Tang, T, Liu, R orcid.org/0000-0003-0627-3184, Choudhury, C orcid.org/0000-0002-8886-8976 et al. (2 more authors) (2023) Predicting hourly boarding demand of bus passengers using imbalanced records from smart-cards: A deep learning approach. IEEE Transactions on Intelligent Transportation Systems, 24 (5). pp. 5105-5119. ISSN 1524-9050
Abstract
The tap-on smart-card data provides a valuable source to learn passengers’ boarding behaviour and predict future travel demand. However, when examining the smart-card records (or instances) by the time of day and by boarding stops, the positive instances (i.e. boarding at a specific bus stop at a specific time) are rare compared to negative instances (not boarding at that bus stop at that time). Imbalanced data has been demonstrated to significantly reduce the accuracy of machine-learning models deployed for predicting hourly boarding numbers from a particular location. This paper addresses this data imbalance issue in the smart-card data before applying it to predict bus boarding demand. We propose the deep generative adversarial nets (Deep-GAN) to generate dummy travelling instances to add to a synthetic training dataset with more balanced travelling and non-travelling instances. The synthetic dataset is then used to train a deep neural network (DNN) for predicting the travelling and non-travelling instances from a particular stop in a given time window. The results show that addressing the data imbalance issue can significantly improve the predictive model’s performance and better fit ridership’s actual profile. Comparing the performance of the Deep-GAN with other traditional resampling methods shows that the proposed method can produce a synthetic training dataset with a higher similarity and diversity and, thus, a stronger prediction power. The paper highlights the significance and provides practical guidance in improving the data quality and model performance on travel behaviour prediction and individual travel behaviour analysis.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Keywords: | Boarding behaviour prediction; smart-card; bus; data imbalance issue; deep generative adversarial network; deep 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: Choice Modelling The University of Leeds > Faculty of Environment (Leeds) > Institute for Transport Studies (Leeds) > ITS: Spatial Modelling and Dynamics (Leeds) |
Funding Information: | Funder Grant number RCUK (Research Councils UK) MR/T020423/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 11 Oct 2022 08:52 |
Last Modified: | 16 May 2023 11:54 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers |
Identification Number: | 10.1109/TITS.2023.3237134 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:191453 |