Dataset Information

Does free-floating carsharing reduce private vehicle ownership? The case of SHARE NOW in European cities.

ABSTRACT: During the last decade, the use of free-floating carsharing systems has grown rapidly in urban areas. However, little is known on the effects free-floating carsharing offerings have on car ownership in general. Also the main drivers why free-floating users sell their cars are still rarely analysed. To shed some light on these issues, we carried out an online survey among free-floating carsharing users in 11 European cities and based our analysis on a sample of more than 10,000 survey participants. Our results show that one carsharing car replaces several private cars - in optimistic scenarios up to 20 cars. In Copenhagen (followed by Rome, Hamburg, and London) one carsharing car replaces about two times more private cars than in Madrid, the city with the lowest number. The main non-city specific influencing factor of shedding a private car due to the availability of the free-floating carsharing services seems to be the usage frequency of the service. The more kilometres users drive with these cars, the more likely it becomes that they sell a private car (or they sell their car and, therefore, use this service more often). Further memberships of bikesharing and other carsharing services, users that live in larger buildings as well as users that own several cars are more likely to reduce their number of cars, too. Finally, our findings are highly valuable for carsharing operators and (transport) policy makers when introducing free-floating carsharing systems in further cities. According to our results, all 11 cities show a reduced private car fleet due to members' access to free-floating carsharing.

SUBMITTER: Jochem P

PROVIDER: S-EPMC7544607 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Does free-floating carsharing reduce private vehicle ownership? The case of SHARE NOW in European cities.

Jochem Patrick P Frankenhauser Dominik D Ewald Lukas L Ensslen Axel A Fromm Hansjörg H

Transportation research. Part A, Policy and practice 20201009

During the last decade, the use of free-floating carsharing systems has grown rapidly in urban areas. However, little is known on the effects free-floating carsharing offerings have on car ownership in general. Also the main drivers why free-floating users sell their cars are still rarely analysed. To shed some light on these issues, we carried out an online survey among free-floating carsharing users in 11 European cities and based our analysis on a sample of more than 10,000 survey participant ...[more]

PMID: 33052178

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Project description:Vehicle ownership modeling and prediction is a crucial task in the transportation planning processes which, traditionally, uses statistical models in the modeling process. However, with the advancement in computing power of computers and Artificial Intelligence, Machine Learning (ML) algorithms are becoming an alternative or a complement to the statistical models in modeling the transportation planning processes. Although the application of ML algorithms to the transportation planning processes-like mode choice, and traffic forecasting and demand modeling-have received much attention in research and abound in literature, scanty attention is paid to its application to vehicle ownership modeling especially in the context of small to medium cities in developing countries. Therefore, this study attempts to fill this gap by modeling vehicle ownership in the Greater Tamale Area (GTA), a typically small to medium city in Ghana. Using a cross sectional survey of formal sectors workers, data was collected between June-August 2018. The study applied nine different ML classification algorithms to the dataset using 10-fold cross-validation technique/s and the Cohen-Kappa static/statistic to evaluate the predictive performance of each of the algorithms, and the Permutation Feature Importance to examine the features that contribute significantly to the prediction of vehicle ownership in GTA. The results showed that Linear Support Vector Classification (LinearSVC) classifier performed well in comparison with the other classifiers with regards to the overall predictive ability of the classifiers. In terms of class predictions, K- Nearest Neighbors (KNN) classifier performs well for no-vehicle class whiles Linear Support Vector Classification (LinearSVC) and GaussianNB classifiers performs well for motorcycle ownership. LinearSVC and Logistic Regression classifiers performed well on the car ownership class. Also, the results indicated that travel mode choice, average monthly income, average travel distance to workplace, average monthly expenditure on transport, duration of travel to workplace, occupational rank, age, household size and marital status were significant in predicting vehicle ownership for most of the classifiers. These findings could help policies makers carve out strategies that would reduce vehicle ownership but improve personal mobility.

Dataset Information

Does free-floating carsharing reduce private vehicle ownership? The case of SHARE NOW in European cities.

Publications

Does free-floating carsharing reduce private vehicle ownership? The case of SHARE NOW in European cities.

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