ST-GRAT: A Novel Spatio-temporal Graph Attention Network for Accurately Forecasting Dynamically Changing Road Speed

Abstract

Predicting road traffic speed is a challenging task due to different types of roads, abrupt speed change and spatial dependencies between roads; it requires the modeling of dynamically changing spatial dependencies among roads and temporal patterns over long input sequences. This paper proposes a novel spatio-temporal graph attention (ST-GRAT) that effectively captures the spatio-temporal dynamics in road networks. The novel aspects of our approach mainly include spatial attention, temporal attention, and spatial sentinel vectors. The spatial attention takes the graph structure information (e.g., distance between roads) and dynamically adjusts spatial correlation based on road states. The temporal attention is responsible for capturing traffic speed changes, and the sentinel vectors allow the model to retrieve new features from spatially correlated nodes or preserve existing features. The experimental results show that ST-GRAT outperforms existing models, especially in difficult conditions where traffic speeds rapidly change (e.g., rush hours). We additionally provide a qualitative study to analyze when and where ST-GRAT tended to make accurate predictions during rush-hour times.

arXiv commentto be published in CIKM-2020

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• Traffic Prediction using Artificial Intelligence: Review of Recent Advances and Emerging Opportunities
• ETA Prediction with Graph Neural Networks in Google Maps
• LSTTN: A Long-Short Term Transformer-based Spatio-temporal Neural Network for Traffic Flow Forecasting
• Multi-graph convolutional network for short-term passenger flow forecasting in urban rail transit
• Dynamic Spatiotemporal Graph Convolutional Neural Networks for Traffic Data Imputation with Complex Missing Patterns
• Long-Range Transformers for Dynamic Spatiotemporal Forecasting
• Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting
• Learning to Remember Patterns: Pattern Matching Memory Networks for Traffic Forecasting
• Residual Correction in Real-Time Traffic Forecasting
• Spatial-Temporal Tensor Graph Convolutional Network for Traffic Prediction