Spatial-Temporal Transformer Networks for Traffic Flow Forecasting

Mingxing Xu · Wenrui Dai · Chunmiao Liu · Xing Gao · Weiyao Lin · Guo-Jun Qi +1 more

606 citations0/0 files storedarXiv (Cornell University)

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Traffic Prediction and Management Techniques Transportation Planning and Optimization Traffic control and management

Abstract

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.

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References

2018Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting 2019Graph WaveNet for Deep Spatial-Temporal Graph Modeling 2017Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting 2017Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction 2015Long short-term memory neural network for traffic speed prediction using remote microwave sensor data 2004Travel-Time Prediction With Support Vector Regression 2017Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction 2013Short-Term Traffic Flow Forecasting: An Experimental Comparison of Time-Series Analysis and Supervised Learning

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2023PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Prediction 2022Dynamic Graph Convolutional Recurrent Network for Traffic Prediction: Benchmark and Solution 2023Spatio-Temporal Adaptive Embedding Makes Vanilla Transformer SOTA for Traffic Forecasting 2020A Survey on Modern Deep Neural Network for Traffic Prediction: Trends, Methods and Challenges

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