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Pregled bibliografske jedinice broj: 1085191

Spatiotemporal Traffic Anomaly Detection on Urban Road Network Using Tensor Decomposition Method

Tišljarić, Leo; Fernandes, Sofia; Carić, Tonči; Gama, João
Spatiotemporal Traffic Anomaly Detection on Urban Road Network Using Tensor Decomposition Method // Discovery Science / Appice, Annalisa ; Tsoumakas, Grigorios ; Manolopoulos, Yannis ; Matwin, Stan (ur.).
Cham: Springer, 2020. str. 674-688 doi:10.1007/978-3-030-61527-7_44 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)

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Naslov
Spatiotemporal Traffic Anomaly Detection on Urban Road Network Using Tensor Decomposition Method

Autori
Tišljarić, Leo ; Fernandes, Sofia ; Carić, Tonči ; Gama, João

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Discovery Science / Appice, Annalisa ; Tsoumakas, Grigorios ; Manolopoulos, Yannis ; Matwin, Stan - Cham : Springer, 2020, 674-688

ISBN
978-3-030-61527-7

Skup
23rd International Conference on Discovery Science (DS 2020)

Mjesto i datum
Online, 19.10.2020. - 21.10.2020

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Road traffic anomaly detection ; Tensor decomposition methods ; Speed probability distribution ; Intelligent transport systems ; Traffic state estimation

Sažetak
Tensor-based models emerged only recently in modeling and analysis of the spatiotemporal road traffic data. They outperform other data models regarding the property of simultaneously capturing both spatial and temporal components of the observed traffic dataset. In this paper, the nonnegative tensor decomposition method is used to extract traffic patterns in the form of Speed Transition Matrix (STM). The STM is presented as the approach for modeling the large sparse Floating Car Data (FCD). The anomaly of the traffic pattern is estimated using Kullback–Leibler divergence between the observed traffic pattern and the average traffic pattern. Experiments were conducted on the large sparse FCD dataset for the most relevant road segments in the City of Zagreb, which is the capital and largest city in Croatia. Results show that the method was able to detect the most anomalous traffic road segments, and with analysis of the extracted spatial and temporal components, conclusions could be drawn about the causes of the anomalies. Results are validated by using the domain knowledge from the Highway Capacity Manual and achieved a precision score value of more than 90%. Therefore, such valuable traffic information can be used in routing applications and urban traffic planning.

Izvorni jezik
Engleski

Znanstvena područja
Tehnologija prometa i transport, Interdisciplinarne tehničke znanosti, Informacijske i komunikacijske znanosti

Napomena
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12323)
Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 12323)



POVEZANOST RADA

Projekti:
--KK.01.1.1.01.009 - Napredne metode i tehnologije u znanosti o podatcima i kooperativnim sustavima (DATACROSS) (Šmuc, Tomislav; Lončarić, Sven; Petrović, Ivan; Jokić, Andrej; Palunko, Ivana) ( CroRIS)

Ustanove:
Fakultet prometnih znanosti, Zagreb

Profili:

Avatar Url Leo Tišljarić (autor)

Avatar Url Tonči Carić (autor)

Poveznice na cjeloviti tekst rada:

doi link.springer.com

Citiraj ovu publikaciju:

Tišljarić, Leo; Fernandes, Sofia; Carić, Tonči; Gama, João
Spatiotemporal Traffic Anomaly Detection on Urban Road Network Using Tensor Decomposition Method // Discovery Science / Appice, Annalisa ; Tsoumakas, Grigorios ; Manolopoulos, Yannis ; Matwin, Stan (ur.).
Cham: Springer, 2020. str. 674-688 doi:10.1007/978-3-030-61527-7_44 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
Tišljarić, L., Fernandes, S., Carić, T. & Gama, J. (2020) Spatiotemporal Traffic Anomaly Detection on Urban Road Network Using Tensor Decomposition Method. U: Appice, A., Tsoumakas, G., Manolopoulos, Y. & Matwin, S. (ur.)Discovery Science doi:10.1007/978-3-030-61527-7_44.
@article{article, author = {Ti\v{s}ljari\'{c}, Leo and Fernandes, Sofia and Cari\'{c}, Ton\v{c}i and Gama, Jo\~{a}o}, year = {2020}, pages = {674-688}, DOI = {10.1007/978-3-030-61527-7\_44}, keywords = {Road traffic anomaly detection, Tensor decomposition methods, Speed probability distribution, Intelligent transport systems, Traffic state estimation}, doi = {10.1007/978-3-030-61527-7\_44}, isbn = {978-3-030-61527-7}, title = {Spatiotemporal Traffic Anomaly Detection on Urban Road Network Using Tensor Decomposition Method}, keyword = {Road traffic anomaly detection, Tensor decomposition methods, Speed probability distribution, Intelligent transport systems, Traffic state estimation}, publisher = {Springer}, publisherplace = {online} }
@article{article, author = {Ti\v{s}ljari\'{c}, Leo and Fernandes, Sofia and Cari\'{c}, Ton\v{c}i and Gama, Jo\~{a}o}, year = {2020}, pages = {674-688}, DOI = {10.1007/978-3-030-61527-7\_44}, keywords = {Road traffic anomaly detection, Tensor decomposition methods, Speed probability distribution, Intelligent transport systems, Traffic state estimation}, doi = {10.1007/978-3-030-61527-7\_44}, isbn = {978-3-030-61527-7}, title = {Spatiotemporal Traffic Anomaly Detection on Urban Road Network Using Tensor Decomposition Method}, keyword = {Road traffic anomaly detection, Tensor decomposition methods, Speed probability distribution, Intelligent transport systems, Traffic state estimation}, publisher = {Springer}, publisherplace = {online} }

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