Multi-stage deep learning approaches to predict boarding behaviour of bus passengers

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. The authors 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 ridership 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.

• Record URL:
  https://doi.org/10.1016/j.scs.2021.103111
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S2210670721003930
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/22106707
• Supplemental Notes:
  • © 2021 Published by Elsevier Ltd. Abstract reprinted with permission of Elsevier.
• Authors:
  • Tang, Tianli
  • Fonzone, Achille
  • Liu, Ronghui
  • Choudhury, Charisma
• Publication Date: 2021-10

Language

• English

Media Info

• Media Type: Web
• Features: Figures; Maps; References; Tables;
• Serial:
  • Sustainable Cities and Society
  • Volume: 73
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 2210-6707
  • Serial URL: http://www.sciencedirect.com/science/journal/22106707?sdc=2

Subject/Index Terms

• TRT Terms: Behavior; Boarding; Bus transit; Machine learning; Neural networks; Predictive models; Ridership; Smart cards
• Subject Areas: Data and Information Technology; Planning and Forecasting; Public Transportation;

Filing Info

• Accession Number: 01780482
• Record Type: Publication
• Files: TRIS
• Created Date: Aug 27 2021 2:58PM