul has been possible with the government’s official support. Under the agenda of ‘Sharing City Seoul’ announced in 2012, the SMG has used spending programs, public-private partnership, and city legislation to provide financial and infrastructure support for the adoption of carsharing among citizens. The support includes user subsidies for discounted service rates; operator subsidies for renting parking spaces, purchasing electric vehicles and installing chargers; dedicated parking spots for shared vehicles; station installation in residential and business areas; integrated online reservation and payment platform; administrative enforcement and public relations. 4. Data 4.1. Two cross-sectional surveys An online survey was carried out over two weeks in November 2014 and March 2018, respectively, through a website managed by carsharing service companies. Email invitations including a link to the website were distributed to the carsharing members registered at the time of each survey. The invitation recipients were informed that they would be given a rental voucher of 30 min after survey completion. Finally, a total of 5598 members in the 2014 survey and 9498 members in the 2018 survey responded with complete answers, representing an approximate response rate of 1.6% (=5598/349,999) in 2014 and 0.5% (=9498/1,933,000) in 2018. It was not possible to assess how representative the respondents were of the whole membership because of the lack of access to the information on entire member characteristics. Nevertheless, the characteristics of respondents appear to be in accordance with the general trends known in the literature on carsharing (such as the larger proportions of young, single, or male members) (Table 3). It was also not possible to estimate how many respondents completed both 2014 and 2018 surveys since the access to any identifiable information of the survey participants was not allowed. However, given the massive size of the carsharing membership to which the surveys were distributed, the number of users who participated in both is assumed to be very small. In the surveys, respondents were asked about (1) vehicle ownership preferences after using carsharing services, (2) carsharing usage patterns including frequency of use and trip purposes, (3) the level of satisfaction with a range of carsharing system components on the Likert scale of one (very dissatisfied) to five (very satisfied), and (4) demographic and socioeconomic characteristics. Regarding vehicle ownership preferences, respondents were asked to answer: (1) whether they disposed of their privately-owned vehicles while using carsharing and whether the decision was induced by carsharing, and (2) whether they gave up or postponed a vehicle purchase after carsharing participation. For those who decided to postpone a purchase, further questions followed about how long they were willing to do so: 1–2 years, 3–4 years, 5–10 years, or 10 years or longer. Some of the survey questions asked how often and why they used carsharing. A list of primary motivations for carsharing includes the need for additional cars, cost savings, environmental concerns, car maintenance burden, and recommendation from others. Regarding carsharing use frequency, six options were provided: (1) less than once a month, (2) once a month, (3) 2–3 times per month, (4) once a week, (5) 2–3 times per week, and (6) more than four times a week. As stated earlier, this study develops two different statistical models, one predicting the probability of carsharing members shedding at least one private vehicle in favor of using shared vehicles, and the other predicting the length of deferring a vehicle purchase. These two models require different segments of the survey respondents. Since only vehicle owners are able to shed a vehicle, the sample for the vehicle disposal model consists of those who owned at least one vehicle before using carsharing services. Therefore, the final sample size of this model is 1982 for the 2014 model, and 3447 for the 2018 model. For the vehicle purchase plan model, the sample was made up after dropping those respondents who removed their cars while carsharing. As a result, the vehicle purchase plan models were developed based on 5468 (2014) and 9090 (2018) respondents, respectively. 4.2. Respondent characteristics The socioeconomic traits of the respondents are summarized in Table 3. The table shows that the demographic composition and household characteristics of the respondents are mostly consistent between two years and five years into the carsharing program. In 127

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Table 3 Characteristics of survey respondents. Category

Description

2014 (N = 5598)

2018 (N = 9498)

Population proportion (%)a,b

Male Female

4629 (82.7) 969 (17.3)

7334 (77.2) 2164 (22.8)

49 51

Age

Below 20 20–29 30–39 40–49 50–59 Over 60

26 (0.5) 2957 (52.8) 1976 (35.3) 496 (8.9) 127 (2.3) 16 (0.3)

23 (0.2) 4687 (49.3) 3419 (36.0) 1033 (10.9) 299 (3.1) 37 (0.4)

– 18 22 24 16 21

Monthly household income (KRW 1 million; USD 850)

Below 1 1–2 2–3 3–5 5–10 Over 10

733 (13.1) 1166 (20.8) 1617 (28.9) 1243 (22.2) 653 (11.7) 186 (3.3)

1117 (11.8) 1741 (18.3) 2701 (28.4) 2331 (24.5) 1167 (12.3) 441 (4.6)

5 11 16 35 33

Household size

1 2 3 4 Over 5

1927 (34.4) 1184 (21.2) 1098 (19.6) 1129 (20.2) 260 (4.6)

3074 (32.4) 2336 (24.6) 1887 (19.9) 1736 (18.3) 465 (4.9)

18 22 24 16 2

Housing type

Apartment Multifamily (type 1)c Multifamily (type 2)d Single family Others

2042 (36.5) 733 (13.1) 2013 (36.0) 686 (12.3) 124 (2.2)

3053 (32.1) 1408 (14.8) 3898 (41.0) 1136 (12.0) 3 (0.0)

45 19 36 1

None 1 2 Over 3

3718 (66.4) 1423 (25.4) 396 (7.1) 61 (1.1)

6361 (67.0) 2341 (24.6) 663 (7.0) 133 (1.4)

37 50 12 1

Less than once a month Once a month 2–3 times a month Once a week 2–3 times a week 5 times or more a week

1407 (25.1) 1706 (30.5) 1871 (33.4) 375 (6.7) 204 (3.6) 35 (0.6)

1834 (19.3) 2014 (21.2) 3792 (39.9) 903 (9.5) 836 (8.8) 119 (1.3)

– – – – – –

Main purpose of using shared car

Commute Business Shopping Recreation/Leisure/Meals Others (e.g. personal errands, pickup, etc.)

