ST-Discovery: Data-Driven Discovery of Structural Dependencies in Urban Road Networks

Nicolas Tempelmeier; Udo Feuerhake; Oskar Wage; Elena Demidova

doi:10.1145/3347146.3359109

ST-Discovery: Data-Driven Discovery of Structural Dependencies in Urban Road Networks

Nicolas Tempelmeier, Udo Feuerhake, Oskar Wage, Elena Demidova

Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review

Abstract

The discovery of structural dependencies that cause correlated congestion patterns within urban road networks is of crucial importance for numerous real-world applications, including urban planning and scheduling of public transportation services. These dependencies can often result from the road network topology, are often not well understood and can become apparent under an increased traffic load. In this paper we propose the data-driven ST-Discovery approach that facilitates the effective discovery of structural dependencies using historical traffic flow data.

Originalsprache	Englisch
Titel des Sammelwerks	SIGSPATIAL '19
Untertitel	Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Herausgeber/-innen	Farnoush Banaei-Kashani, Goce Trajcevski, Ralf Harmut Güting, Lars Kulik, Shawn Newsam
Erscheinungsort	New York
Herausgeber (Verlag)	Association for Computing Machinery (ACM)
Seiten	488-491
Seitenumfang	4
ISBN (elektronisch)	9781450369091
DOIs	https://doi.org/10.1145/3347146.3359109
Publikationsstatus	Veröffentlicht - 5 Nov. 2019
Veranstaltung	SIGSPATIAL '19: 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems - Chicago, USA / Vereinigte Staaten Dauer: 5 Nov. 2019 → 8 Nov. 2019

Publikationsreihe

Name	GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems

Konferenz

Konferenz	SIGSPATIAL '19
Land/Gebiet	USA / Vereinigte Staaten
Zeitraum	5 Nov. 2019 → 8 Nov. 2019

UN-Ziele für nachhaltige Entwicklung (SDGs)

2015 einigten sich die UN-Mitgliedstaaten auf 17 globale Ziele für nachhaltige Entwicklung (Sustainable Development Goals, SDGs) zur Beendigung von Armut, zum Schutz des Planeten und zur Förderung des allgemeinen Wohlstands. Hiermit leisten wir einen Beitrag zu folgendem/n Ziel(en) für nachhaltige Entwicklung (SDGs):

SDG 9 Industrie, Innovation und Infrastruktur
SDG 11 Nachhaltige Städte und Gemeinschaften

ASJC Scopus Sachgebiete

Erdoberflächenprozesse
Angewandte Informatik
Modellierung und Simulation
Computergrafik und computergestütztes Design
Information systems

Zugriff auf Dokument

10.1145/3347146.3359109

Andere Dateien und Links

Verknüpfung zur Publikation in Scopus

Dieses zitieren

Tempelmeier, N., Feuerhake, U., Wage, O., & Demidova, E. (2019). ST-Discovery: Data-Driven Discovery of Structural Dependencies in Urban Road Networks. in F. Banaei-Kashani, G. Trajcevski, R. H. Güting, L. Kulik, & S. Newsam (Hrsg.), SIGSPATIAL '19: Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (S. 488-491). (GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems). Association for Computing Machinery (ACM). https://doi.org/10.1145/3347146.3359109

Tempelmeier, Nicolas ; Feuerhake, Udo ; Wage, Oskar et al. / ST-Discovery : Data-Driven Discovery of Structural Dependencies in Urban Road Networks. SIGSPATIAL '19: Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. Hrsg. / Farnoush Banaei-Kashani ; Goce Trajcevski ; Ralf Harmut Güting ; Lars Kulik ; Shawn Newsam. New York : Association for Computing Machinery (ACM), 2019. S. 488-491 (GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems).

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abstract = "The discovery of structural dependencies that cause correlated congestion patterns within urban road networks is of crucial importance for numerous real-world applications, including urban planning and scheduling of public transportation services. These dependencies can often result from the road network topology, are often not well understood and can become apparent under an increased traffic load. In this paper we propose the data-driven ST-Discovery approach that facilitates the effective discovery of structural dependencies using historical traffic flow data.",

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Tempelmeier, N, Feuerhake, U , Wage, O & Demidova, E 2019, ST-Discovery: Data-Driven Discovery of Structural Dependencies in Urban Road Networks. in F Banaei-Kashani, G Trajcevski, RH Güting, L Kulik & S Newsam (Hrsg.), SIGSPATIAL '19: Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems, Association for Computing Machinery (ACM), New York, S. 488-491, SIGSPATIAL '19, USA / Vereinigte Staaten, 5 Nov. 2019. https://doi.org/10.1145/3347146.3359109

ST-Discovery: Data-Driven Discovery of Structural Dependencies in Urban Road Networks. / Tempelmeier, Nicolas; Feuerhake, Udo ; Wage, Oskar et al.
SIGSPATIAL '19: Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. Hrsg. / Farnoush Banaei-Kashani; Goce Trajcevski; Ralf Harmut Güting; Lars Kulik; Shawn Newsam. New York: Association for Computing Machinery (ACM), 2019. S. 488-491 (GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems).

Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review

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AB - The discovery of structural dependencies that cause correlated congestion patterns within urban road networks is of crucial importance for numerous real-world applications, including urban planning and scheduling of public transportation services. These dependencies can often result from the road network topology, are often not well understood and can become apparent under an increased traffic load. In this paper we propose the data-driven ST-Discovery approach that facilitates the effective discovery of structural dependencies using historical traffic flow data.

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Tempelmeier N, Feuerhake U , Wage O, Demidova E. ST-Discovery: Data-Driven Discovery of Structural Dependencies in Urban Road Networks. in Banaei-Kashani F, Trajcevski G, Güting RH, Kulik L, Newsam S, Hrsg., SIGSPATIAL '19: Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. New York: Association for Computing Machinery (ACM). 2019. S. 488-491. (GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems). doi: 10.1145/3347146.3359109