Mining topological dependencies of recurrent congestion in road networks

verfasst von
Nicolas Tempelmeier, Udo Feuerhake, Oskar Wage, Elena Demidova
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 the 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 over-looked. This article proposes the ST-DISCOVERY algorithm, a novel unsupervised spatio-temporal data mining algorithm that facilitates 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.

Organisationseinheit(en)
Forschungszentrum L3S
Institut für Kartographie und Geoinformatik
Externe Organisation(en)
Rheinische Friedrich-Wilhelms-Universität Bonn
Typ
Artikel
Journal
ISPRS International Journal of Geo-Information
Band
10
ISSN
2220-9964
Publikationsdatum
08.04.2021
Publikationsstatus
Veröffentlicht
Peer-reviewed
Ja
ASJC Scopus Sachgebiete
Geografie, Planung und Entwicklung, Computer in den Geowissenschaften, Erdkunde und Planetologie (sonstige)
Ziele für nachhaltige Entwicklung
SDG 9 – Industrie, Innovation und Infrastruktur, SDG 11 – Nachhaltige Städte und Gemeinschaften
Elektronische Version(en)
https://doi.org/10.3390/ijgi10040248 (Zugang: Offen)