584 (10.4) 1293 (23.1) 211 (3.8) 2105 (37.6) 1405 (25.1)

532 (5.6) 2171 (22.9) 868 (9.1) 3300 (34.7) 2627 (27.7)

– – – – –

Main travel mode before joining carsharing (weekday)

Personal car Public transit (bus and subway) Taxi Rental car Others

600 (10.7) 4052 (72.4) 475 (8.5) 400 (7.1) 71 (1.3)

1079 (11.4) 6517 (68.6) 1139 (12.0) 532 (5.6) 231 (2.4)

24 65 7 4

Main travel mode before joining carsharing (weekend)

Personal car Public transit (bus and subway) Taxi Rental car Others

805 (14.4) 3555 (63.5) 414 (7.4) 751 (13.4) 73 (1.3)

1318 (13.9) 5711 (60.1) 1268 (13.4) 950 (10.0) 251 (2.6)

24 65 7 4

Socioeconomic characteristics Gender

Household car ownership

Carsharing usage patterns Carsharing use frequency

a The data are as of 2014 from the Korea Statistical Information Service (KOSIS), except for the data on household size and household vehicle holdings for 2010, which are the most recent data available from 2010 Korea Census. b The data on mode share are obtained from the Seoul Open Data Portal (SMG, 2016). c Type 1 multifamily housing refers to condominiums, studio apartments, and mixed-use buildings. d Type 2 multifamily housing refers to semi-detached houses. Generally, minimum parking requirements are rarely enforced for this type of housing, causing parking shortage issues.

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2014, most of the surveyed users were males (82.7%) and under the age of 40 (88.1%), and these percentages were similar in 2018 (77.2% and 85.3%, respectively), implying that young males consistently account for a majority of carsharing users. In terms of household size, single-person households represent the largest group (34.4% in 2014 and 33.6% in 2018), greater than its proportion among the general population (18%), followed by two-person households (21.2% in 2014 and 24.6% in 2018). The distribution of respondents by household income is rather skewed toward lower income levels compared with that of the general population. About 67% of respondents are from zero-car households, while 37% of the households in Seoul are carless. It is also noteworthy that multifamily housing type 2 (i.e., semi-detached housing) represents the most common residence type among respondents (36% in 2014 and 41% in 2018), much higher than the 19% among the general population. This implies that a shortage of residential parking spaces, the typical problem facing type 2 multifamily housing in Seoul, may lead to a greater need of shared automobility. These descriptive findings are in agreement with those of previous studies that found that carsharing services tended to serve a fairly distinct and unique market: moderate-to-lower income, non-traditional households who reside in dense urban areas without cars (Cervero et al. 2007; Lane, 2005). While individual and household characteristics of the respondents were similar between 2014 and 2018, the way that respondents used carsharing services changed from 2014 to 2018. A greater proportion of respondents were frequent users of carsharing in 2018 than in 2014. In 2014, about 11% of the respondents used carsharing at least once a week, and the proportion substantially increased to 19.6% in 2018. The proportion of occasional users (once or less than once a month) decreased from 55.7% in 2014 to 40.5% in 2018. As the main purpose of carsharing travel, an increased percentage of respondents chose shopping (3.8–9.1%), while the percentage of respondents selecting commuting decreased (10.4–5.6%). Still, recreation/leisure trips represented the largest category (37.6–34.7%). A large proportion of respondents used to travel by public transit before participating in carsharing (72.4% in 2014, and 68.6% in 2018) than by personal car (10.7% and 11.4%, respectively). Interestingly, more respondents were taxi users, particularly for weekend trips, in 2018 (13.4%) than in 2014 (7.4%), possibly because more flexible forms of travel became available with shared vehicles, such as one-way trips, in 2018. 4.3. Car ownership changes According to the surveys, about 31% of Seoul’s carsharing users (1737 out of 5598 in 2014, and 3026 out of 9498 in 2018) decided to postpone or abandon car ownership after participating in carsharing. Fig. 2 shows the distribution of these members by decision type and duration of delay. Those who would postpone a car purchase for 1–2 years account for the largest proportion of these members (45.6% in 2014, and 40.6% in 2018). It is noteworthy that the proportion of those who had already shed their personal vehicle primarily due to carsharing almost doubled from 7.5% in 2014 to 13.6% in 2018, which are equivalent to 2.3% (in 2014) and 4.3% (in 2018) of all respondents. Given the size of membership in 2014 (264,000) and in 2018 (1,300,000 adjusted for duplicate membership holders), the potential number of personal vehicles substituted with carsharing services is estimated to be about 6072 in 2014, and 56,167 in 2018. In addition, considering the number of shared vehicles in operation in 2014 (1922) and in 2018 (4259), it is estimated that one shared vehicle replaced 3.2 (in 2014) and 13.2 (in 2018) private vehicles in Seoul. Obviously, the replacement rate seems to have significantly increased during the three years of market growth. In the same vein, the proportion of respondents who decided to give up an additional vehicle in the future increased from 3.6% in 2014 to 7.3% in 2018. 4.4. Satisfaction with carsharing service Overall, the carsharing members appear to be reasonably content with the carsharing service, with the average satisfaction scores

50%

Percentage

40%

45.6% 40.6%

36.1% 32.9%

30% 20% 10%

13.6% 6.2%4.6%

1.0%1.0%

7.3% 3.6%

7.5%

0% postpone for postpone for postpone for postpone for will give up have disposed 1 to 2 years 3 to 4 years 5 to 10 years more than 10 years

