Anti-circulant dynamic mode decomposition with sparsity-promoting for highway traffic dynamics analysis
arXiv:2302.07108 · doi:10.1016/j.trc.2023.104178
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.
References in corpus (12)
- A Data-Driven Approximation of the Koopman Operator: Extending Dynamic Mode Decomposition
- Sparsity-promoting dynamic mode decomposition
- Koopman invariant subspaces and finite linear representations of nonlinear dynamical systems for control
- Extracting spatial-temporal coherent patterns in large-scale neural recordings using dynamic mode decomposition
- Ergodic theory, Dynamic Mode Decomposition and Computation of Spectral Properties of the Koopman operator
- On Convergence of Extended Dynamic Mode Decomposition to the Koopman Operator
- De-Biasing the Dynamic Mode Decomposition for Applied Koopman Spectral Analysis
- Characterizing and correcting for the effect of sensor noise in the dynamic mode decomposition
- Bayesian Temporal Factorization for Multidimensional Time Series Prediction
- Compressed Dynamic Mode Decomposition for Background Modeling
- Real-time forecasting of metro origin-destination matrices with high-order weighted dynamic mode decomposition
- Hankel-structured Tensor Robust PCA for Multivariate Traffic Time Series Anomaly Detection