A Hybrid Method for Traffic Flow Forecasting Using Multimodal Deep Learning

Understand

Traffic flow forecasting has been regarded as a key problem of intelligent transport systems.

• In this work, we propose a hybrid multimodal deep learning method for short-term traffic flow forecasting, which can jointly and adaptively learn the spatial-temporal correlation features and long temporal interdependence of multi-modality traffic data by an attention auxiliary multimodal deep learning architecture.
• According to the highly nonlinear characteristics of multi-modality traffic data, the base module of our method consists of one-dimensional Convolutional Neural Networks (1D CNN) and Gated Recurrent Units (GRU) with the attention mechanism.
• The former is to capture the local trend features and the latter is to capture the long temporal dependencies.