2014 Survey

2018 Survey

Fig. 2. Distribution of survey respondents that intend to postpone car purchases or have already disposed of a vehicle. Note: The number of responses used is 1737 (100%) for 2014 and 3026 (100%) for 2018. 129

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Degree of satisfaction

4.5

4.26 4.22

4.2

3.98

3.9

3.72

3.80 3.66

3.78 3.76

3.6

3.63 3.64 3.22

3.3

3.13

3.0 Q1

Q2

Q3

Q4

Q5

Q6

Questions about satisfaction with carsharing attributes 2014 Survey

2018 Survey

Fig. 3. Mean scores of satisfaction with carsharing service attributes. Note: Q1 = reservation system (e.g. mobile application, website), Q2 = carsharing location proximity, Q3 = customer services, Q4 = rental service availability (rental hour and fleet availability), Q5 = accident claims process, and Q6 = rental charges. The error bars represent a 99% confidence interval for the mean.

being 4.01 (2014) and 3.97 (2018) on a 5-point scale. The difference between these scores was not statistically significant. Yet, the satisfaction levels with specific service components did vary, as shown in Fig. 3. The satisfaction with the reservation system (Q1) was the highest (4.26 in 2014, and 4.22 in 2018), whereas the satisfaction with rental charges (Q6) was the lowest in both years (3.22 and 3.13). The satisfaction with rental charges even slightly decreased from 2014 to 2018. However, a significant leap in user satisfaction did occur regarding carsharing location proximity (Q2) (3.72–3.98) and customer services (Q3) (3.66–3.80) between 2014 and 2018. The satisfaction with rental hour and fleet availability (Q4) did not meaningfully change (3.78–3.76). Given that a significant growth in carsharing membership happened between 2014 and 2018 (resulting in more than doubled daily rental demand on the system), the increased or stable satisfaction towards service accessibility and availability suggests that the Seoul carsharing program has well managed the rising demand and user experience during the growing years. In addition to the increase in number of stations and fleet size, the Seoul carsharing program has incorporated various rental vehicle types with different payment options since 2014. Initially, the fleet consisted mostly of small sedans and hatchbacks, and gradually incorporated a variety of options including mid-sized, large, and luxury sedans, sport utility vehicles, electric vehicles, and imported vehicles, with varying rental rates by vehicle type. Still, when it comes to rental charges, more reasonable fare schemes may be needed to improve user satisfaction. 5. Modeling approach 5.1. Statistical models Logistic regression models are developed to identify the factors underlying the changes in vehicle ownership behavior after carsharing participation. The models adapt random utility theory to discrete and ordered answers such as those provided in the binary or qualitative scale of one to five to represent the member’s choice probabilities. Random utility theory assumes that every individual is a rational decision-maker, maximizing utility relative to their choices (Ben Akiva and Morikawa, 1990; Cantillo et al., 2007; McKelvey and Zavoina, 1975). The perceived utility U j of each individual i choosing j can be expressed by the sum of the systematic utility Vi j and a random residual representing the deviation of the utility from the systematic utility as Ui j = Vi j + γi j + εi , where γ is independently and identically distributed according to a Gumbel random variable in terms of each individual’s perceived utility. In the vehicle disposal model, the dependent variable has a binary outcome: whether the carsharing member has removed a vehicle or not. As for the vehicle purchase plan model, ordered logistic models are developed to predict how long the carsharing member is willing to defer a car purchase. The responses have six levels of delay: no delay in purchasing a vehicle, delay a car purchase for 1–2 years, delay for 3–4 years, delay for 5–10 years, delay for 10 years or longer, and give up a car purchase. As a statistical measure to link this targeted behavioral outcome with explanatory variables, an odds ratio is calculated by the exponential function of the estimated coefficient. The typical forms of the binary and ordered logistic model with a latent variable Y indicating the level of underlying utility or willingness are described as (Greene, 2012):

Y = X 'β + ∊ where X ' is a vector of explanatory variables, β is a vector of coefficients, and ∊ is the error term, which is assumed to be a standard logistic cumulative distribution function. The probability of the dependent variable Y taking one of the outcome levels, j = 1, ⋯, J is:

P (Y = 1) = Φ(τ1 − X 'β ) P (Y = j ) = Φ(τj − X 'β ) − Φ(τj − 1 − X 'β ) P (Y = J ) = 1 − Φ(τj − 1 − X 'β ) 130

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where P (Y = j ) is the probability of the outcome being a specific choice j, J is the number of choices (for the ordinal model, J = 6), Φ(z) is the logistic cumulative distribution function which can be expressed as Φ(z) = e z (1+e z ), and τj is the threshold parameter dividing the discrete levels. In an ordered logistic model, the coefficient of each explanatory variable represents the effect of the variable on the outcome level. A maximum likelihood estimator was used for parameter estimation through the Rchoice package (Sarrias, 2016). The distributions of the random parameters were assumed to be normal distribution for continuous variables and uniform distributions for dummy variables. Under the normality assumption, the coefficient for variablek , βk , can be written as:

βik = βk + σk ωik where σk is the standard deviation, which controls for unobserved heterogeneity around βk , and ωik ~ N (0, 1) . Given that the domain of the normal distribution is (−∞, +∞) , the proportion of positive coefficients can be computed as F (βk σk̂ ) , where βk and σk̂ are the parameters estimated, and F is the cumulative distribution function of the standard normal distribution. When a uniform distribution is assumed, the coefficient for variablek , βk , can be written as:

