A Hybrid Method for Traffic Flow Forecasting Using Multimodal Deep Learning
This work addresses traffic flow forecasting for intelligent transport systems, but it is incremental as it combines existing techniques like CNN, GRU, and attention mechanisms.
The authors tackled short-term traffic flow forecasting by proposing a hybrid multimodal deep learning method that jointly learns spatial-temporal features from multi-modality traffic data, achieving satisfying accuracy and effectiveness in complex urban scenarios.
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. Then, we design a hybrid multimodal deep learning framework (HMDLF) for fusing share representation features of different modality traffic data by multiple CNN-GRU-Attention modules. The experimental results indicate that the proposed multimodal deep learning model is capable of dealing with complex nonlinear urban traffic flow forecasting with satisfying accuracy and effectiveness.