Urban traffic analysis and forecasting through shared Koopman eigenmodes

• URL: http://arxiv.org/abs/2409.04728v1
• Date: Sat, 7 Sep 2024 06:24:50 GMT
• Title: Urban traffic analysis and forecasting through shared Koopman eigenmodes
• Authors: Chuhan Yang, Fares B. Mehouachi, Monica Menendez, Saif Eddin Jabari,
• Abstract summary: Predicting traffic flow in data-scarce cities is challenging due to limited historical data. We leverage transfer learning by identifying periodic patterns common to data-rich cities. This method uncovers common eigenmodes (urban heartbeats) in traffic patterns and transfers them to data-scarce cities.
• Score: 5.207485728774798
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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