βik = βk + σk (2 × uik − 1) uik ~ U (0, 1) where βk and σk are estimated. In this case, the parameter for each individual is equally likely to take on any value in some interval. The beta coefficients estimated for the 2014 and 2018 logistic models were then compared to examine whether a pair of regression coefficients for the same variable from the 2014 and 2018 models contains significantly different values. The statistical significance was assessed by the following formula (Brame et al. 1998; Paternoster et al., 1998):

Z=

β1 − β2 SEβ12 + SEβ22

where SEβ1 and SEβ2 are the standard errors of the coefficient β1 of the 2014 model and β2 of the 2018 model. 5.2. Explanatory variables The explanatory variables considered were grouped into three categories: (1) the socioeconomic traits of carsharing users, (2) their level of satisfaction with carsharing system and service attributes, and (3) carsharing service usage patterns. The socioeconomic traits of carsharing users include gender, age, household income, household size, and housing type. The variables representing satisfaction levels with carsharing service attributes were formulated using factor analyses, which will be discussed in the next subsection. The carsharing service usage patterns include frequency of use, trip purpose, and main transportation mode. The final models developed include variables rarely examined in prior studies: satisfaction with service attributes, and reasons for using carsharing. In an effort to obtain more stable and robust results, the classes of the explanatory variables were adjusted on the basis of statistical significance, sample size for each category, and empirical knowledge about the study area. For example, the number of age groups was reduced from six to four classes by merging ‘younger than 20’ with ‘between 20 and 29’, and ‘between 50 and 59’ with ‘older than 60.’ Regarding household size and housing type, binary classes were generated for each variable: single- or two-person households and larger households; type 2 multifamily housing and other housing types. The frequency of using carsharing was reclassified into three categories: ‘at least once a week,’ ‘at least once a month,’ and ‘less than once a month.’ The purpose of carsharing use was aggregated into ‘business,’ ‘commute,’ and ‘other.’ The primary travel mode options for regular trips were simplified as a binary form: personal car versus other modes. The variable lineup of the regression models for 2014 and 2018 is symmetric to allow for statistically sound comparisons. 5.3. Factor analysis of service satisfaction The service satisfaction levels are also used as explanatory variables. This study used factor analysis (FA), a multivariate statistical measure used to reduce multicollinearity and data dimensionality (Thompson, 2007). The FA is utilized as a way to identify uncorrelated, latent variables that best explain the observed pattern of user satisfaction ratings. A maximum likelihood FA is implemented with a Varimax rotation in order to maximize the sum of the squared factor loadings. The squared factor loading is the percent of variance in the respective variable explained by the generated factor (maximum value of one and minimum of zero). The optimal number of factors is determined based on the eigenvalues of resulting factors (Kaiser 1958; Thompson, 2007). Interpretation of each identified factor depends on the variables with high factor loadings on that factor. Variables with high factor loadings are considered as being highly influential in comprising the factor (Thompson, 2007). After this factor analysis, the six individual variables are grouped into two uncorrelated factors, as shown in Table 4. The similarity of the groupings in 2014 and 2018 indicates the robustness of the identified factors that are structured with high explanatory power. The result for the 2014 model shows that the two factors collectively account for 55.7% of the total variance. In the 2018 model, 60.7% of the total variance is explained by the identified factors. Factor 1, labeled ‘Rental system,’ includes three variables related to the ease of using the carsharing system: rental hour and fleet availability, carsharing location proximity, and reservation system. The rental availability explains the rental system-related satisfaction to a greater extent than other variables in 2014, whereas in 2018 the carsharing location proximity is the most influential, followed by the rental availability. Factor 2, labeled ‘Assistance 131

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Table 4 Factor loading for satisfaction levels. Variables

2014

2018

Factor 1

Factor 2

Factor 1

Factor 2

Factor 1: Rental system Rental service availability (i.e., rental hour and fleet availability) Carsharing location proximity Reservation system

0.907 0.580 0.444

0.223 0.287 0.365

0.736 0.816 0.576

0.274 0.287 0.380

Factor 2: Assistance services and charges Customer services Accident claims process Rental charges Eigenvalue % of variance

0.225 0.310 0.385 1.652 0.275

0.790 0.772 0.453 1.690 0.282

0.282 0.280 0.355 1.824 0.304

0.752 0.835 0.501 1.817 0.303

Note: Values in bold indicate relatively high factor loadings for each element.

