Congestion Barcodes: Exploring the Topology of Urban Congestion Using Persistent Homology
Physics and Society
2017-07-27 v1 Data Structures and Algorithms
Algebraic Topology
Data Analysis, Statistics and Probability
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.
Keywords
Cite
@article{arxiv.1707.08557,
title = {Congestion Barcodes: Exploring the Topology of Urban Congestion Using Persistent Homology},
author = {Yu Wu and Gabriel Shindnes and Vaibhav Karve and Derrek Yager and Daniel B. Work and Arnab Chakraborty and Richard B. Sowers},
journal= {arXiv preprint arXiv:1707.08557},
year = {2017}
}
Comments
9 pages, 15 figures, Accepted to IEEE 20th International Conference on Intelligent Transportation Systems 2017