Understanding intra-urban human mobility through an exploratory spatiotemporal analysis of bike-sharing trajectories

ABSTRACT

In this paper, we present a data-driven framework to support exploratory spatial, temporal, and statistical analysis of intra-urban human mobility. We leveraged a new mobility data source, the dockless bike-sharing service Mobike, to quantify short-trip transportation patterns in Shanghai, China, the world’s largest bike-share city. A data-driven framework was established to integrate multiple data sources, including transportation network data (roads, bikes, and public transit), road characteristics, and urban land use, to achieve a detailed, accurate analysis of cycling patterns at both the individual and group levels. The results provide a comprehensive view of mobility patterns in the use of shared-ride bicycles, including: (1) the temporal and spatiotemporal distribution of shared-bike usage and how this varies according to different land use; (2) the statistical distribution of Mobike trips, which are primarily short-distance; and (3) the travel behavior and road factors that influence Mobike users’ route choice. The findings offer valuable insights for city planners regarding infrastructure development, for shared-ride bike companies to offer better bike rebalancing strategies to meet user demand, and for the promotion of this new green transportation mode to alleviate traffic congestion and enhance public health.

KEYWORDS:

• bike sharing
• human mobility
• smart cities
• travel behavior
• data-driven geography

Data and codes availability statement

The data and codes that support the findings of this study are available with a DOI at https://doi.org/10.6084/m9.figshare.11493420.v1

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This research is supported by the CAREER program of the National Science Foundation under grant [# BCS-1455349].

Notes on contributors

Wenwen Li

Dr. Wenwen Li is an Associate Professor in GIScience in the School of Geographical Sciences and Urban Planning, Arizona State University. Her research interests include cyberinfrastructure, geospatial big data, machine learning and their applications in data-intensive environmental and social sciences.

Shaohua Wang

Dr. Shaohua Wang is a postdoctoral scholar at Arizona State University. His research interests include spatial analysis, spatial optimization and high performance computing.

Xiaoyi Zhang

Ms. Xiaoyi Zhang is a visiting PhD student at Arizona State University. Her research interests include social media analysis, smart cities and human mobility.

Qingren Jia

Dr. Qingren Jia is a PhD student from Northeastern University and a visiting PhD student at Arizona State University. His research interest is GIS and 3D modeling.

Yuanyuan Tian

Ms. Yuanyuan Tian is a PhD student at Arizona State University. Her research interest is cyberinfrastructure, ontology and semantics.