services and charges,’ includes customer services, accident claims process, and rental charges. Overall, the accident claims process is most closely correlated to Factor 2, followed by customer services. 6. Results 6.1. Car disposal models Among the carsharing members who owned a car before carsharing participation (1982 in 2014 and 3447 in 2018), 130 (6.6%) and 412 (11.9%) responded that they had removed their vehicles primarily for the transition to shared automobility services. To predict vehicle disposal decisions induced by carsharing, two binary logistic mixed-effect (i.e., fixed and random parameters) regression models were developed with the dependent variable of one for respondents who reduced vehicle ownership and zero otherwise. The mixed-effect model specifications were gradually formulated by testing for heteroscedasticity and random variance in the base-model coefficients. The random parameters that are significant at a 90% confidence level are included for the final models. As a result, different sets of random-effect coefficients are discovered for the 2014 and 2018 models (Table 5). For the 2014 model, some socioeconomic variables (i.e., male, household income of 3–5 million KRW, and multifamily housing type 2) have heterogenous variance across the individuals, whereas for the 2018 model, multifamily housing type 2 and user satisfaction factor (Factor 1: rental system) have heterogenous variance. This statistical control of random effects of the variables considered helps improve the goodness-of-fit of the regression models for 2014 and 2018, given the increased McFadden R2 and log likelihood ratio test statistics compared with the base models (Table 5). The regression constant of the 2018 model is significantly higher than that of the 2014 model, suggesting that the overall likelihood of shedding a private car among carsharing members is higher in 2018 than in 2014. The results show that the socioeconomic traits of carsharing members have significant impacts on vehicle disposal choice after carsharing participation. Age plays a major role, and the 2014 and 2018 models share this pattern. Those aged 30 years or older are more likely to decide to shed a vehicle after carsharing than younger people, and those older than 50 are greater than 6 times more likely to do so than those younger than 30. This may be because carsharing can either relieve or remove the burden of car maintenance, and this advantage is generally more attractive to aged persons. It is noteworthy that the age effects are much stronger at the early stages of carsharing services (2014) than at the maturity stages (2018), as indicated by the higher beta coefficients for ages in 2014 than in 2018. The differences are statistically significant, implying that possible advantages of replacing car ownership with carsharing use may have become more generally appealing to younger members as carsharing programs continued to expand. There is no gender difference found in vehicle disposal choice, except that there exists significant heterogeneity among male members in the 2014 model. Household income is also significantly associated with car ownership disposal. Higher income levels are likely to decrease the likelihood of shedding a vehicle. This finding is in line with previous studies that lower-income households are more likely to shed a car, favoring carsharing services, because carsharing provides a chance to reduce fixed costs related to car ownership (Ko et al. 2019; Martin et al., 2010). Notably, the income-dependent impact of carsharing on vehicle disposal seems to taper off in 2018 compared with 2014, as reflected by the beta coefficients for moderate-to-high income categories in 2018 that are statistically significantly smaller in size than those in 2014. Household size is also an important factor in car disposal choice. One-person households are more than three times as likely to remove a private vehicle as households consisting of three or more members. Two-person households did not behave differently from larger households in 2014, but in 2018 two-person households are 1.5 times more likely to remove a vehicle than those households. This finding again adds up to the observation that carsharing advantages and their impact on reduced vehicle ownership may extend from a niche market to a more general socioeconomic group over time (Martin and Shaheen, 2011). Housing type does not have a significant fixed effect on vehicle disposal choice, but its variance is significant, both in 2014 and 2018. This means that the characteristics associated with multifamily housing type 2 may have heterogenous effects on individuals with regard to their vehicle disposal choice. For example, the shortage of parking spaces is a typical problem in residential blocks mostly taken up by type 2 multifamily housing in Seoul. However, this may not always lead to a higher likelihood of replacing a vehicle with 132

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Table 5 Logistic regression results for car disposal. Variables

2014 (N = 1982)

2018 (N = 3447)

Z=

β2 − β1 SE12 + SE22

Coefficient, β1

Standard error, SE1

p-value

Odds ratio

Coefficient, β2

Standard error, SE2

p-value

Odds ratio

Z

p-value

Socioeconomic traits Age (ref.: <30) 30–39 years 40–49 years ≥50 years Gender: male Gender: male (s.d.)a

1.791 4.443 6.688 −3.427 7.985

0.651 1.239 1.898 2.383 3.826

0.006 0.000 0.000 0.150 0.037

5.994 85.03 802.33 0.032 –

0.481 0.966 1.830 0.231 –

0.165 0.207 0.310 0.176 –

0.003 0.000 0.000 0.190 –

1.618 2.629 6.235 1.260 –

−1.950 −2.768 −2.525 1.531 –

0.026 0.003 0.006 0.063 –

Monthly income (ref.: < KRW 1–2 2–3 3–5 3–5 (s.d.)a 5–10 > 10

1 million) −1.583 −1.859 −6.153 9.190 −4.238 −5.005

0.726 0.761 2.604 3.709 1.235 1.667

0.029 0.015 0.018 0.013 0.001 0.003

0.205 0.156 0.002 – 0.014 0.007

−0.637 −1.078 −1.241 – −1.682 −1.243

0.258 0.239 0.243 – 0.277 0.308

0.013 0.000 0.000 – 0.000 0.000

0.529 0.340 0.289 – 0.186 0.289

1.228 0.979 1.878 – 2.019 2.219

0.110 0.164 0.030 – 0.022 0.013

0.824 0.577

0.006 0.315

9.647 1.787

1.357 0.444

0.161 0.176

0.000 0.012

3.884 1.559

−1.084 −0.226

0.139 0.411

0.812 1.903

0.732 0.013

1.321 –

−2.189 5.604

1.375 2.130

0.111 0.008

0.112 –

−1.545 –

0.061 –

0.289 –

0.004 –

2.298 –

0.184 0.497

0.080 0.217

0.022 0.022

1.203 –

−2.162 –

0.015 –

0.248

0.851

1.048

0.190

0.078

0.015

1.210

0.553

0.290

Carsharing usage characteristics Carsharing use frequency: reference = less than once a month At least once a week 1.413 0.751 0.060 At least once a month −0.127 0.454 0.779

4.109 0.881

0.865 0.159

0.197 0.163

0.000 0.330

2.376 1.172

−0.706 0.593

0.240 0.277

Household size: reference = more than two One 2.267 Two 0.580 Housing type Multifamily housing type 2 Multifamily housing type 2 (s.d.)a

0.279 4.703

Satisfaction degree for carsharing service Factor 1: Rental system 0.832 Factor 1: Rental system – b (s.d.) Factor 2: Assistance 0.047 services and charges

