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

TL;DR

This paper introduces a topological data analysis method called congestion barcodes to quantify and analyze the robustness of urban road network connectivity under congestion, demonstrated on NYC traffic data.

Contribution

It presents a novel application of persistent homology to transportation networks, providing a new way to assess network robustness amid congestion.

Findings

Congestion barcodes effectively capture network connectivity robustness.

The method can be directly applied to existing traffic datasets.

Initial results demonstrate the approach on NYC neighborhood data.

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.