Forecasting Transportation Network Speed Using Deep Capsule Networks With Nested LSTM Models
Forecasting Transportation Network Speed Using Deep Capsule Networks With Nested LSTM Models
Accurate and reliable traffic forecasting for complicated transportation networks is of vital importance to modern transportation management. The complicated spatial dependencies of roadway links and the dynamic temporal patterns of traffic states make it particularly challenging. To address these challenges, we propose a new capsule network (CapsNet) to extract the …