Probabilistic Traffic Forecasting with Dynamic Regression

TL;DR

This paper introduces a dynamic regression framework that improves traffic forecasting models by modeling error processes with autoregressive structures, enabling probabilistic predictions and better interpretability.

Contribution

It presents a novel integration of autoregressive error modeling into deep traffic forecasting models, enhancing their probabilistic capabilities and interpretability.

Findings

Improved forecasting accuracy on speed and flow datasets

Enhanced interpretability through AR coefficients and covariance matrices

Joint optimization of base and error models

Abstract

This paper proposes a dynamic regression (DR) framework that enhances existing deep spatiotemporal models by incorporating structured learning for the error process in traffic forecasting. The framework relaxes the assumption of time independence by modeling the error series of the base model (i.e., a well-established traffic forecasting model) using a matrix-variate autoregressive (AR) model. The AR model is integrated into training by redesigning the loss function. The newly designed loss function is based on the likelihood of a non-isotropic error term, enabling the model to generate probabilistic forecasts while preserving the original outputs of the base model. Importantly, the additional parameters introduced by the DR framework can be jointly optimized alongside the base model. Evaluation on state-of-the-art (SOTA) traffic forecasting models using speed and flow datasets demonstrates improved performance, with interpretable AR coefficients and spatiotemporal covariance matrices enhancing the understanding of the model.