Spatiotemporal Traffic Anomaly Detection on Urban Road Network Using Tensor Decomposition Method (CROSBI ID 695000)
Prilog sa skupa u časopisu | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Tišljarić, Leo ; Fernandes, Sofia ; Carić, Tonči ; Gama, João
engleski
Spatiotemporal Traffic Anomaly Detection on Urban Road Network Using Tensor Decomposition Method
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.
Road traffic anomaly detection ; Tensor decomposition methods ; Speed probability distribution ; Intelligent transport systems ; Traffic state estimation
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)
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Podaci o prilogu
674-688.
2020.
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objavljeno
10.1007/978-3-030-61527-7_44
Podaci o matičnoj publikaciji
Lecture notes in computer science
Appice, Annalisa ; Tsoumakas, Grigorios ; Manolopoulos, Yannis ; Matwin, Stan
Cham: Springer
978-3-030-61527-7
0302-9743
1611-3349
Podaci o skupu
23rd International Conference on Discovery Science (DS 2020)
predavanje
19.10.2020-21.10.2020
online
Povezanost rada
Informacijske i komunikacijske znanosti, Interdisciplinarne tehničke znanosti, Tehnologija prometa i transport