Forecasting Transportation Network Speed Using Deep Capsule Networks with Nested LSTM Models

Understand

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 spatial features of traffic networks and utilize a nested LSTM (NLSTM) structure to capture the hierarchical temporal dependencies in traffic sequence data.
• A framework for network-level traffic forecasting is also proposed by sequentially connecting CapsNet and NLSTM.