Main purpose of using shared Business trip Commute trip Main transportation mode: personal car user Constant −2 log-likelihood of null constant only model −2 log-likelihood of full model −2(L(c) − L(β)) McFadden R2: base model McFadden R2: random parameter model a b

cars 0.121 0.610 1.756 −5.118 960

0.604 0.485 0.631

0.842 0.209 0.005

1.128 1.840 5.789

0.641 0.847 0.002

0.150 0.225 0.167

0.000 0.000 0.989

1.898 2.332 1.002

0.835 0.443 −2.687

0.202 0.329 0.004

1.288

0.000

0.010

−2.675 2508

0.292

0.000

0.069

1.850 –

0.032

775

2242

–

184 (p-value = 0.000) 0.175 0.192

266 (p-value = 0.000) 0.101 0.106

– –

Assumed the error term is a uniformly distributed random variable. Assumed the error term is a normally distributed random variable.

carsharing services, but rather its impact significantly varies by individual or by specific conditions (e.g., homeowner or renter). The degree of satisfaction with carsharing service components can also explain members’ car disposal choice to a meaningful extent. Those who are more satisfied with rental system attributes (i.e., rental hour and fleet availability, rental location proximity, and reservation system) are more likely to get rid of a private vehicle after using a carsharing service. This indicates that a carsharing program may be able to make a greater impact toward reducing vehicle ownership by improving rental service accessibility, possibly with a larger fleet size and close-by rental stations. The impact of these service improvements, however, is not expected to be uniform for individuals, as suggested by the significant inter-individual heterogeneity among the carsharing members in the 2018 model. The relative importance of satisfaction with rental systems attributes also significantly decreases in 2018 compared with the 2014 model. Interestingly, the level of satisfaction with assistance service attributes (i.e., customer services, accident claims process, and rental 133

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charges) becomes a significant determinant in 2018, with its odds ratio comparable to that of the satisfaction with rental system attributes. The customer services and accident claim process are related to the reliability of a carsharing program, particularly the program’s ability to handle and resolve problems properly and in a timely manner. Since the reliability-related attributes are generally tested over time through many occasions, their impact on members’ vehicle disposal choice may be more significant for mature programs with several years of operation, rather than for immature programs. It is also found that the frequency of use of carsharing services is closely associated with reduced vehicle ownership among carsharing members. Frequent users who use shared vehicles at least once a week are at least two to four times more likely to shed a private vehicle than occasional users who use carsharing less than once a month. Using carsharing a few times per month is not meaningfully different from using carsharing less than once a month in terms of vehicle disposal propensities. In addition to the frequency of use, the main purpose of using carsharing services also matters with respect to vehicle ownership change. However, this effect is only significant in 2018. In 2018, carsharing members who use carsharing services for business or commute trips are 1.9 times or 2.3 times more likely to shed a private vehicle, respectively, than those using carsharing for non-work trips (e.g., shopping, leisure, and recreation). This implies that, with the system growth, shared vehicle services may provide a greater utility to members who use carsharing for regular trips such as business or commute trips. Conversely, the impact of auto-dependency of carsharing members on personal vehicle disposal is not significant in 2018, but is highly significant in 2014. In 2014, auto-dependent users are more likely to dispose of a vehicle than mixed-mode users, suggesting that those who depend on automobility are initially more likely to be receptive to shared automobility as an alternative to personal car ownership than mixed-mode users who may reserve personal vehicles for particular purposes or other household members. However, this gap in propensity between auto users and mixed-mode users disappears in 2018. Note that the impacts of trip purpose and primary travel mode on car disposal choice have been rarely examined in the literature. Therefore, there is a need to compile more empirical evidence in this field. 6.2. Car purchase plan models Two ordered logistic regression models were developed to predict the intentions of carsharing members to give up, delay, or hold on to a car purchase plan. The ordinal dependent variables represent six levels of intention to defer a car purchase: (1) maintain a car purchase plan, (2) defer for 1–2 years, (3) defer for 3–4 years, (4) defer for 5–10 years, (5) defer for more than 10 years, and (6) give up a car purchase completely. The car purchase plan models include the same set of explanatory variables as used in the car disposal models, except that there is one additional variable representing household car ownership (Table 6). Similar to the car disposal models, the selection of random parameters is based on the statistical significance of the coefficient variance at the 90% confidence level. The results show that the willingness to defer a car purchase is significantly associated with a variety of factors, including individual age, gender, household size and income, household car ownership, satisfaction with service attributes, and the frequency and purpose of carsharing use. Those aged 30 or older are more willing to delay purchasing vehicles for longer periods or give up the purchase itself than younger people, with people aged 50 or older being much more likely to do so. Again, age plays a significant role in explaining car ownership behavior of carsharing users. Aged carsharing members are both more inclined to dispose of a vehicle, and more willing to hold off a car purchase than younger members. This tendency to defer a car purchase becomes stronger for some age groups (aged 30–39, and aged ≥50) from 2014 to 2018, as indicated by their larger beta coefficients in 2018 than in 2014. Concerning the effect of gender, the results show that male members are less willing to delay their car purchases than females, suggesting that carsharing services may provide female drivers a better reason to postpone a car purchase for a while. However, individual heterogeneity is observed across male members in 2018, indicating that this gender effect may not be significant for some male individuals. Household income also affects the willingness to delay a car purchase. Members from moderate-to-high income households are more likely to delay or abandon car purchases than those from low-income households. This pattern is observed in both 2014 and 2018 models. This finding is rather surprising because, according to the car disposal models, higher income households tend to maintain their existing vehicles rather than shedding them compared with their lower-income counterparts. This disparity suggests that the decisions to shed a vehicle and to defer buying one need to be considered as separate issues. Higher-income members would be willing to reduce their dependency on personal cars by giving up or deferring potential car purchases, even though they may not eliminate their own vehicles. Since higher-income members are likely to afford many other mobility options than personal cars, they might be willing to postpone vehicle purchases for a longer period than lower-income members. There are also mixed findings in terms of household size. Two-person households are more willing to postpone vehicle purchases after carsharing participation than households of three or more persons. This may be because two-person households generally have more discretion on whether to purchase a car or not than larger households, particularly with children. Single-person households, however, are not significantly different from households of three or more persons regarding vehicle purchase delay. Also, the result revealed that members from carless households are willing to postpone vehicle purchases for a longer period or give up the purchase after using carsharing services than single- and multi-car households. Yet, such difference is significant only for 2014, and becomes weak and not significant in 2018, implying that the effect of private car ownership on a purchase delay might have become irrelevant over time. The willingness to defer or abandon a car purchase differs by satisfaction with service attributes, such as rental systems, assistance services and rental charges. The more satisfied carsharing members are with rental systems (Factor 1), the more likely they are willing to postpone future vehicle purchases or even give up the plan completely. The marginal impact of an increase in satisfaction with rental system attributes on vehicle purchase delay is greater in 2018 than in 2014, as exhibited by the significantly larger beta coefficient in 2018 compared with 2014. How satisfied carsharing members are with assistance service attributes and rental charges 134

