# An Analysis of ML-Based Outlier Detection from Mobile Phone Trajectories
## 2 Related Work  
The use of _mobile crowd sensing (MCS)_[7] applications for urban planning within the context of Smart Cities has been increasing over the last decade. For instance, [PERSON] et al. analyse mobile phone traces to detect specific Pols such as home and work [2]. The authors show that mobile trajectories can be used to infer specific areas of interest of the user with a fine-grained detail level. However, the detection of outliers is not a core topic in this work. [PERSON] et al. address the detection of mobility patterns (and Pols) based on mobile phone data [8]. [PERSON] et al. focus on situational awareness to define the nature of Pols (stopovers, specific interest in an area, or occasional stop) [9]. The focus of the authors is on situational awareness and not on semi-automated outlier detection. Another category of work in regard to PoI detection specifically focuses on outlier detection aspects. The way of approaching the problem of outliers varies from author to author, not only due to different applied methodologies but also due to the different use cases where outlier detection is required. In this context, [PERSON] et al. rely on the use of LOF for outlier detection in a traffic study [10], primarily using _principal component analysis (PCA)_ as an orthogonal linear transformation that transforms the data to a new coordinate system, so that the largest variance by any projection of the data lies along the first coordinate (the so-called first component), the second largest variance lies along the second coordinate, and so on. What the authors aim at is to analyse LOF as an effective and efficient method for outlier detection on data using PCA. Their study is relevant as they analyse the efficacy of LOF based on the variance of coordinates considering different dimensions. However, the efficacy in terms of different datasets and comparison with DBSCAN or any other algorithm is not addressed by the authors. [PERSON] et al. focus on the difference between global and local outlier detectors giving primacy to LOF [5]. Their study refers to data streams used in big data, analysing both parametric and non-parametric methods, and indicating a new methodology for the use of LOF in data streams. This work corroborates the applicability of LOF to data streams and provides a sound methodology for the use of LOF. However, the work does not compare LOF to other algorithms, such as DBSCAN. Several authors, such as [PERSON] and [PERSON] [11], [PERSON] et al. [12], [PERSON] et al. [13], and [PERSON] et al. [14], have published surveys and reviews about outlier detection methods, some of which provide an analytical comparison of the features of existing outlier detection methods. In this context, LOF and DBSCAN are among the most popular solutions for outlier detection. [PERSON] et al. propose _trajectory outlier detection algorithms using boundary (TODB)_[15] and as they state _\"The main contribution in this paper is outlier trajectories are identified using boundary method and their classification is done based on the constructed boundary.\"_ The authors rely on a convex hull algorithm for all trajectories, classifying them as being outlier trajectories or not. So, they have to use a boundary, in which the trajectories are inserted. For a continuous assessment, this is not feasible. In the path of finding outlying sub-trajectories [16], [PERSON] and [PERSON] propose the _density-based trajectory outlier detection (DBTOD)_, using Hausdorff distance computed in metric spaces, becoming a highly complex method with numerous steps. In our case, we are not interested in eliminating sub-trajectories, but points in sub-trajectories that are outliers. Similarly, in the case of [PERSON] and [PERSON], the goal of applying the _group trajectory outlier detection (GTOD)_ and the _closed DBSCAN k-nearest (CDRNN-GTOD)_[17] algorithmsis to detect groups of trajectories that can be considered as outliers in reference to most trajectories in a dataset. Recently, [PERSON] et al.