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On Car-Sharing Usage Prediction with Open Socio-Demographic Data

Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Torino TO, Italy
Departamento de Ciência da Computação, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, Brazil
Author to whom correspondence should be addressed.
Electronics 2020, 9(1), 72;
Received: 12 November 2019 / Revised: 7 December 2019 / Accepted: 24 December 2019 / Published: 1 January 2020
(This article belongs to the Special Issue Big Data Analytics for Smart Cities)
Free-Floating Car-Sharing (FFCS) services are a flexible alternative to car ownership. These transportation services show highly dynamic usage both over different hours of the day, and across different city areas. In this work, we study the problem of predicting FFCS demand patterns—a problem of great importance to the adequate provisioning of the service. We tackle both the prediction of the demand (i) over time and (ii) over space. We rely on months of real FFCS rides in Vancouver, which constitute our ground truth. We enrich this data with detailed socio-demographic information obtained from large open-data repositories to predict usage patterns. Our aim is to offer a thorough comparison of several machine-learning algorithms in terms of accuracy and ease of training, and to assess the effectiveness of current state-of-the-art approaches to address the prediction problem. Our results show that it is possible to predict the future usage with relative errors down to 10%, while the spatial prediction can be estimated with relative errors of about 40%. Our study also uncovers the socio-demographic features that most strongly correlate with FFCS usage, providing interesting insights for providers interested in offering services in new regions. View Full-Text
Keywords: Machine Learning; Regression models; Car Sharing Machine Learning; Regression models; Car Sharing
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MDPI and ACS Style

Cocca, M.; Teixeira, D.; Vassio, L.; Mellia, M.; Almeida, J.M.; Couto da Silva, A.P. On Car-Sharing Usage Prediction with Open Socio-Demographic Data. Electronics 2020, 9, 72.

AMA Style

Cocca M, Teixeira D, Vassio L, Mellia M, Almeida JM, Couto da Silva AP. On Car-Sharing Usage Prediction with Open Socio-Demographic Data. Electronics. 2020; 9(1):72.

Chicago/Turabian Style

Cocca, Michele, Douglas Teixeira, Luca Vassio, Marco Mellia, Jussara M. Almeida, and Ana P. Couto da Silva. 2020. "On Car-Sharing Usage Prediction with Open Socio-Demographic Data" Electronics 9, no. 1: 72.

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