Finding Factors that Influence Carsharing Usage: Case Study in Seoul

Kang, Junhee; Hwang, Keeyeon; Park, Sung Jin

doi:10.3390/su8080709

Cited by 35 publications

(24 citation statements)

References 22 publications

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“…This view is consistent with the findings reported by Kang et al [12] (p. 10); 'that carsharing demand is high in an area where a higher proportion of building floor area is used for business, and which has a higher proportion of young residents in their 20s and 30s'. That is why this result can be used to forecast carsharing demand, especially in Asian cities with urban conditions similar to Seoul, where the study occurred.…”

Section: Introductionsupporting

confidence: 92%

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Collaborative Consumption: A Proposed Scale for Measuring the Construct Applied to a Carsharing Setting

2017

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In recent years, there has been a significant shift towards greater collaboration in various spheres of society, in which the creation of value from shared resources while balancing self-interest and community well-being is emphasized. Consumption has ceased to be characterized exclusively by the purchase and possession of goods; instead new collaborative initiatives represented by exchanges, loans, renting, and other forms of sharing that allow consumers access to a good or service only in the time they are necessary have appeared. However, few studies have attempted to measure the reasons that lead consumers to practice collaborative consumption. Therefore, the main objective of this article is to propose a scale that measures the motivators, facilitators, and constraints for this mode of consumption. For this, a study was conducted among carsharing users in Brazil, which aimed to purify and validate the proposed scale. The results indicate that collaborative consumption applied to a carsharing setting is composed of six dimensions and confirm the validity and reliability of the studied construct. The discussion highlights the study findings and offers suggestions for further research into this topic.

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Section: Introductionsupporting

confidence: 92%

“…The consumers researched were residents of the biggest Brazilian city, São Paulo. In this sense, Kang et al [12] emphasizes the importance of studies based on real carsharing usage data.…”

Section: Developing and Validating The Scale-the Descriptive Stagementioning

confidence: 99%

Collaborative Consumption: A Proposed Scale for Measuring the Construct Applied to a Carsharing Setting

2017

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“…A possible interpretation of this is, that free-floating car-sharing activity in general scales with social activity in a given area, whereas economic activity has a much lower -or even inverse -effect, which is in contrast to station-based car-sharing (Kang et al, 2016). This implies that although opening up car-sharing for one-way and especially commute trips, free-floating car-sharing is still mostly used for discretionary trips.…”

Section: Discussionmentioning

confidence: 99%

“…The findings were extended by Stillwater et al (Stillwater et al, 2009) showing that also characteristics of the built environment, particularly street width and public transportation service levels significantly affect local demand for station-based car-sharing. Including land-use variables in their model, Kang et al (Kang et al, 2016) point out that car-sharing is used more intensively in business districts and areas with a high density of car-sharing stations. However, they also find that in Seoul, station-based round-trip car-sharing is most successful in areas featuring higher vehicle ownership rates and less rail accessibility indicating substantial differences in car-sharing adoption and use between Asia and the North America.…”

Section: Introductionmentioning

confidence: 99%

Modeling free-floating car-sharing use in Switzerland: A spatial regression and conditional logit approach

Becker

Ciari

Axhausen

2017

Transportation Research Part C: Emerging Technologies

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Free-floating car-sharing has been one of the latest innovations in the car-sharing market. It allows its customers to locate available vehicles via a smartphone app and reserve them for a short time prior to their rental. Because it is available for point-to-point trips, free-floating car-sharing is not only an alternative to private cars, but also to public transportation. Using spatial regression and conditional logit analysis of original transaction data of a free-floating carsharing scheme in Switzerland, this research shows that free-floating car-sharing is mainly used for discretionary trips, for which only substantially inferior public transportation alternatives are available. In contrast to station-based car-sharing, it does not rely on high-quality local public transportation access, but bridges gaps in the existing public transportation network.

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“…The authors applied a multilevel regression analysis and showed that vehicle age, the concentration of users within a specific geographic region, and the vicinity of stations are important factors for high vehicle usage. Applying an analogous model for a station-based system in Seoul, Kang et al [15] identified a high density of business offices and a high density of people aged between 20 and 30 to be positively correlated with carsharing demand. However, for understanding and predicting the use of FFCS, it is necessary to create a more comprehensive customer profile.…”

Section: Literature Reviewmentioning

confidence: 99%

An Explanatory Model Approach for the Spatial Distribution of Free-Floating Carsharing Bookings: A Case-Study of German Cities

2017

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Abstract:When the first free-floating carsharing operators launched their business, they did not know if it would be profitable. They often started in highly populated cities without performing extensive target group analysis, and were less concerned about fleet management. Usually, there are two main datasets that can be used to find areas that would have a high demand for free-floating carsharing: booking data, for measuring the actual demand; and land use and census data for describing the activities performed in different areas in a city. In this paper, we aim to use this information to help predict the demand of free-floating carsharing systems. We use booking data provided by DriveNow for Berlin in 2014 and contextual information about the type of activity each neighborhood has. Using Berlin as a case study, we apply a negative binomial statistical model to explain the number of bookings. From the results, we conclude that free-floating carsharing is predominantly successful in areas with more affluent citizens who are open to trying new and sustainable technologies. Other important determinants that result in a high number of carsharing bookings are the area's centrality and parking lot availability. The statistical model for Berlin was then transferred to Munich and Cologne, two other cities in Germany with similar population sizes. A comparison between the estimated demand categories and actual bookings shows satisfying results, but also non-negligible local conditions influencing the spatial demand for bookings.

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Finding Factors that Influence Carsharing Usage: Case Study in Seoul

Cited by 35 publications

References 22 publications

Collaborative Consumption: A Proposed Scale for Measuring the Construct Applied to a Carsharing Setting

Collaborative Consumption: A Proposed Scale for Measuring the Construct Applied to a Carsharing Setting

Modeling free-floating car-sharing use in Switzerland: A spatial regression and conditional logit approach

An Explanatory Model Approach for the Spatial Distribution of Free-Floating Carsharing Bookings: A Case-Study of German Cities

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