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A Modularity-Driven Framework for Unraveling Congestion Centers with Enhanced Spatial-Semantic Features

Authors Weihua Huan , Xintao Liu , Wei Huang

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LIPIcs.GIScience.2025.7.pdf
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ACM Subject Classification
  • Information systems → Geographic information systems
Keywords
  • Congestion center
  • Temporal granularity
  • Fast Fourier Transform
  • Local dominance

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Abstract

The propagation of traffic congestion is a complicated spatiotemporal phenomenon in urban networks. Extensive studies mainly relied on dynamic Bayesian network or deep learning approaches. However, they often struggle to adapt seamlessly to diverse data granularities, limiting their applicability. In this study, we propose a modularity-driven method to unravel the spatiotemporal congestion propagation centers, effectively addressing temporal granularity challenges through the use of the fast Fourier Transform (FFT). Our framework distinguishes itself due to its capacity to integrate enhanced spatial-semantic features while eliminating temporal granularity dependence, which consists of two data-driven modules. One is adaptive adjacency matrix learning module, which captures the spatiotemporal relationship from evolving congestion graphs by fusing node degree, spatial proximity, and the FFT of traffic state indices. The other one is local search module, which employs local dominance principles to unravel the congestion propagation centers. We validate our proposed methodology on the large-scale traffic networks in New York City, the United States. An ablation study on the dataset reveals that the combination of the three features achieves the highest modularity scores of 0.65. The contribution of our work is to provide a novel way to infer the propagation centers of traffic congestion, and reveals the flexibility of extending our framework at temporal scales. The network resilience and dynamic evolution of the identified congestion centers can provide implications for actional decisions.

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Weihua Huan, Xintao Liu, and Wei Huang. A Modularity-Driven Framework for Unraveling Congestion Centers with Enhanced Spatial-Semantic Features. In 13th International Conference on Geographic Information Science (GIScience 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 346, pp. 7:1-7:11, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025) https://doi.org/10.4230/LIPIcs.GIScience.2025.7

Author Details

Weihua Huan
  • College of Surveying and Geo-informatics, Tongji University, Shanghai, China
  • Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong
Xintao Liu
  • Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong
Wei Huang
  • College of Surveying and Geo-informatics, Tongji University, Shanghai, China
  • Department of Civil Engineering, Toronto Metropolitan University, Canada
  • Urban Mobility Institute, Tongji University, Shanghai, China

Acknowledgements

I want to thank my supervisor Wei Huang from Tongji University, China and co-supervisor Xintao Liu from the Hong Kong Polytechnic University, China.

Supplementary Materials

References

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