Incorporating dynamicity of transportation network with multi-weight traffic graph convolutional network for traffic forecasting

Understand

Traffic forecasting problem remains a challenging task in the intelligent transportation system due to its spatio-temporal complexity.

• Although temporal dependency has been well studied and discussed, spatial dependency is relatively less explored due to its large variations, especially in the urban environment.
• In this study, a novel graph convolutional network model, Multi-Weight Traffic Graph Convolutional (MW-TGC) network, is proposed and applied to two urban networks with contrasting geometric constraints.
• The model conducts graph convolution operations on speed data with multi-weighted adjacency matrices to combine the features, including speed limit, distance, and angle.