GAMIT: A New Encoder-Decoder Framework with Graphical Space and Multi-grained Time for Traffic Predictions

Nowadays, many researchers study on characterizing complex and dynamic traffic environments by modeling the spatio-temporal dependencies in a road network for traffic predictions. However, existing works fail to investigate comprehensive spatio-temporal dependencies, because most of them ignore the spatial dependencies from the topological graph structure information in the road network, or only consider the temporal dependencies between fine-grained time slots whereas ignore those among coarse-grained time periods, in which a time period is composed of a number of time slots. To this end, we propose a new encoder-decoder framework called GAMIT with graphical space and multi-grained time by developing a spatiotemporal recurrent neural network (STRNN) for traffic predictions, where the graphical space consists of spatial networks which represent road networks. STRNN first devises a spatiotemporal convolution block to capture the fine-grained spatiotemporal dependencies between time slots in the road network. Then, STRNN uses the recurrent architecture to catch the coarsegrained spatio-temporal dependencies among time periods. The encoder finally applies STRNN to learn the multi-grained spatiotemporal dependencies which are fed into the decoder for computing traffic predictions based on STRNN as well. To evaluate the performance of GAMIT, we conduct extensive experiments on two real traffic flow datasets. Experimental results show that GAMIT outperforms the state-of-the-art traffic prediction models.