forecasting long-term traffic flows on real-world PeMS-Bay and PeMSD7(M) datasets.'
author:
- 'Mingxing Xu, Wenrui Dai, Chunmiao Liu, Xing Gao, Weiyao Lin, Guo-Jun Qi, Hongkai Xiong'
bibliography:
- 'STTN.bib'
title: 'Spatial-Temporal Transformer Networks for Traffic Flow Forecasting'
---

traffic flow predictions, spatial-temporal dependencies, dynamcial graph neural networks, transformer.

Introduction
============

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        ![(a) Traffic forecasting models with joint spatial temporal dependencies. (b) Evolution of the traffic conditions spatial distribution. []{data-label="fig0"}](model.pdf "fig:"){width="1\columnwidth" height="0.25\columnwidth"}
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   ![(a) Traffic forecasting models with joint spatial temporal dependencies. (b) Evolution of the traffic conditions spatial distribution. []{data-label="fig0"}](Dynamical_spatial.pdf "fig:"){width="1\columnwidth" height="0.6\columnwidth"}
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   ![image](Shortv.pdf){width="1\columnwidth" height="0.75\columnwidth"}   ![image](Longv_full_loss.pdf){width="1\columnwidth" height="0.75\columnwidth"}
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the deployment of affordable traffic sensor technologies over the last few years, the exploding traffic data have been bringing us to the era of big data of transportation. Intelligent Transportation System (ITS) [@mori2015review] is thus developed to leverage transportation big data for efficient urban traffic controlling and planning. As a core component of ITS, accurate traffic forecasting in a timely fashion has attracted increasing attentions.

In traffic forecasting, the future traffic conditions (e.g. speeds, volumes and density) of a node are predicted from its historical traffic data as well as its neighbors. Thus, it is important 