STGAtt: A Spatial-Temporal Unified Graph Attention Network for Traffic Flow Forecasting

TL;DR

STGAtt is a novel deep learning model that uses a unified graph attention mechanism to accurately capture complex spatial-temporal dependencies in traffic flow data, improving forecasting performance.

Contribution

The paper introduces a unified graph attention network that models spatial-temporal correlations directly within a single graph structure, with a novel exchanging mechanism for capturing diverse dependencies.

Findings

Outperforms state-of-the-art methods on PEMS-BAY and SHMetro datasets.

Effectively captures long-range and short-range traffic dependencies.

Demonstrates adaptability to dynamic traffic patterns.

Abstract

Accurate and timely traffic flow forecasting is crucial for intelligent transportation systems. This paper presents a novel deep learning model, the Spatial-Temporal Unified Graph Attention Network (STGAtt). By leveraging a unified graph representation and an attention mechanism, STGAtt effectively captures complex spatial-temporal dependencies. Unlike methods relying on separate spatial and temporal dependency modeling modules, STGAtt directly models correlations within a Spatial-Temporal Unified Graph, dynamically weighing connections across both dimensions. To further enhance its capabilities, STGAtt partitions traffic flow observation signal into neighborhood subsets and employs a novel exchanging mechanism, enabling effective capture of both short-range and long-range correlations. Extensive experiments on the PEMS-BAY and SHMetro datasets demonstrate STGAtt's superior performance compared to state-of-the-art baselines across various prediction horizons. Visualization of attention weights confirms STGAtt's ability to adapt to dynamic traffic patterns and capture long-range dependencies, highlighting its potential for real-world traffic flow forecasting applications.