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Lessons Learnt from the analysis of a bike sharing system

Published: 21 June 2017 Publication History

Abstract

Bike-sharing systems have been deployed in many major cities around the world today. Bike sharing systems provide great advantages as a mean of urban public transportation facilitating a green solution for daily commuters and tourists. Users tend to use more often this type of transportation for their daily needs. The key to success for such systems is the efficient distribution of bikes among the bike stations in order to satisfy high user demands. Existing schemes in the literature focus either on predicting the bike station demand and modeling user mobility mainly focusing on making cycling more accessible to people, or on minimizing the costly and time-consuming movement of bikes among the stations while the system is in use. In this work our objective is to gain insights into the usage of bike sharing systems and in particular the pick-up and drop-off operations. Our goal is to get a better understanding of the bike mobility patterns and identify the key factors that lead to imbalances in the distribution of the bikes at the stations, towards creating effective and sustainable bike sharing systems.

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Cited By

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  • (2024)On Urban Data Analytics and Applications in the Big Data Era2024 25th IEEE International Conference on Mobile Data Management (MDM)10.1109/MDM61037.2024.00067(328-330)Online publication date: 24-Jun-2024
  • (2023)A holistic approach for modeling and predicting bike demandInformation Systems10.1016/j.is.2022.102129111(102129)Online publication date: Jan-2023

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  1. Lessons Learnt from the analysis of a bike sharing system

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    cover image ACM Other conferences
    PETRA '17: Proceedings of the 10th International Conference on PErvasive Technologies Related to Assistive Environments
    June 2017
    455 pages
    ISBN:9781450352277
    DOI:10.1145/3056540
    Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    • NSF: National Science Foundation
    • CSE@UTA: Department of Computer Science and Engineering, The University of Texas at Arlington

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 21 June 2017

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    Author Tags

    1. Bike Mobility
    2. SmartCities
    3. Urban Mobility

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    View all
    • (2024)On Urban Data Analytics and Applications in the Big Data Era2024 25th IEEE International Conference on Mobile Data Management (MDM)10.1109/MDM61037.2024.00067(328-330)Online publication date: 24-Jun-2024
    • (2023)A holistic approach for modeling and predicting bike demandInformation Systems10.1016/j.is.2022.102129111(102129)Online publication date: Jan-2023

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