article Open AccessTop 10% cited
Understanding Bike-Sharing Systems using Data Mining: Exploring Activity Patterns
Procedia - Social and Behavioral Sciences · 2011 · Vol. 20 · pp. 514–523
Patrick Vogel✉(Technische Universität Braunschweig)Torsten Greiser(Technische Universität Braunschweig)Dirk C. Mattfeld(Technische Universität Braunschweig)
Abstract
In this paper we analyze extensive operational data from bike-sharing systems in order to derive bike activity patterns. A common issue observed in bike-sharing systems is imbalances in the distribution of bikes. We use Data Mining to gain insight into the complex bike activity patterns at stations. Activity patterns reveal imbalances in the distribution of bikes and lead to a better understanding of the system structure. A structured Data Mining process supports planning and operating decisions for the design and management of bike-sharing systems.
Urban Transport and AccessibilityHuman Mobility and Location-Based AnalysisTransportation Planning and OptimizationBike sharingComputer scienceProcess (computing)Order (exchange)Data sharingDistribution (mathematics)Data miningData scienceTransport engineeringEngineering
Funding
- Johns Hopkins University
Citations
298
FWCI
11.17
field-weighted impact
References
24
Percentile
98%
vs. same field & year
Citations per year
References
Algorithms for Clustering Data
Technometrics · 1990 · 7,836 citations
Data mining: concepts and techniques
Choice Reviews Online · 2012 · 28,852 citations
Bikesharing in Europe, the Americas, and Asia
Transportation Research Record Journal of the Transportation Research Board · 2010 · 1,125 citations
Citation Network
How this paper connects to the literature. Drag to explore, click any node to open that paper.
