access icon free Combining weather condition data to predict traffic flow: a GRU-based deep learning approach

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.

Inspec keywords: environmental factors; recurrent neural nets; intelligent transportation systems; traffic engineering computing; learning (artificial intelligence)

Other keywords: recurrent neural network; intelligent transportation management system; urban traffic flow prediction; predictive accuracy; GRU-based deep learning approach; prediction error rate; weather condition data; gated recurrent unit-based deep learning framework; urban freeways

Subjects: Neural computing techniques; Traffic engineering computing

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