A matrix factorization model with local and global consistency for flow prediction in bike-sharing systems

Abstract

With the increasing concerns about the environmental impact of motor vehicles, more cities are investing in Bike-Sharing Systems (BSSs) as an alternative mode of transport for their citizens. In such systems, predicting the fine-grained station-level bike-flow improves the operation and reliability. Some recent studies have employed graph-based approaches to model BSSs, however, considering spatial closeness and communities in the flow prediction has not been fully addressed yet. In this paper, we propose a Matrix Factorization model with Local and Global consistency (MFLOG) to be used for flow prediction in BSSs. MFLOG captures the dynamics and underlying structure of a BSS and models spatial closeness, temporal variations, and communities in a BSS. We also investigate the relationship between spatial closeness and bike-flow and explore the stability of communities in the BSS. The proposed method is evaluated on the Divvy Trips data set in the City of Chicago. The results show that the MFLOG model improves the accuracy of single and multiple-step bike-flow and check-in/out predictions over the baseline models.

Keywords:

• Bike-Sharing System (BSS)
• station-level prediction
• local and global consistency
• communities
• spatial closeness
• graph analytics

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data and codes availability statement

The code and data that support the findings of this study are available here https://doi.org/10.6084/m9.figshare.18551234.

Additional information

Funding

This work was supported by the NSERC/Cisco Industrial Research Chair, Grant IRCPJ 488403-1.

Notes on contributors

Rouzbeh Forouzandeh Jonaghani

Rouzbeh Forouzandeh Jonaghani is a Ph.D. candidate at the Department of Geodesy and Geomatics Engineering, University of New Brunswick, Canada. Rouzbeh’s research lies at the intersection of advanced geospatial analytics, machine learning, and graph analytics.

Monica Wachowicz

Dr. Monica Wachowicz is a Professor of Data Science at RMIT. Her pioneering work in multidisciplinary teams from government, industry, and research organizations is fostering the next generation of data scientists for innovation in green economies.

Trevor Hanson

Dr. Trevor Hanson is a Professor of Civil Engineering at the University of New Brunswick where he teaches transportation engineering and planning. His research interests include age-friendly transportation alternatives and transportation planning and forecasting tools.