Congestion Barcodes: Exploring the Topology of Urban Congestion Using Persistent Homology

July 20, 2017 Β· Declared Dead Β· πŸ› 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC)

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Authors Yu Wu, Gabriel Shindnes, Vaibhav Karve, Derrek Yager, Daniel B. Work, Arnab Chakraborty, Richard B. Sowers arXiv ID 1707.08557 Category physics.soc-ph Cross-listed cs.DS, math.AT, physics.data-an Citations 4 Venue 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC) Last Checked 4 months ago
Abstract
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.
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