流浪着幸福ka
一种基于多模态深度学习的混合交通流预测方法
a hybrid method for traffic flow forecasting using multimodal deep learning
作者团队：西南交通大学&国立台湾科技大学等
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 spatialtemporal 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 th
