Enhancing Traffic Prediction with Learnable Filter Module
Yuanshao Zhu Yongchao Ye Xiangyu Zhao James J. Q. Yu
Modeling future traffic conditions often relies heavily on complex
spatial-temporal neural networks to capture spatial and temporal correlations,
which can overlook the inherent noise in the data. This noise, often
manifesting as unexpected short-term peaks or drops in traffic observation, is
typically caused by traffic accidents or inherent sensor vibration. In
practice, such noise can be challenging to model due to its stochastic nature
and can lead to overfitting risks if a neural network is designed to learn this
behavior. To address this issue, we propose a learnable filter module to filter
out noise in traffic data adaptively. This module leverages the Fourier
transform to convert the data to the frequency domain, where noise is filtered
based on its pattern. The denoised data is then recovered to the time domain
using the inverse Fourier transform. Our approach focuses on enhancing the
quality of the input data for traffic prediction models, which is a critical
yet often overlooked aspect in the field. We demonstrate that the proposed
module is lightweight, easy to integrate with existing models, and can
significantly improve traffic prediction performance. Furthermore, we validate
our approach with extensive experimental results on real-world datasets,
showing that it effectively mitigates noise and enhances prediction accuracy.