Predicting bus ridership based on the weather conditions using deep learning algorithms

Zakir H. Farahmand*, Konstantinos Gkiotsalitis, Karst T. Geurs

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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This study proposes a new approach to predict bus ridership based on the weather conditions while accounting for additional factors such as large events, holidays, and bus cancellations. For this purpose, a deep learning algorithm - multilayer perceptron (MLP) networks - is implemented using smart-card data from the bus network in the Twente Region in the Netherlands. The prediction was carried out under three scenarios: (1) without the weather conditions (base model), (2) with the weather conditions of the same time as the boarding time into buses, and (3) with the weather conditions of an hour ahead of boarding time into buses. The results showed that the application of the MLP is very promising in forecasting bus ridership considering the meteorological parameters. The average errors were improved by 4.9% on weekdays and 2.8% on weekends as a result of including the meteorological parameters in the models. The improvements were even more prominent on weekdays with moderate to extreme weather conditions (for instance, heavy precipitation, strong wind speed, and low temperature). However, the models showed higher errors during the morning peak hours on days with heavy rainfall and strong wind speed.

Original languageEnglish
Article number100833
JournalTransportation Research Interdisciplinary Perspectives
Publication statusPublished - May 2023


  • Bus ridership
  • Deep learning
  • Demand prediction
  • Multilayer perceptron
  • Weather conditions
  • UT-Gold-D


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