Journal
IET INTELLIGENT TRANSPORT SYSTEMS
Volume 12, Issue 7, Pages 578-585Publisher
WILEY
DOI: 10.1049/iet-its.2017.0313
Keywords
recurrent neural nets; learning (artificial intelligence); environmental factors; traffic engineering computing; intelligent transportation systems; weather condition data; urban traffic flow prediction; GRU-based deep learning approach; intelligent transportation management system; recurrent neural network; predictive accuracy; prediction error rate; urban freeways; gated recurrent unit-based deep learning framework
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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.
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