A Multi-Channel Spatial-Temporal Transformer Model for Traffic Flow Forecasting

TL;DR

This paper introduces a multi-channel spatial-temporal transformer model that enhances traffic flow forecasting accuracy by integrating graph convolutional networks and adaptive adjacency matrices to better capture spatial and temporal dependencies.

Contribution

The paper proposes a novel multi-channel transformer architecture with adaptive adjacency matrices for improved traffic flow prediction accuracy.

Findings

Outperforms state-of-the-art models on six real-world datasets.

Multi-channel mechanism significantly improves forecasting performance.

Adaptive adjacency matrix effectively captures dynamic spatial dependencies.

Abstract

Traffic flow forecasting is a crucial task in transportation management and planning. The main challenges for traffic flow forecasting are that (1) as the length of prediction time increases, the accuracy of prediction will decrease; (2) the predicted results greatly rely on the extraction of temporal and spatial dependencies from the road networks. To overcome the challenges mentioned above, we propose a multi-channel spatial-temporal transformer model for traffic flow forecasting, which improves the accuracy of the prediction by fusing results from different channels of traffic data. Our approach leverages graph convolutional network to extract spatial features from each channel while using a transformer-based architecture to capture temporal dependencies across channels. We introduce an adaptive adjacency matrix to overcome limitations in feature extraction from fixed topological structures. Experimental results on six real-world datasets demonstrate that introducing a multi-channel mechanism into the temporal model enhances performance and our proposed model outperforms state-of-the-art models in terms of accuracy.