Statistical patterns of human mobility in emerging Bicycle Sharing Systems

The emerging Bicycle Sharing System (BSS) provides a new social microscope that allows us to "photograph" the main aspects of the society and to create a comprehensive picture of human mobility behavior in this new medium. BSS has been deployed in many major cities around the world as a sh...

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Bibliographic Details
Published in:PloS one 2018-03, Vol.13 (3), p.e0193795-e0193795
Main Authors: Chang, Xiangyu, Shen, Jingzhou, Lu, Xiaoling, Huang, Shuai
Format: Article
Language:English
Subjects:
Bicycles
Bicycling
Bike sharing
Biology and Life Sciences
Clustering
Community structure
Computer and Information Sciences
Datasets
Earth Sciences
Ecology and Environmental Sciences
Homogeneity
Human behavior
Human geography
Information management
International conferences
Inventory
Inventory management
Management methods
Mobility
Operations research
Physical Sciences
Population (statistical)
Principal components analysis
Public transportation
Research and Analysis Methods
Social science research
Social Sciences
Statistical analysis
Statistics
Taxonomy
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Summary:The emerging Bicycle Sharing System (BSS) provides a new social microscope that allows us to "photograph" the main aspects of the society and to create a comprehensive picture of human mobility behavior in this new medium. BSS has been deployed in many major cities around the world as a short-distance trip supplement for public transportations and private vehicles. A unique value of the bike flow data generated by these BSSs is to understand the human mobility in a short-distance trip. This understanding of the population on short-distance trip is lacking, limiting our capacity in management and operation of BSSs. Many existing operations research and management methods for BSS impose assumptions that emphasize statistical simplicity and homogeneity. Therefore, a deep understanding of the statistical patterns embedded in the bike flow data is an urgent and overriding issue to inform decision-makings for a variety of problems including traffic prediction, station placement, bike reallocation, and anomaly detection. In this paper, we aim to conduct a comprehensive analysis of the bike flow data using two large datasets collected in Chicago and Hangzhou over months. Our analysis reveals intrinsic structures of the bike flow data and regularities in both spatial and temporal scales such as a community structure and a taxonomy of the eigen-bike-flows.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0193795