Combining weather condition data to predict traffic flow: A GRU-based deep learning approach

Research output: Contribution to journalArticle

16 Scopus citations

Abstract

Traffic flow prediction is an essential component of the intelligent transportation management system. This study applies gated recurrent neural network to predict urban traffic flow considering weather conditions. Running results show that, under the review of weather influences, their method improves predictive accuracy and also decreases the prediction error rate. To their best knowledge, this is the first time that traffic flow is predicted in urban freeways in this particular way. This study examines it with respect to extensive weather influence under gated recurrent unit-based deep learning framework.

Original languageEnglish (US)
Pages (from-to)578-585
Number of pages8
JournalIET Intelligent Transport Systems
Volume12
Issue number7
DOIs
StatePublished - Sep 1 2018

ASJC Scopus subject areas

  • Transportation
  • Environmental Science(all)
  • Mechanical Engineering
  • Law

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