# Spatiotemporal Road Traffic Anomaly Detection: A Tensor-Based Approach
## 2 Related Work
### Road Traffic Anomaly Detection Approaches  
Many anomaly detection methods are developed for specific application domains, while there are some more generic methods. Review papers on anomaly detection focus only on some outlier detection categories like statistical or pattern mining methods. Most of the review papers present anomaly detection methods not explicitly designed for some area [24]. [PERSON] et al. [25] presented the review on local outlier detection with an application on spatial data, video, and network outlier detection methods. In [26; 27] the authors presented an overview of the methods related to the trajectory of data mining techniques. Methods for various tasks related to the mining of trajectory data are presented, like trajectory pattern mining, anomaly detection, movement behavioral analysis, and trajectory classification. [PERSON] et al. [28] presented the review paper on the detection of temporal anomalies. It gives an overview of various data types like time series data, data streams, distributed data, spatiotemporal data, and network data. Methods for anomaly detection are presented for each data type. The most recent survey paper [29] gives a comprehensive review of traffic anomaly detection methods in an urban area context. It divides the anomaly detection methods into two main categories: Trajectory and traffic-related anomalies.  
There are three general approaches in anomaly detection: (i) Model-based, (ii) proximity-based and (iii) density-based methods [30]. Model-based methods include statistical models based on the assumption that normal observation has a much higher probability of occurrence in the model than the outlier occurrence. Capturing data is fitted to the statistical model, and a statistical interference test is applied to determine if the data behaves according to that model or not [31]. Proximity-based approaches are distance-based anomaly detection methods. Anomalous observations are those values that are the most distant from all of the other values [32]. Density-based methods estimate the density of observations, and the anomaly is detected as the observation with low density when compared to its local neighbors [33].  
This paper presents the novel paradigm for the anomaly detection of the traffic networks using the STM. The proposed measure explains an anomalous traffic state as unexpected traffic flow behavior and avoids detecting the recurrent congestion as an anomaly as the anomaly is defined as sudden breaks and intense accelerations events. Many road traffic anomaly detection methods are based on the detection of the large deviations within the traffic parameter observed in a defined time period. When analyzing the hourly-hour data, recurrent traffic congestion could be wrongly detected as an anomaly because it represents the peak traffic load in rush hours that do not occur simultaneously with the same intensity on the whole city-wide area. For example, many anomaly detection methods based on the computation of the traffic volume cannot detect the anomaly in some time interval if the daily average traffic volume is not changed. Based on the STM, the proposed approach does not suffer from false anomaly detection, and it is adaptable for near real-time and real-time anomaly detection applications.