Using graph structural information about flows to enhance short-term demand prediction in bike-sharing systems

Yang, Y., Heppenstall, A. , Turner, A. and Comber, A. (2020) Using graph structural information about flows to enhance short-term demand prediction in bike-sharing systems. Computers, Environment and Urban Systems, 83, 101521. (doi: 10.1016/j.compenvurbsys.2020.101521)

Abstract

Short-term demand prediction is important for managing transportation infrastructure, particularly in times of disruption, or around new developments. Many bike-sharing schemes face the challenges of managing service provision and bike fleet rebalancing due to the “tidal flows” of travel and use. For them, it is crucial to have precise predictions of travel demand at a fine spatiotemporal granularities. Despite recent advances in machine learning approaches (e.g. deep neural networks) and in short-term traffic demand predictions, relatively few studies have examined this issue using a feature engineering approach to inform model selection. This research extracts novel time-lagged variables describing graph structures and flow interactions from real-world bike usage datasets, including graph node Out-strength, In-strength, Out-degree, In-degree and PageRank. These are used as inputs to different machine learning algorithms to predict short-term bike demand. The results of the experiments indicate the graph-based attributes to be more important in demand prediction than more commonly used meteorological information. The results from the different machine learning approaches (XGBoost, MLP, LSTM) improve when time-lagged graph information is included. Deep neural networks were found to be better able to handle the sequences of the time-lagged graph variables than other approaches, resulting in more accurate forecasting. Thus incorporating graph-based features can improve understanding and modelling of demand patterns in urban areas, supporting bike-sharing schemes and promoting sustainable transport. The proposed approach can be extended into many existing models using spatial data and can be readily transferred to other applications for predicting dynamics in mass transit systems. A number of limitations and areas of further work are discussed.

• Item Type: Articles
• Additional Information: This study is supported and funded by University of Leeds and Chinese Scholarship Council (201606420071), the Natural Environment Research Council (NE/S009124/1) and the Economic and Social Research Council Alan Turing research fellowship (ES/R007918/1).
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Heppenstall, Professor Alison
• Authors: Yang, Y., Heppenstall, A., Turner, A., and Comber, A.
• College/School: College of Social Sciences > School of Social and Political Sciences > Urban Studies
• Journal Name: Computers, Environment and Urban Systems
• Publisher: Elsevier
• ISSN: 0198-9715
• ISSN (Online): 1873-7587
• Published Online: 13 July 2020
• Copyright Holders: Crown Copyright © 2020
• First Published: First published in Computers, Environment and Urban Systems 83:101521
• Publisher Policy: Reproduced under a creative commons licence
• Data DOI: 10.5518/851