Urban traffic analysis and forecasting through shared Koopman eigenmodes
<|reference_start|>Urban traffic analysis and forecasting through shared Koopman eigenmodes: Predicting traffic flow in data-scarce cities is challenging due to limited historical data. To address this, we leverage transfer learning by identifying periodic patterns common to data-rich cities using a customized variant of Dynamic Mode Decomposition (DMD): constrained Hankelized DMD (TrHDMD). This method uncovers common eigenmodes (urban heartbeats) in traffic patterns and transfers them to data-scarce cities, significantly enhancing prediction performance. TrHDMD reduces the need for extensive training datasets by utilizing prior knowledge from other cities. By applying Koopman operator theory to multi-city loop detector data, we identify stable, interpretable, and time-invariant traffic modes. Injecting ``urban heartbeats'' into forecasting tasks improves prediction accuracy and has the potential to enhance traffic management strategies for cities with varying data infrastructures. Our work introduces cross-city knowledge transfer via shared Koopman eigenmodes, offering actionable insights and reliable forecasts for data-scarce urban environments.<|reference_end|>
@article{yang2024urban,
    title={Urban traffic analysis and forecasting through shared Koopman eigenmodes},
    author={Chuhan Yang, Fares B. Mehouachi, Monica Menendez, Saif Eddin Jabari},
    journal={arXiv preprint arXiv:2409.04728},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.04728},
    primaryClass={eess.SY cs.LG cs.SY eess.SP}
}