Quantifying Traffic Patterns with Percolation Theory: A Case Study of Seoul Roads
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866912243513294848 |
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| author | Kwon, Yongsung Lee, Mi Jin Son, Seung-Woo |
| author_facet | Kwon, Yongsung Lee, Mi Jin Son, Seung-Woo |
| contents | Urban traffic systems are characterized by dynamic interactions between congestion and free-flow states, influenced by human activity and road topology. This study employs percolation theory to analyze traffic dynamics in Seoul, focusing on the transition point $q_c$ and Fisher exponent $τ$. The transition point $q_c$ quantifies the robustness of the free-flow clusters, while the exponent $τ$ captures the spatial fragmentation of the traffic networks. Our analysis reveals temporal variations in these metrics, with lower $q_c$ and lower $τ$ values during rush hours representing low-dimensional behavior. Weight-weight correlations are found to significantly impact cluster formation, driving the early onset of dominant traffic states. Comparisons with uncorrelated models highlight the role of real-world correlations. This approach provides a comprehensive framework for evaluating traffic resilience and informs strategies to optimize urban transportation systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_05159 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quantifying Traffic Patterns with Percolation Theory: A Case Study of Seoul Roads Kwon, Yongsung Lee, Mi Jin Son, Seung-Woo Physics and Society Urban traffic systems are characterized by dynamic interactions between congestion and free-flow states, influenced by human activity and road topology. This study employs percolation theory to analyze traffic dynamics in Seoul, focusing on the transition point $q_c$ and Fisher exponent $τ$. The transition point $q_c$ quantifies the robustness of the free-flow clusters, while the exponent $τ$ captures the spatial fragmentation of the traffic networks. Our analysis reveals temporal variations in these metrics, with lower $q_c$ and lower $τ$ values during rush hours representing low-dimensional behavior. Weight-weight correlations are found to significantly impact cluster formation, driving the early onset of dominant traffic states. Comparisons with uncorrelated models highlight the role of real-world correlations. This approach provides a comprehensive framework for evaluating traffic resilience and informs strategies to optimize urban transportation systems. |
| title | Quantifying Traffic Patterns with Percolation Theory: A Case Study of Seoul Roads |
| topic | Physics and Society |
| url | https://arxiv.org/abs/2501.05159 |