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Explainable Graph Pyramid Autoformer for Long-Term Traffic Forecasting by Zhong, Weiheng; Mallick, Tanwi; Meidani, Hadi; Macfarlane, Jane; Balaprakash, Prasanna is a scholarly article available to read on EtoBox.

What is Explainable Graph Pyramid Autoformer for Long-Term Traffic Forecasting about?

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.

Author

Zhong, Weiheng; Mallick, Tanwi; Meidani, Hadi; Macfarlane, Jane; Balaprakash, Prasanna

Published

2022

Language

EN