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.
Combining weather condition data to predict traffic flow: a GRU‐based deep learning approach
IET Intelligent Transport Systems ; 12 , 7 ; 578-585
2018-09-01
8 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
urban traffic flow prediction , environmental factors , recurrent neural nets , recurrent neural network , prediction error rate , urban freeways , traffic engineering computing , learning (artificial intelligence) , predictive accuracy , weather condition data , intelligent transportation systems , GRU‐based deep learning approach , intelligent transportation management system , gated recurrent unit‐based deep learning framework
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