## 1 Introduction  
The study of human mobility has received significant attention in recent years due to the growing collection and availability of motion data, making it relatively easy to track large numbers of people and create massive trajectory data sets from GPS traces, road sensors, mobile phone traces, social media geo-spatial check-ins, and many more recording tools [1]. This massive amount of mobility data has allowed a better understanding and modeling of travel behaviors and motion patterns [2], leading to significant analysis covering various applications, such as personalized recommendation [3] and preference-based route planning [4]. Human trajectory classification is a well-studied problem in literature, especially for detecting activity patterns and transportation modalities: based on spatial-temporal values and activities, traces are classified into some predefined categories, e.g., walking or driving [5]. However, the use of motion activity for inferring information about individual users is a very recent trend that has room for improvement and expansion. Our work is inserted in this new wave of user profiling, in particular aiming to identify the nationality of individual foreign visitors from their mobility traces. In the big picture of mining user motion behaviors [6], linking anonymous users to their nationality on the basis of only their generated trajectories can be very useful in many scenarios, especially for touristic purposes [7]. In the context of \"smart\" tourism, which provides personalized services for improving the experience of travelers and the management and marketing of companies in the sector, the connection between motion traces and user nationalities is indeed helpful in serving tourists more efficiently, making better recommendations, personalized and precise suggestions, and targeted advertisement. Moreover, it may turn out to be a relevant factor for trajectory prediction, particularly if distinctive paths are explored by different nationalities. The problem of inferring nationalities from motion activity is a challenging task. The hypothesis is that foreign visitors of different nationalities may not only visit different locations over the territory, but also move in a different way between the same locations. The number of classes (nationalities) considered can be on the order of a few tens of units, so typically larger than the number of motion patterns used in the traditional trajectory classification studies. In addition, the motion activity of foreign tourists is naturally characterized by short traces and non-repetitive behaviors, and large-scale mobility can lead to analyzing a very wide territory, hence encountering problems such as a sparsity of trajectory data and high number of locations, entailing the curse of dimensionality. In this paper, we propose a new method for revealing short-term foreign visitors' nationality based uniquely on their generated motion traces over the territory. The problem is defined as a multinomial classification using trajectory as an input and the corresponding nationality class as an output. The method consists of three steps: trajectory pre-processing, location embeddings generation, long short-term memory (LSTM)-based model building. More specifically, raw traces are transformed into discrete (in time and space) location sequences and, inspired by word embedding approaches in natural language processing (NLP), fed to a Word2 vec-based model for learning the embedding vector of each location according to the motion behavior of people, whereby behaviorally-related locations share similar representations in mathematical terms. Trajectories are therefore defined as sequences of embeddings, which are used as an input to an LSTM neural network for learning the underlying motion patterns of human mobility. The collective motion behavior of people over the territory is used to train the model and associate individual traces to a specific predicted nationality.