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Table 6 Ordered logistic models for car purchase plan. 2014 (N = 5468)

2018 (N = 9090)

Z=

β2 − β1 SE12 + SE22

Variables

Coefficient, β1

Standard error, SE1

p-value

Coefficient, β2

Standard error, SE2

p-value

Z

p-value

Socioeconomic traits Age (ref.: <30) 30–39 years 40–49 years ≥50 years ≥50 years (s.d.)a Gender: male Gender: male (s.d.)a

0.419 0.882 0.795 – −0.297 –

0.074 0.114 0.198 – 0.082 –

0.000 0.000 0.000 – 0.000 –

0.596 0.999 1.277 1.225 −0.433 1.288

0.063 0.090 0.151 0.677 0.078 0.332

0.000 0.000 0.000 0.070 0.000 0.000

1.823 0.806 1.938 – −1.206 –

0.034 0.210 0.026 – 0.114 –

Monthly income (ref.: < KRW 1 million) 1–2 1–2 (s.d.)a 2–3 2–3 (s.d.)a 3–5 5–10 > 10

−0.156 2.678 0.658 – 0.658 0.582 0.621

0.248 0.532 0.121 – 0.128 0.145 0.202

0.529 0.000 0.000 – 0.000 0.000 0.002

0.350 1.787 0.516 1.215 0.665 0.742 0.533

0.139 0.393 0.114 0.376 0.103 0.115 0.148

0.012 0.000 0.000 0.001 0.000 0.000 0.000

1.780 – −0.852 – 0.044 0.860 −0.350

0.038 – 0.197 – 0.482 0.195 0.363

Household size: reference = more than two One Two Car ownership: one or more cars Household type: multifamily housing type 2

−0.028 0.170 −0.264 0.082

0.081 0.084 0.076 0.067

0.729 0.042 0.000 0.218

0.087 0.176 −0.051 0.029

0.065 0.065 0.060 0.052

0.177 0.007 0.397 0.573

1.114 0.056 2.208 −0.629

0.133 0.478 0.014 0.265

0.085 0.105 0.343

0.035 0.038 0.176

0.017 0.006 0.051

0.156 0.097 –

0.030 0.029 –

0.000 0.001 –

1.530 −0.168 –

0.063 0.433 –

0.115 0.078

0.000 0.000

0.698 0.533

0.088 0.071

0.000 0.000

0.160 1.566

0.436 0.059

0.103 0.076 0.115 0.154 0.037 0.085 0.122 0.135

0.504 0.001 0.548 0.000 0.000 0.000 0.000 0.000

0.272 0.360 −0.261 −1.956 0.981 2.399 2.857 2.998 18,910

0.061 0.106 0.096 0.128 0.043 0.084 0.095 0.098

0.000 0.001 0.006 0.000 0.000 0.000 0.000 0.000

1.692 0.774 −2.204 −0.776 0.401 −1.916 −4.223 −4.516 –

0.045 0.220 0.014 0.219 0.344 0.028 0.000 0.000

Satisfaction degree for carsharing service Factor 1: Rental system Factor 2: Assistance services and charges Factor 2: Assistance services and charges (s.d.)b

Carsharing usage characteristics Carsharing use frequency: reference = less than once a month At least once a week 0.675 At least once a month 0.367 Main purpose of using shared cars Business trip Commute trip Main transportation mode: personal car user Threshold τ1 Threshold τ2 Threshold τ3 Threshold τ4 Threshold τ5 −2 log-likelihood of null constant only model, L(c) −2 log-likelihood of full model, L(β) −2(L(c) − L(β)) McFadden R2: base model McFadden R2: random parameter model

0.069 0.259 0.069 −1.800 0.958 2.627 3.509 3.754 10,066

9790 276 (p-value = 0.000) 0.026 0.027

18,296 614 (p-value = 0.000) 0.031 0.032

– – – –

a

Assumed the error term is a uniformly distributed random variable. Assumed the error term is a normally distributed random variable.Table 6 header needs to be modified. The "Odds ratio" will be deleted, and the other column headers needs to be moved to the left, accordingly. Please refers to the changes made by the authors. b

