On prediction of traffic flows in smart cities: a multitask deep learning based approach

With the rapid development of transportation systems, traffic data have been largely produced in daily lives. Finding the insights of all these complex data is of great significance to vehicle dispatching and public safety. In this work, we propose a multitask deep learning model called Multitask Recurrent Graph Convolutional Network (MRGCN) for accurately predicting traffic flows in the city. Specifically, we design a multitask framework consisting of four components: a region-flow encoder for modeling region-flow dynamics, a transition-flow encoder for exploring transition-flow correlations, a context modeling component for contextualized fusion of two types of traffic flows and a task-specific decoder for predicting traffic flows. Particularly, we introduce Dual-attention Graph Convolutional Gated Recurrent Units (DGCGRU) to simultaneously capture spatial and temporal dependencies, which integrate graph convolution and recurrent model as a whole. Extensive experiments are carried out on two real-world datasets and the results demonstrate that our proposed method outperforms several existing approaches.