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Article

Mining Topological Dependencies of Recurrent Congestion in Road Networks

1
L3S Research Center, Leibniz University Hannover, 30167 Hannover, Germany
2
Institute of Cartography and Geoinformatics, Leibniz University Hannover, 30167 Hannover, Germany
3
Data Science & Intelligent Systems Group (DSIS), University of Bonn, D-53012 Bonn, Germany
*
Author to whom correspondence should be addressed.
Academic Editors: Wolfgang Kainz, Géraldine Del Mondo, Peng Peng, Feng Lu and Jérôme Gensel
ISPRS Int. J. Geo-Inf. 2021, 10(4), 248; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040248
Received: 29 January 2021 / Revised: 19 March 2021 / Accepted: 4 April 2021 / Published: 8 April 2021
(This article belongs to the Special Issue Spatio-Temporal Models and Geo-Technologies)
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 overlooked. 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. View Full-Text
Keywords: road network analysis; recurrent congestion; spatio-temporal data mining road network analysis; recurrent congestion; spatio-temporal data mining
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MDPI and ACS Style

Tempelmeier, N.; Feuerhake, U.; Wage, O.; Demidova, E. Mining Topological Dependencies of Recurrent Congestion in Road Networks. ISPRS Int. J. Geo-Inf. 2021, 10, 248. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040248

AMA Style

Tempelmeier N, Feuerhake U, Wage O, Demidova E. Mining Topological Dependencies of Recurrent Congestion in Road Networks. ISPRS International Journal of Geo-Information. 2021; 10(4):248. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040248

Chicago/Turabian Style

Tempelmeier, Nicolas; Feuerhake, Udo; Wage, Oskar; Demidova, Elena. 2021. "Mining Topological Dependencies of Recurrent Congestion in Road Networks" ISPRS Int. J. Geo-Inf. 10, no. 4: 248. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040248

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