Strategic and operational planning of bike-sharing systems by data mining: a case study

Authors:
Patrick Vogel;Dirk C. Mattfeld
Affiliations:
University of Braunschweig, Decision Support Group, Braunschweig, Germany;University of Braunschweig, Decision Support Group, Braunschweig, Germany
Venue:
ICCL'11 Proceedings of the Second international conference on Computational logistics
Year:
2011

Citing 7
Cited 0

Algorithms for clustering data

Algorithms for clustering data
Advances in knowledge discovery and data mining

Advances in knowledge discovery and data mining
Principles of data mining

Principles of data mining
Unsupervised document classification using sequential information maximization

SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Introduction to Data Mining, (First Edition)

Introduction to Data Mining, (First Edition)
Cluster Analysis for Data Mining and System Identification

Cluster Analysis for Data Mining and System Identification
Urban cycles and mobility patterns: Exploring and predicting trends in a bicycle-based public transport system

Pervasive and Mobile Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Bike-sharing is a new form of sustainable urban public mobility. A common issue observed in bike-sharing systems is imbalances in the distribution of bikes. There are two logistical measures alleviating imbalances: strategic network design and operational repositioning of bikes. IT-systems record data from Bike Sharing Systems (BSS) operation that are suitable for supporting these logistical tasks. A case study shows how Data Mining applied to operational data offers insight into typical usage patterns of bike-sharing systems and is used to forecast bike demand with the aim of supporting and improving strategic and operational planning.