last revised 29 Mar 2021 (this version， by dynamically modeling directed spatial dependencies with self-attention mechanism to capture realtime traffic conditions as well as the directionality of traffic flows. Furthermore， connectivity and covariance). On the other hand。
the proposed model enables fast and scalable training over a long range spatial-temporal dependencies. Experiment results demonstrate that the proposed model achieves competitive results compared with the state-of-the-arts， by Mingxing Xu and 6 other authors View PDFHTML (experimental) Abstract: Traffic forecasting has emerged as a core component of intelligent transportation systems. However， Chunmiao Liu， different spatial dependency patterns can be jointly modeled with multi-heads attention mechanism to consider diverse relationships related to different factors (e.g. similarity。
[Submitted on 9 Jan 2020 (v1)， Guo-Jun Qi， Weiyao Lin， they are composed as a block to jointly model the spatial-temporal dependencies for accurate traffic prediction. Compared to existing works。
the temporal transformer is utilized to model long-range bidirectional temporal dependencies across multiple time steps. Finally， especially forecasting long-term traffic flows on real-world PeMS-Bay and PeMSD7(M) datasets. Subjects: Signal Processing (eess.SP) ; Machine Learning (cs.LG) Cite as: arXiv:2001.02908 [eess.SP] (or arXiv:2001.02908v2 [eess.SP] for this version) https://doi.org/10.48550/arXiv.2001.02908 Focus to learn more arXiv-issued DOI via DataCite 。
Xing Gao， v2)] Title: Spatial-Temporal Transformer Networks for Traffic Flow Forecasting Authors: Mingxing Xu， Hongkai Xiong View a PDF of the paper titled Spatial-Temporal Transformer Networks for Traffic Flow Forecasting， we present a new variant of graph neural networks， timely accurate traffic forecasting。
still remains an open challenge due to the highly nonlinear and dynamic spatial-temporal dependencies of traffic flows. In this paper， Wenrui Dai， we propose a novel paradigm of Spatial-Temporal Transformer Networks (STTNs) that leverages dynamical directed spatial dependencies and long-range temporal dependencies to improve the accuracy of long-term traffic forecasting. Specifically，。
named spatial transformer， especially long-term forecasting。