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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.09530 |
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| _version_ | 1866913939171835904 |
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| author | Lee, Garyoung Jha, Aryaman Wiesenfeld, Kurt Laval, Jorge |
| author_facet | Lee, Garyoung Jha, Aryaman Wiesenfeld, Kurt Laval, Jorge |
| contents | Traffic congestion, a daily frustration for millions and a multi-billion dollar drain on economies, has long resisted deep physical understanding. While simple theoretical models of traffic flow have suggested connections to critical phenomena and non-equilibrium universality, direct empirical validation is lacking. Using extensive, high-resolution vehicle trajectory data from the I-24 MOTION testbed, we show that traffic flow exhibits both a percolation phase transition that is self-organized critical and fluctuations consistent with the Kardar-Parisi-Zhang universality in 1+1 dimensions. This suggests that the complex and seemingly chaotic formation of traffic jams has predictable statistical properties, which opens new avenues in traffic science for developing advanced forecasting and management strategies grounded in universal scaling laws. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_09530 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Universal Scaling Laws in Freeway Traffic Lee, Garyoung Jha, Aryaman Wiesenfeld, Kurt Laval, Jorge Chaotic Dynamics Data Analysis, Statistics and Probability Traffic congestion, a daily frustration for millions and a multi-billion dollar drain on economies, has long resisted deep physical understanding. While simple theoretical models of traffic flow have suggested connections to critical phenomena and non-equilibrium universality, direct empirical validation is lacking. Using extensive, high-resolution vehicle trajectory data from the I-24 MOTION testbed, we show that traffic flow exhibits both a percolation phase transition that is self-organized critical and fluctuations consistent with the Kardar-Parisi-Zhang universality in 1+1 dimensions. This suggests that the complex and seemingly chaotic formation of traffic jams has predictable statistical properties, which opens new avenues in traffic science for developing advanced forecasting and management strategies grounded in universal scaling laws. |
| title | Universal Scaling Laws in Freeway Traffic |
| topic | Chaotic Dynamics Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2507.09530 |