Anti-circulant dynamic mode decomposition with sparsity-promoting for highway traffic dynamics analysis

TL;DR

This paper introduces circDMDsp, a novel data-driven method for analyzing highway traffic dynamics that effectively extracts meaningful spatiotemporal patterns, denoises data, and improves prediction accuracy in noisy, high-dimensional settings.

Contribution

The paper develops circDMDsp, a new anti-circulant dynamic mode decomposition approach with sparsity promotion, addressing limitations of existing DMD models in traffic data analysis.

Findings

Traffic speed data exhibits periodic spatial-temporal patterns.

circDMDsp outperforms other DMD models in data reconstruction.

The method effectively denoises data and predicts future traffic states.

Abstract

Highway traffic states data collected from a network of sensors can be considered a high-dimensional nonlinear dynamical system. In this paper, we develop a novel data-driven method -- anti-circulant dynamic mode decomposition with sparsity-promoting (circDMDsp) -- to study the dynamics of highway traffic speed data. Particularly, circDMDsp addresses several issues that hinder the application of existing DMD models: limited spatial dimension, presence of both recurrent and non-recurrent patterns, high level of noise, and known mode stability. The proposed circDMDsp framework allows us to numerically extract spatial-temporal coherent structures with physical meanings/interpretations: the dynamic modes reflect coherent spatial bases, and the corresponding temporal patterns capture the temporal oscillation/evolution of these dynamic modes. Our result based on Seattle highway loop detector data showcases that traffic speed data is governed by a set of periodic components, e.g., mean pattern, daily pattern, and weekly pattern, and each of them has a unique spatial structure. The spatiotemporal patterns can also be used to recover/denoise observed data and predict future values at any timestamp by extrapolating the temporal Vandermonde matrix. Our experiments also demonstrate that the proposed circDMDsp framework is more accurate and robust in data reconstruction and prediction than other DMD-based models.