Spatial-Temporal Transformer Networks for Traffic Flow Forecasting

by   Mingxing Xu, et al.

Traffic forecasting has emerged as a core component of intelligent transportation systems. However, timely accurate traffic forecasting, especially long-term forecasting, still remains an open challenge due to the highly nonlinear and dynamic spatial-temporal dependencies of traffic flows. In this paper, 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, we present a new variant of graph neural networks, named spatial transformer, by dynamically modeling directed spatial dependencies with self-attention mechanism to capture realtime traffic conditions as well as the directionality of traffic flows. Furthermore, different spatial dependency patterns can be jointly modeled with multi-heads attention mechanism to consider diverse relationships related to different factors (e.g. similarity, connectivity and covariance). On the other hand, the temporal transformer is utilized to model long-range bidirectional temporal dependencies across multiple time steps. Finally, they are composed as a block to jointly model the spatial-temporal dependencies for accurate traffic prediction. Compared to existing works, 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, especially forecasting long-term traffic flows on real-world PeMS-Bay and PeMSD7(M) datasets.


page 1

page 2

page 11

page 12


PDFormer: Propagation Delay-aware Dynamic Long-range Transformer for Traffic Flow Prediction

As a core technology of Intelligent Transportation System, traffic flow ...

Explainable Graph Pyramid Autoformer for Long-Term Traffic Forecasting

Accurate traffic forecasting is vital to an intelligent transportation s...

Long-Range Transformers for Dynamic Spatiotemporal Forecasting

Multivariate Time Series Forecasting (TSF) focuses on the prediction of ...

MMST-ViT: Climate Change-aware Crop Yield Prediction via Multi-Modal Spatial-Temporal Vision Transformer

Precise crop yield prediction provides valuable information for agricult...

A Lightweight and Accurate Spatial-Temporal Transformer for Traffic Forecasting

We study the forecasting problem for traffic with dynamic, possibly peri...

Spatiotemporal Modeling of Multivariate Signals With Graph Neural Networks and Structured State Space Models

Multivariate signals are prevalent in various domains, such as healthcar...

Please sign up or login with your details

Forgot password? Click here to reset