Skip to main content

Research Repository

Advanced Search

Predicting Hourly Boarding Demand of Bus Passengers Using Imbalanced Records From Smart-Cards: A Deep Learning Approach

Tang, Tianli; Liu, Ronghui; Choudhury, Charisma; Fonzone, Achille; Wang, Yuanyuan

Authors

Tianli Tang

Ronghui Liu

Charisma Choudhury

Yuanyuan Wang



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.

Journal Article Type Article
Acceptance Date Jan 12, 2023
Online Publication Date Jan 23, 2023
Publication Date 2023
Deposit Date Jan 23, 2023
Publicly Available Date Jan 23, 2023
Print ISSN 1524-9050
Electronic ISSN 1558-0016
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
DOI https://doi.org/10.1109/TITS.2023.3237134
Keywords Boarding behaviour prediction, Smart-card, Bus, Data imbalance issue, Deep generative adversarial network, Deep neural network

Files

Predicting Hourly Boarding Demand Of Bus Passengers Using Imbalanced Records From Smart-cards: A Deep Learning Approach (accepted version) (8.6 Mb)
PDF




You might also like



Downloadable Citations