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Koopman mode decomposition for short-term traffic prediction
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Date
2025-05
Publication Type
Conference Paper
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yes
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Abstract
Traffic flow data exhibits temporal and spatial correlations and a dynamic sequential structure; therby making short-term prediction challenging. This study explores the application of the Koopman operator for traffic prediction, which transforms a nonlinear system into a linear one in an infinite-dimensional space. The data-driven nature of Koopman mode decomposition (KMD) enables it to effectively capture these spatio-temporal correlations, making it well- suited for traffic prediction. Specifically, we incorporate known spatio-temporal inter-dependencies in traffic flow to develop a physics-informed modeling pipeline. A comparison between KMD and its linear counterpart, dynamic mode decomposition (DMD), demonstrates that KMD yields more accurate predictions and handles a wider range of traffic scenarios. Future research focuses on improving the robustness of KMD by addressing challenges related to missing and noisy data, further enhancing its applicability in real-world traffic prediction.
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unpublished
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Publisher
STRC
Event
25th Swiss Transport Research Conference (STRC 2025)
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Organisational unit
08686 - Gruppe Strassenverkehrstechnik
Notes
Conference lecture held on May 15, 2025