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Congestion Barcodes: Exploring the Topology of Urban Congestion Using Persistent Homology
Yu Wu, Gabriel Shindnes, Vaibhav Karve, Derrek Yager, Daniel B. Work, Arnab Chakraborty, Richard B. Sowers
This work presents a new method to quantify connectivity in transportation networks. Inspired by the field of topological data analysis, we propose a novel approach to explore the robustness of road network connectivity in the presence of congestion on the roadway. The robustness of the pattern is summarized in a congestion barcode, which can be constructed directly from traffic datasets commonly used for navigation. As an initial demonstration, we illustrate the main technique on a publicly available traffic dataset in a neighborhood in New York City.