Mining Topological Dependencies of Recurrent Congestion in Road Networks

TL;DR

This paper introduces ST-Discovery, an unsupervised data mining algorithm that uncovers topological dependencies causing recurrent congestion in urban road networks by analyzing traffic data and identifying spatio-temporal correlations.

Contribution

The paper presents a novel algorithm for data-driven discovery of topological dependencies in road networks, focusing on recurrent congestion patterns caused by network topology.

Findings

Effectively reveals topological dependencies in traffic data

Identifies spatio-temporal correlations in congestion patterns

Demonstrates improved understanding of recurrent congestion causes

Abstract

The discovery of spatio-temporal dependencies within urban road networks that cause Recurrent Congestion (RC) patterns is crucial for numerous real-world applications, including urban planning and scheduling of public transportation services. While most existing studies investigate temporal patterns of RC phenomena, the influence of the road network topology on RC is often overlooked. This article proposes the ST-Discovery algorithm, a novel unsupervised spatio-temporal data mining algorithm that facilitates the effective data-driven discovery of RC dependencies induced by the road network topology using real-world traffic data. We factor out regularly reoccurring traffic phenomena, such as rush hours, mainly induced by the daytime, by modelling and systematically exploiting temporal traffic load outliers. We present an algorithm that first constructs connected subgraphs of the road network based on the traffic speed outliers. Second, the algorithm identifies pairs of subgraphs that indicate spatio-temporal correlations in their traffic load behaviour to identify topological dependencies within the road network. Finally, we rank the identified subgraph pairs based on the dependency score determined by our algorithm. Our experimental results demonstrate that ST-Discovery can effectively reveal topological dependencies in urban road networks.