Open AccessCombining weather condition data to predict traffic flow: a GRU‐based deep learning approach
Author(s) -
Zhang Da,
Kabuka Mansur R.
Publication year - 2018
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2017.0313
Subject(s) - traffic flow (computer networking) , intelligent transportation system , computer science , deep learning , artificial neural network , mean squared prediction error , component (thermodynamics) , artificial intelligence , machine learning , meteorology , transport engineering , engineering , geography , computer security , physics , thermodynamics
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.