# Clustering Moving Object Trajectories: Integration in CROSS-CPP Analytic Toolbox
## 1 Introduction  
Nowadays, there is a growing number of devices connected to the internet, and an exponential rise is expected, along with the development of 5G communication standards; see [1]. The availability of data and the increase in computation capacity have boosted the proliferation of new applications. Geolocation services have attracted great interest due to the easy collection of geopositional data through all kind of devices. This type of data can proceed from vehicle services [[http://vis.cs.kent.edu/demo/list_of_trajectory_datasets.pdf](http://vis.cs.kent.edu/demo/list_of_trajectory_datasets.pdf)]([http://vis.cs.kent.edu/demo/list_of_trajectory_datasets.pdf](http://vis.cs.kent.edu/demo/list_of_trajectory_datasets.pdf)) (accessed date 17 April 2021), electrical micro-mobility (bikes and sociers) [[https://www.kaggle.com/pronto/cycle-share-dataset/code](https://www.kaggle.com/pronto/cycle-share-dataset/code)]([https://www.kaggle.com/pronto/cycle-share-dataset/code](https://www.kaggle.com/pronto/cycle-share-dataset/code)), 17 April 2021, people-collected data [[https://archive.ics.uci.edu/ml/datasets/GPS+Trajectories](https://archive.ics.uci.edu/ml/datasets/GPS+Trajectories)]([https://archive.ics.uci.edu/ml/datasets/GPS+Trajectories](https://archive.ics.uci.edu/ml/datasets/GPS+Trajectories)) (accessed date 17 April 2021), and public transport [[https://data-crtm.opendata.arcgis.com/](https://data-crtm.opendata.arcgis.com/)]([https://data-crtm.opendata.arcgis.com/](https://data-crtm.opendata.arcgis.com/)) (accessed date 17 April 2021). These new services have realized the relevance of analyzing moving patterns of their customers as a key characteristic to improve their value proposition and competitivity. Obtaining knowledge from moving agents' data has applications such as bus routes optimization [2], prediction of collective behavior [3, 4], customized trips, or fleet management in the mobility field [5, 6]. Additionally, the maturity of the mentioned services is impacting mobility in cities. The study of citizens' movements patterns may push forward more customized transportation [7]. This is a part of the more global concept of smart transportation and smart cities [8]. The surge in data from moving objects faces several challenges. One of them is how to extract useful insights of the data. Clustering of trajectories refers to grouping them based on a similarity measure [9]. This is a task that serves as a exploratory step in the discoveryof patterns in moving objects and enables one to synthesize the information by establishing a representative trajectory for each cluster. By doing so, it serves as a base from which to start a deeper analysis of the characteristics of the trajectories. A second challenge is related to quality assurance and trust. Among the diversity and randomness typical of the data of moving objects, it is vital to establish the data that correspond to real behaviors and those that are due to errors in the sensors or systems. This task is referred to as outlier/anomaly detection. It is domain-specific and is also used for detection of fraudulent activities, since these activities usually fall away from expected common behavior. Another challenge in the application of trajectory data analysis is the development of user-friendly tools that enable the development of services to extract knowledge from integrated data sources. An obstacle is the dispersion of libraries due to most of them being domain-specific. [PERSON] et al. [10] mentioned more than 20 different packages in R, mostly focused on animal trajectories. In their work, they propose a set of data structures, classes and methods to deal with generic trajectory data. Another option is the MovingObjects library [11] implemented in python, which is based in the Geopandas data structures. The mentioned libraries do not offer no-code options, and thus, they present a learning curve for non-tech users.