Tang, T, Fonzone, A, Liu, R orcid.org/0000-0003-0627-3184 et al. (1 more author) (2021) Multi-stage deep learning approaches to predict boarding behaviour of bus passengers. Sustainable Cities and Society, 73. 103111. ISSN 2210-6707
Abstract
Smart card data has emerged in recent years and provide a comprehensive, and cheap source of information for planning and managing public transport systems. This paper presents a multi-stage machine learning framework to predict passengers’ boarding stops using smart card data. The framework addresses the challenges arising from the imbalanced nature of the data (e.g. many non-travelling data) and the ‘many-class’ issues (e.g. many possible boarding stops) by decomposing the prediction of hourly ridership into three stages: whether to travel or not in that one-hour time slot, which bus line to use, and at which stop to board. A simple neural network architecture, fully connected networks (FCN), and two deep learning architectures, recurrent neural networks (RNN) and long short-term memory networks (LSTM) are implemented. The proposed approach is applied to a real-life bus network. We show that the data imbalance has a profound impact on the accuracy of prediction at individual level. At aggregated level, FCN is able to accurately predict the rideship at individual stops, it is poor at capturing the temporal distribution of ridership. RNN and LSTM are able to measure the temporal distribution but lack the ability to capture the spatial distribution through bus lines.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 Elsevier Ltd. All rights reserved. This is an author produced version of an article published in Sustainable Cities and Society. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Deep learning; Smart public transport; Travel pattern; Smart card data; Neural network |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > Institute for Transport Studies (Leeds) > ITS: Spatial Modelling and Dynamics (Leeds) |
Funding Information: | Funder Grant number Department of Transport P4002008 |
Depositing User: | Symplectic Publications |
Date Deposited: | 23 Jun 2021 11:28 |
Last Modified: | 24 Feb 2025 13:28 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.scs.2021.103111 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:175414 |
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