Mass Rapid Transit System Passenger Traffic Forecast Using a Re-Sample Recurrent Neural Network

Rong Hu, Yi Chang Chiu, Chih Wei Hsieh, Tang Hsien Chang, Xingsi Xue, Fumin Zou, Lyuchao Liao

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


In this study, we developed a model re-sample Recurrent Neural Network (RRNN) to forecast passenger traffic on Mass Rapid Transit Systems (MRT). The Recurrent Neural Network was applied to build a model to perform passenger traffic prediction, where the forecast task was transformed into a classification task. However, in this process, the training dataset usually ended up being imbalanced. To address this dataset imbalance, our research proposes re-sample Recurrent Neural Network. A case study of the California Mass Rapid Transit System revealed that the model introduced in this work could timely and effectively predict passenger traffic of MRT. The measurements of passenger traffic themselves were also studied and showed that the new method provided a good understanding of the level of passenger traffic and was able to achieve prediction accuracy upwards of 90% higher than standard tests. The development of this model adds value to the methodology of traffic applications by employing these Recurrent Neural Networks.

Original languageEnglish (US)
Article number8943291
JournalJournal of Advanced Transportation
StatePublished - 2019

ASJC Scopus subject areas

  • Automotive Engineering
  • Economics and Econometrics
  • Mechanical Engineering
  • Computer Science Applications
  • Strategy and Management


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