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Bikesharing: Analysis and Prediction

Published: 26 September 2019 Publication History

Abstract

As one of the most popular shared-economy industries, the bikesharing system allows the users to rent a bike from one location and return it to any other locations in their network. In this paper, we perform the statistical and predictive analysis on the bikesharing data in Washington, D.C. Our results discover different usage patterns by the registered and casual users, and we are able to predict the rental demands effectively in the bikesharing system.

References

[1]
Paul DeMaio. 2009. Bike-sharing: History, impacts, models of provision, and future. Journal of public transportation, Vol. 12, 4 (2009), 3.
[2]
Hadi Fanaee-T and Joao Gama. 2014. Event labeling combining ensemble detectors and background knowledge. Progress in Artificial Intelligence, Vol. 2, 2--3 (2014), 113--127.

Cited By

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  • (2020)Sustainable City Mobility—Comparison of Actual State in Selected European CountriesSmart and Sustainable Supply Chain and Logistics – Trends, Challenges, Methods and Best Practices10.1007/978-3-030-61947-3_9(133-151)Online publication date: 13-Dec-2020

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Published In

cover image ACM Conferences
SIGITE '19: Proceedings of the 20th Annual SIG Conference on Information Technology Education
September 2019
206 pages
ISBN:9781450369213
DOI:10.1145/3349266
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 September 2019

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Author Tags

  1. bikesharing
  2. data analytics
  3. predictive model

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SIGITE '19 Paper Acceptance Rate 23 of 57 submissions, 40%;
Overall Acceptance Rate 176 of 429 submissions, 41%

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  • (2020)Sustainable City Mobility—Comparison of Actual State in Selected European CountriesSmart and Sustainable Supply Chain and Logistics – Trends, Challenges, Methods and Best Practices10.1007/978-3-030-61947-3_9(133-151)Online publication date: 13-Dec-2020

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