论文标题
可解释可长期流量预测的可解释金字塔自动构造
Explainable Graph Pyramid Autoformer for Long-Term Traffic Forecasting
论文作者
论文摘要
准确的交通预测对于智能运输系统至关重要。尽管许多深度学习模型已经达到了最新的1小时交通预测的最新性能，但长期流量预测跨越多小时仍然是一个主要挑战。此外，大多数现有的深度学习流量预测模型都是黑匣子，提出了与解释性和解释性有关的其他挑战。我们开发了图形金字塔自动构造（X-GPA），这是一种基于注意力集中的时空图神经网络，它使用了新型的金字塔自相关注意机制。它可以从图表上的长时间序列中学习并提高长期流量预测准确性。与几种最先进的方法相比，我们的模型可以实现高达35％的长期流量预测准确性。 X-GPA模型的基于注意力的分数提供了基于交通动态的空间和时间解释，这些解释会改变正常与高峰时段的流量以及工作日与周末流量的变化。
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.
