Explainable Graph Pyramid Autoformer for Long-Term Traffic Forecasting

• URL: http://arxiv.org/abs/2209.13123v1
• Date: Tue, 27 Sep 2022 02:31:06 GMT
• Title: Explainable Graph Pyramid Autoformer for Long-Term Traffic Forecasting
• Authors: Weiheng Zhong, Tanwi Mallick, Hadi Meidani, Jane Macfarlane, Prasanna Balaprakash
• Abstract summary: We develop an explainable attention-based spatial-temporal graph neural network that uses a novel pyramid autocorrelation attention mechanism. Our model can achieve up to 35 % better long-term traffic forecast accuracy than that of several state-of-the-art methods.
• Score: 3.5908670236727933
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate traffic forecasting is vital to an intelligent transportation system. Although many deep learning models have achieved state-of-art performance for short-term traffic forecasting of up to 1 hour, long-term traffic forecasting that spans multiple hours remains a major challenge. Moreover, most of the existing deep learning traffic forecasting models are black box, presenting additional challenges related to explainability and interpretability. We develop Graph Pyramid Autoformer (X-GPA), an explainable attention-based spatial-temporal graph neural network that uses a novel pyramid autocorrelation attention mechanism. It enables learning from long temporal sequences on graphs and improves long-term traffic forecasting accuracy. Our model can achieve up to 35 % better long-term traffic forecast accuracy than that of several state-of-the-art methods. The attention-based scores from the X-GPA model provide spatial and temporal explanations based on the traffic dynamics, which change for normal vs. peak-hour traffic and weekday vs. weekend traffic.

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