A new approach to predict the market and impacts of round-trip and point-to-point carsharing systems: Case study of London

Vine, Scott Le; Lee‐Gosselin, Martin; Sivakumar, Aruna; Polak, John

doi:10.1016/j.trd.2014.07.005

Cited by 106 publications

(51 citation statements)

References 12 publications

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“…However, as demonstrated in our study, the behavioral and the routine changes are central to the success of the sharing economy platforms as well. Research on understanding such changes has also been suggested by earlier studies [55] but has rarely been addressed [56]. This issue seems especially important when thinking about the sustainability of the sharing economy.…”

Section: Discussionmentioning

confidence: 97%

Online and Offline Communities in the Sharing Economy

Vaskelainen

Piscicelli

2018

Sustainability

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Abstract:The online community is crucial to sharing economy platforms because without it, no transactions can take place. Online communities have been studied extensively, but so far, little attention has been paid to how they link to different offline communities, such as geographic (e.g., neighborhoods) and relational communities (e.g., friends and colleagues). In this study, we address this gap by examining the importance of communities to the users and the entrepreneurs of the goods-sharing platform Ecomodo. We conduct a qualitative content analysis of archival and interview data to uncover the importance of different communities and the relationships among them. We discover that the platform design aimed to facilitate lending and borrowing in relational communities. However, geographic communities were more important to the users since most of them joined the platform to be acquainted with their neighbors. We also find that the platform entrepreneurs underestimated the behavioral changes needed to use the platform. The producers were not used to asking for money to lend their possessions, and it was difficult to teach consumers to borrow instead of buying. We use these findings to offer recommendations to practitioners and discuss some avenues for further research.

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Section: Discussionmentioning

confidence: 97%

Online and Offline Communities in the Sharing Economy

Vaskelainen

Piscicelli

2018

Sustainability

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“…Due to such structural differences, it was found to attract different customer groups and to also have a different impact on travel behavior (Le Vine et al, 2014, Becker et al, 2017a. Therefore, knowledge about the drivers of station-based car-sharing demand as outlined above may not be applicable to free-floating car-sharing.…”

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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“…Strategy level covers issues of environment and resources benefits [8,9], market investigation [10,11], users' behavior [12,13], and network design [14,15]. Operating level problems also received attention, but studies are still not sufficient to support the industrial development, especially for one-way model, which allows users to pick up and drop off vehicles at different stations.…”

Section: Study On Vehicle Relocation Problemsmentioning

confidence: 99%

Vehicle Relocation Triggering Thresholds Determination in Electric Carsharing System under Stochastic Demand

Wang

2018

Journal of Advanced Transportation

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Vehicle unbalance usually occurs in multistation electric carsharing systems. Threshold triggering method is one of the most practicable approaches for vehicle relocation, while determination of thresholds has not been sufficiently studied particularly for electric carsharing system. This paper presents an approach on determining the thresholds considering the stochastic demands and system states. Firstly, we establish a state transition model involving the stochastic variables to capture the dynamics of the number and battery status of vehicles as well as the traffic demands. Consequently, a dual-objective optimization model was developed to determine the proper values of thresholds. The solution algorithm employed the min-max robust optimization to tackle the uncertainty and the Pareto optimum to decide the solution under dual objectives. To test the distribution stochastic variables, we involve the orders data and the supplementary user survey. Comparison is conducted among three methods: the empirical rules, the deterministic method, and the stochastic method, where the results suggest that the stochastic method achieves better solutions on the dual objectives under stochastic demands.

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A new approach to predict the market and impacts of round-trip and point-to-point carsharing systems: Case study of London

Cited by 106 publications

References 12 publications

Online and Offline Communities in the Sharing Economy

Online and Offline Communities in the Sharing Economy

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

Vehicle Relocation Triggering Thresholds Determination in Electric Carsharing System under Stochastic Demand

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