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
2018-03-27
8 pages
Article (Journal)
Electronic Resource
English
weather condition data , predictive accuracy , intelligent transportation systems , learning (artificial intelligence) , environmental factors , urban traffic flow prediction , recurrent neural network , prediction error rate , GRU-based deep learning approach , intelligent transportation management system , traffic engineering computing , urban freeways , gated recurrent unit-based deep learning framework , recurrent neural nets
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