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Weak signals in the mobility landscape: car sharing in ten European cities
Open accessAbstract
Car sharing is one the pillars of a smart transportation infrastructure, as it is expected to reduce traffic congestion, parking demands and pollution in our cities. From the point of view of demand modelling, car sharing is a weak signal in the city landscape: only a small percentage of the population uses it, and thus it is difficult to study reliably with traditional techniques such as households travel diaries. In this work, we depart from these traditional approaches and we leverage web-based, digital records about vehicle availability in 10 European cities for one of the major active car sharing operators. We discuss which sociodemographic and urban activity indicators are associated with variations in car sharing demand, which forecasting approach (among the most popular in the related literature) is better suited to predict pickup and drop-off events, and how the spatio-temporal information about vehicle availability can be used to infer how different zones in a city are used by customers. We conclude the paper by presenting a direct application of the analysis of the dataset, aimed at identifying where to locate maintenance facilities within the car sharing operation area.
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“These are mostly highly educated, middle to high income individuals who use shared mobility as a substitute for other means of transport.”
From The opportunity of shared autonomous vehicles to improve spatial equity in accessibility and socio-economic developments in European urban areas (2021) · cites this paper as [3]
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References 53 · Cited by 32
Research works printed but not linked: 2
- Boldrini C, Bruno R, Laarabi HM (2017) Car sharing through the data analysis lens—KNOWMe: 1st International Workshop on Knowledge Discovery from Mobility and Transportation Systems. Technical report. arXiv:1708.00497 · arXiv
- Karamshuk D, Noulas A, Scellato S, Nicosia V, Mascolo C (2013) Geo-spotting. In: Proceedings of the 19th ACM SIGKDD international conference on knowledge discovery and data mining—KDD ’13
Metadata
- Authors
- Boldrini, C., Bruno, R., & Laarabi, M. H.
- Affiliations
- Institute of Informatics and Telematics (IT)
- Corresponding author
- Boldrini, Chiara
- Venue
- EPJ Data Science
- Published
- 2019-03-01
- Volume
- 8
- Issue
- 1
- ISSN
- 2193-1127 (ISSN-L)
- OpenAlex
- W2933971328
- Cited by
- 32
- References
- 53
- Citations by year
- 2026: 5 · 2025: 6 · 2024: 3 · 2023: 3 · 2022: 3 · 2021: 4 · 2020: 7 · 2019: 1
- FWCI
- 1.71
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- EPJ Data Science (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- arXiv (Cornell University) (cc-by)
- Funders
- Horizon 2020 Framework Programme (653395, 691735, 654024)
- Topics
- Social Sciences › Transportation › Transportation and Mobility Innovations
- Social Sciences › Transportation › Urban Transport and Accessibility
- Social Sciences › Business, Management and Accounting › Marketing › Sharing Economy and Platforms
- Keywords
- car sharing, weak signals, demand forecasting, web data, sociodemographic indicators, spatio-temporal analysis, smart transportation, European cities
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