Our lives are made of social interactions which can be recorded through personal gadgets as well as sensors capturing ubiquitous and social data. This type of data, such as spatio‐temporal data from the real‐time location of people, for example, can then be used for inferring interactions which can be translated into behavioural patterns. In this paper, we consider the automatic discovery of exceptional social behaviour from spatio‐temporal interaction data, focusing on two areas: exceptional subgroups and spatio‐temporal outliers – both in the form of descriptive patterns. For that, we propose a method for exceptional social behaviour discovery, combining subgroup discovery and network science methods for identifying behaviour that deviates from the norm. We also propose the use of two outlier detection metrics for identifying outliers, namely the Local Outlier Factor (LOF) and the Voronoi area. We applied the proposed method on synthetic data as well as two real datasets containing location data from children playing in the school playground. Our results indicate that this is a valid approach which is able to obtain meaningful knowledge from the data.@en

Essentially, our lives are made of social interactions. These can be recorded through personal gadgets as well as sensors adequately attached to people for research purposes. In particular, such sensors may record real time location of people. This location data can then be used to infer interactions, which may be translated into behavioural patterns. In this paper, we focus on the automatic discovery of exceptional social behaviour from spatio-temporal data. For that, we propose a method for Exceptional Behaviour Discovery (EBD). The proposed method combines Subgroup Discovery and Network Science techniques for finding social behaviour that deviates from the norm. In particular, it transforms movement and demographic data into attributed social interaction networks, and returns descriptive subgroups. We applied the proposed method on two real datasets containing location data from children playing in the school playground. Our results indicate that this is a valid approach which is able to obtain meaningful knowledge from the data.

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Nowadays and in the contemporary age, the reality of an all-autonomous traffic seems closer and closer. However, this transition period casts a lot of cards onto the table. Although technology can be replacing people at the driver seat, it has not as yet gained our full trust in what concerns communication in real time and safety. Humans interact on a daily basis in their various activities, and traffic is no exception. Most actions performed on the road rely on our perception of others’ awareness and potential reactions. For instance, pedestrians seek for an eye contact before crossing the road, drivers seek for a gesture before starting a manoeuvre, and so forth. Thus, the question remaining is what happens when someone is seeking such a communication interaction and the car has no driver, nor has it someone who even knows what the car is doing. Moreover, people seating in the car might be performing any other activities but driving. Other questions also arise such whether people will accept the idea of trusting self-driving vehicles, or whether will they feel safe when walking amongst such machines. In this paper we pursue a rather social perspective and will raise questions, covering the literature so as to understand what practitioners, researchers and the industry have been doing to overcome the lack of confidence in self-driving cars and improve their trustworthiness towards more efficient and smarter mobility, as well as to identify trends and approaches to answer these emerging questions.

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