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Method for prediction of Utilization Rate of Electric Vehicle Free-Floating Car Sharing Services using Data Mining
RISE Viktoria, Gothenburg, Sweden. (Cooperative Systems)ORCID iD: 0000-0002-1043-8773
Nimling AB, Askim, Sweden.
Chalmers University of Technology, Gothenburg, Sweden. (Electromobility)
RISE Viktoria, Gothenburg, Sweden. (Electromobility)
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2018 (English)In: 31st International Electric Vehicles Symposium & Exhibition (EVS 31) & International Electric Vehicle Technology Conference 2018 (EVTeC 2018), 2018Conference paper, Oral presentation only (Refereed)
Abstract [en]

Free-floating car sharing is a form of car rental used by people for short periods of time where the cars can be picked up and returned anywhere within a given area. In this paper, we have collected free-floating car sharing data, for electric as well as fossil fueled cars, and data regarding e.g. size of the city, number of cars in the service, etc. The utilization rates of the free-floating car sharing services vary much between the cities, greatly influencing the success of the services. This paper presents the most important factors influencing the utilization rate, and also a methodology to predict the utilization rate for new cities, using data mining based on Random Forests.© EVS 31 & EVTeC 2018.

Place, publisher, year, edition, pages
2018.
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:hh:diva-37751OAI: oai:DiVA.org:hh-37751DiVA, id: diva2:1240435
Conference
31st International Electric Vehicles Symposium & Exhibition (EVS 31) & International Electric Vehicle Technology Conference 2018 (EVTeC 2018), Sept. 30 - Oct. 3, 2018, Kobe, Japan
Available from: 2018-08-21 Created: 2018-08-21 Last updated: 2019-01-29Bibliographically approved

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Englund, Cristofer

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Citation style
  • apa
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  • de-DE
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  • Other locale
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Output format
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  • asciidoc
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