research-article

Model-Based Count Series Clustering for Bike Sharing System Usage Mining: A Case Study with the Vélib’ System of Paris

Authors:

Oukhellou LatifaAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 5, Issue 3

Article No.: 39, Pages 1 - 21

https://doi.org/10.1145/2560188

Published: 28 July 2014 Publication History

Abstract

Today, more and more bicycle sharing systems (BSSs) are being introduced in big cities. These transportation systems generate sizable transportation data, the mining of which can reveal the underlying urban phenomenon linked to city dynamics. This article presents a statistical model to automatically analyze the trip data of a bike sharing system. The proposed solution partitions (i.e., clusters) the stations according to their usage profiles. To do so, count series describing the stations’s usage through departure/arrival counts per hour throughout the day are built and analyzed. The model for processing these count series is based on Poisson mixtures and introduces a station scaling factor that handles the differences between the stations’s global usage. Differences between weekday and weekend usage are also taken into account. This model identifies the latent factors that shape the geography of trips, and the results may thus offer insights into the relationships between station neighborhood type (its amenities, its demographics, etc.) and the generated mobility patterns. In other words, the proposed method brings to light the different functions in different areas that induce specific patterns in BSS data. These potentials are demonstrated through an in-depth analysis of the results obtained on the Paris Vélib’ large-scale bike sharing system.

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Cited By

Ye JBai JHao W(2024)A Systematic Review of the Coopetition Relationship between Bike-Sharing and Public TransitJournal of Advanced Transportation10.1155/2024/66818952024(1-25)Online publication date: 16-Jan-2024
https://doi.org/10.1155/2024/6681895
Lee DKim K(2024)AdaBoost.RDT: AdaBoost Integrated With Residual-Based Decision Tree for Demand Prediction of Bike Sharing Systems Under Extreme DemandsIEEE Access10.1109/ACCESS.2024.347401712(144316-144336)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3474017
Chiou YWu K(2024)Bikesharing: The first- and last-mile service of public transportation? Evidence from an origin–destination perspectiveTransportation Research Part A: Policy and Practice10.1016/j.tra.2024.104162187(104162)Online publication date: Sep-2024
https://doi.org/10.1016/j.tra.2024.104162
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Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 5, Issue 3

Special Section on Urban Computing

September 2014

361 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/2648782

Editor:
Qiang Yang
Hong Kong University of Science and Technology, Hong Kong

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Copyright © 2014 ACM.

© 2014 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 July 2014

Accepted: 01 November 2013

Revised: 01 March 2013

Received: 01 October 2012

Published in TIST Volume 5, Issue 3

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Cited By

Ye JBai JHao W(2024)A Systematic Review of the Coopetition Relationship between Bike-Sharing and Public TransitJournal of Advanced Transportation10.1155/2024/66818952024(1-25)Online publication date: 16-Jan-2024
https://doi.org/10.1155/2024/6681895
Lee DKim K(2024)AdaBoost.RDT: AdaBoost Integrated With Residual-Based Decision Tree for Demand Prediction of Bike Sharing Systems Under Extreme DemandsIEEE Access10.1109/ACCESS.2024.347401712(144316-144336)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3474017
Chiou YWu K(2024)Bikesharing: The first- and last-mile service of public transportation? Evidence from an origin–destination perspectiveTransportation Research Part A: Policy and Practice10.1016/j.tra.2024.104162187(104162)Online publication date: Sep-2024
https://doi.org/10.1016/j.tra.2024.104162
de Nailly PCôme EOukhellou LSamé AFerriere JMerad-Boudia Y(2024)Multivariate count time series segmentation with “sums and shares” and Poisson lognormal mixture models: a comparative study using pedestrian flows within a multimodal transport hubAdvances in Data Analysis and Classification10.1007/s11634-023-00543-918:2(455-491)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1007/s11634-023-00543-9
Müller C(2024)Practicable solution approaches for differentiated pricing of vehicle sharing systemsCentral European Journal of Operations Research10.1007/s10100-024-00915-2Online publication date: 24-May-2024
https://doi.org/10.1007/s10100-024-00915-2
Wang SZhu XWang GLiu YHe TZhang D(2023)eShare+: A Data-Driven Balancing Mechanism for Bike Sharing Systems Considering Both Quality of Service and MaintenanceIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.325372535:10(10497-10513)Online publication date: 1-Oct-2023
https://doi.org/10.1109/TKDE.2023.3253725
Luo MDu BZhang WSong TLi KZhu HBirkin MWen H(2023)Fleet Rebalancing for Expanding Shared e-Mobility Systems: A Multi-Agent Deep Reinforcement Learning ApproachIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2022.323342224:4(3868-3881)Online publication date: Apr-2023
https://doi.org/10.1109/TITS.2022.3233422
Teusch JGremmel JKoetsier CJohora FSester MWoisetschläger DMüller J(2023)A Systematic Literature Review on Machine Learning in Shared MobilityIEEE Open Journal of Intelligent Transportation Systems10.1109/OJITS.2023.33343934(870-899)Online publication date: 2023
https://doi.org/10.1109/OJITS.2023.3334393
Moreau HCôme ÉSamé AOukhellou L(2023)A Regression Mixture Model to understand the effect of the Covid-19 pandemic on Public Transport Ridership2023 IEEE International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW60847.2023.00163(1259-1268)Online publication date: 4-Dec-2023
https://doi.org/10.1109/ICDMW60847.2023.00163
Lu XHsu HChu W(2023)Public Bicycle Flow Forecasting using Spatial and Temporal Graph Neural Network2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC)10.1109/COMPSAC57700.2023.00071(490-499)Online publication date: Jun-2023
https://doi.org/10.1109/COMPSAC57700.2023.00071
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