Dynamic spatial‐temporal network for traffic forecasting based on joint latent space representation - Qian Yu; Liang Ma; Pei Lai; Jin Guo (IET Intelligent Transport Systems, 2024)
Abstract In the era of data‐driven transportation development, traffic forecasting is crucial. Established studies either ignore the inherent spatial structure of the traffic network or ignore the global spatial correlation and may not capture the spatial relationships adequately. In this work, a Dynamic Spatial‐Temporal Network (DSTN) based on Joint Latent Space Representation (JLSR) is proposed for traffic forecasting. Specifically, in the spatial dimension, a JLSR network is developed by integrating graph convolution and spatial attention operations to model complex spatial dependencies. Since it can adaptively fuse the representation information of local topological space and global dynamic space, a more comprehensive spatial dependency can be captured. In the temporal dimension, a Stacked Bidirectional Unidirectional Gated Recurrent Unit (SBUGRU) network is developed, which captures long‐term temporal dependencies through both forward and backward computations and superimposed recurrent layers. On these bases, DSTN is developed in an encoder‐decoder framework and periodicity is flexibly modeled by embedding branches. The performance of DSTN is validated on two types of real‐world traffic flow datasets, and it improves over baselines.