# Spatiotemporal Road Traffic Anomaly Detection: A Tensor-Based Approach
## 6 Conclusions  
For the development of the more secure, cleaner, and overall more sustainable cities, traffic congestion and corresponding anomalies must be addressed. This paper presents a novel method for the extraction of road traffic patterns and anomaly detection using tensor-based method. It integrates a tensor decomposition with the anomaly detection approach based on estimating the CoM of the observed traffic pattern represented by the STM. This method is evaluated on a large real-life GNSS road traffic dataset and validated using the domain knowledge data. The result presents the valuable traffic insights useful for the routing application, responsible urban planners, and road infrastructure maintenance authorities. It can be used as valuable traffic information about the need for infrastructure expansion, additional improvement strategies, or to analyze the traffic influence of the new road infrastructure.  
Compared to other approaches related to road traffic anomaly detection, the proposed method is more focused on detecting the anomalies that affect the traffic flow and could lead to dangerous situations and, consequently, to traffic accidents. Furthermore, anomaly  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Literature** & **Data Type** & **Traffic Parameter** & **Anomaly Detection** \\ \hline [PERSON] et al. [9] & O-D matrices (car) & Traffic volume & Traffic flow or topology \\ \hline [PERSON] et al. [18] & O-D matrices (car) & Traffic volume & Traffic Flow \\ \hline [PERSON] et al. [46] & O-D matrices (car) & Traffic volume & Event detection \\ \hline [PERSON] et al. [48] & GNSS (bicycle) & Traffic volume & Event detection \\ \hline [PERSON] et al. [47] & Simulation (car) & Traffic speed & Traffic patterns \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the proposed approach to other approaches for the traffic anomaly detection.  
detection will include the expansion of the proposed method for the real-time anomaly detection framework.  
Conceptualization, L.T. and T.C.; methodology, L.T., S.F. and J.G.; software, L.T.; validation, L.T. and T.C.; formal analysis, L.T.; investigation, L.T.; resources, T.C.; data curation, L.T. and T.C.; writing--original draft preparation, L.T. and S.F.; writing--review and editing, L.T., S.F., T.C. and J.G.; visualization, L.T.; supervision, T.C. and J.G.; project administration, T.C.; funding acquisition, T.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by European Regional Development Fund under the grant KK.01.1.1.01.0009 (DATACROSS). [PERSON] acknowledges the support of FCT (Fundacao para a Ciencia e a Tecnologia) via the Ph.D. scholarship PD/BD/114189/2016.  
The data used for this research was collected during the project SORDITO, European Regional Development Fund under contract RC.2.2.08-0022. This work has been partly supported by the University of Zagreb and the Faculty of Transport and Traffic Sciences under the grant \"Innovative Models and Control Strategies for Sustainable Mobility in Smart Cities\" and \"Optimization of the Line Transport Timetables for the Case of Electric Vehicles: A Proof of Concept\".  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
CoM Center of Mass  
CORCONDIA Core Consistency Diagnostic  
CP CANDECOMP/PARAFAC  
GNSS Global Navigation Satellite System  
HCM Highway Capacity Manual  
ITS Intelligent Transport Systems  
NTD Non-negative Tensor Decomposition  
O-D Origin-Destination  
PCA Principal Components Analysis  
STM Speed Transition Matrix