(Factor 2) also significantly affects the likelihood of delayed or avoided car purchases, which is consistent with the data from 2014 and 2018. It is notable that the satisfaction with assistance services and rental charges in 2014 affects decisions only related to vehicle purchase delay, not affecting car disposal decisions. In 2018, however, it affects both car purchase and disposal decisions. Overall, these findings demonstrate that offering carsharing members with satisfactory rental and customer services is central to decreasing vehicle ownership levels among members, and the range of services needs to include a diverse set of elements from reservation systems and charges to car accident and claim management. The frequency of use of carsharing and trip purposes are significantly correlated to the delay in vehicle purchase. If carsharing members use shared vehicles more frequently, i.e., one or more times per month, they are more willing to put off purchasing a car than those who use carsharing less than once a month. If carsharing is primarily used for work trips, such as business or commute 135

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trips, then it is likely that the member would defer purchasing a vehicle for longer times than those who prefer carsharing for nonwork trips. Notably, it becomes clear in 2018 that auto-dependent individuals who mostly use a personal car in addition to carsharing are more reluctant to give up or delay a car purchase plan than other members using alternative modes (e.g., transit, taxi, walking, bicycle).

7. Conclusion Carsharing has rapidly grown in popularity over the past few decades. As such, the Seoul carsharing program has experienced a rapid growth in rental facilities, infrastructure, and registered membership since its launch with private partners in February 2013. This study, based on two cross-sectional datasets collected in the early (2014) and mature phases (2018) of the carsharing program in Seoul, aimed to explore carsharing participants’ behavioral change in terms of car ownership, as well as its time-dependent association with an array of individual socioeconomic backgrounds, service usages, and program satisfaction. Two mixed-effect binary and ordinal logistic models were developed for each year, one predicting a reduction in vehicle ownership and the other estimating the level of willingness to defer a car purchase. The random utility models helped to account for heterogeneity in the propensity of carsharing users to modify car ownership. About 31% of the carsharing members surveyed in both 2014 (N = 5598) and in 2018 (N = 9498) reduced or planned to reduce their car ownership. Around 2.3% (2014) and 4.3% (2018) of the members eliminated private vehicles after participating in the program, yielding an estimated substitution rate of 3.3 private vehicles by a shared vehicle in 2014 and 13.1 in 2018. The increased impact in 2018 came after the expansion of the carsharing service facilities and types (e.g., one-way trip services) for three years after 2014. The modeling results in 2014 and 2018 showed that there were both similarities and differences between the two years regarding the relationship between influencing factors and car ownership behavior. Some cohorts of carsharing members sharing certain socioeconomic traits were consistently more likely to dispose of a private vehicle than others both in 2014 and 2018. Aged members with lower income levels were more likely to reduce personal vehicles and decide against car ownership after experiencing carsharing services. As carsharing can mitigate the physical and financial burden of vehicle maintenance, it is more likely to replace private automobility for those groups. Interestingly, the results identified that the decisions to shed a vehicle and to defer a purchase might be separate and differently affected by underlying factors. For example, household income turned out to have mixed impacts on car ownership behavior; those from wealthy households are less likely to give up an existing car, but they are more willing to abstain from purchasing an extra car than lower-income counterparts. The well-established public transit systems in Seoul, particularly in affluent neighborhoods, may partly support their decisions against an increase in car ownership. There was no significant gender difference in car disposal propensities, but females were more willing to postpone a vehicle purchase than males. Comparing the results in 2014 and 2018, the vehicle ownership behavior of carsharing users was explained by more diverse factors in 2018, probably because the carsharing program began to serve more heterogenous users with different travel demands based on the increased service capacity and one-way services. The base-line likelihood of shedding a vehicle among carsharing users was higher in 2018 than 2014. Another important difference between 2014 and 2018 is the significance of trip purpose in 2018; those who accomplish business and commute trips using shared vehicles were more likely to eliminate or defer vehicle ownership than those using carsharing for leisure or shopping purposes. This shows that shared vehicle services compete better with private automobility or can even replace it when used as work travel modes. The relative importance of satisfaction with service reliability for reduced car ownership also increased as the carsharing program became more established over time. Findings of this study have important policy implications. First, this study provided empirical evidence that carsharing service and membership growth are likely to yield a greater gain in terms of vehicle ownership reduction over time. As this study found that members who use carsharing for work and business trips possess a greater potential to reduce their current and potential car ownership in the future, policy makers and practitioners may extend the role of carsharing as a part of the seamless transportation system for city commuters. The findings also call for more strategic location of carsharing stations to serve a larger pool of workers and commuters. In the long term, improving the user satisfaction of customer services in response to accident and other rental claims may also be the key to sustain the social and environmental benefits of a carsharing system expansion. Public and private service providers may need to invest in service reliability as well as its accessibility. This study is expected to add distinctive and comparative findings to the literature that were derived from repeated cross-sectional data (with a three-year gap) on the rapidly grown carsharing program in Seoul, Korea. Nevertheless, the study is not without limitations. Due to data constraints, some potential factors affecting car ownership behavior of carsharing users were not fully taken into consideration, such as occupation, residential environment, and life-cycle events (e.g., marriage, job relocation, having children). A longitudinal observation of behavioral change of carsharing members will be useful to draw a more causal relationship among personal traits, service usage and attributes, and car ownership modification. Collecting repeated cross-sectional data covering more intervals or longer durations may also be useful in observing a larger picture of divergent car ownership choices among members. Although this study considered the frequency of carsharing use to explore potential behavioral differences between active and non-active members, the length of membership may also create meaningful differences in car ownership decision and its change over time. Future research employing a repeated cross-sectional framework to study a long-term effect of carsharing on users may consider controlling for this duration of use to obtain more reliable results.

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