## 4 Experiment
### Dataset  
To properly depict the general behavior of foreign tourists with a large amount of motion data, we used a real-world dataset of anonymized mobile phone call detailed records (CDRs) of roamers in Italy. Data were provided by a major telecom operator and span the period between the beginning of May to the end of November 2013. Each CDR is related to a mobile phone activity (e.g., phone calls, SMS communication, data connection), enriching the event with a time stamp and the current position of the device, represented as the coverage area of the principal antenna; in addition, each user ID is associated with a mobile country code (MCC). We considered only short-term visitors, located in the country for a maximum of two weeks. CDRs have already been utilized in studies of human mobility to characterize people's behavior and predict human motion [42; 43; 44; 45].  
The mobile activity pattern of people is usually characterized by an erratic profile of sparse connection events separated by relatively long time gaps. To contrast the resulting trace fragmentation, we pre-processed traces into sequences unfolded in 1 h time steps. If more than one event occurred in the same hour, we selected the location associated with the majority of those events in order to represent the current position of the user. Considering the time step unit chosen, the wide territory under study, and our main interest for large-scale movements, we defined a minimum spatial resolution  
Figure 3: Flowchart of the whole classification process.  
of 2 km, aggregating antennas within that distance in a single reference point. We selected the most visited locations as reference points according to the minimum resolution, that is, the antennas with the highest number of connections within 2 km distance, projecting the other antennas to the closest reference point. Furthermore, we removed locations with just a few tens of occurrences. Since they were mostly randomly visited and did not reflect the overall behavior of foreign visitors in Italy, we treated them as a bias in the dataset. In general, the choice of parameters such as time and space resolution can be chosen differently, being highly dependent on the characteristics of the datasets.  
We finally obtained 1 h encoded sequences of almost six thousand unique locations over the Italian territory. Since we were interested in categorizing relatively short motion behaviors, which would also allow us to make proper and complete use of the dataset mostly made of short continuous traces, we constructed the fixed-length trajectory segments with a window length equal to 7 h (seven locations). We discarded sequences containing less than seven consecutive locations and also removed those segments that were completely stationary, where the user never moved for the entire 7 h. Our interest is to model large-scale mobility traces representing foreign tourists' motion behavior.  
In our classification task, we took into account the motion activity of the top 34 nationalities in terms of amount of data (the nationalities of the great majority of visitors), consisting of 96% of the original dataset. The classification problem was hence defined as associating a 7 h trajectory segment with one of the 34 nationality classes.  
The final dataset consists of 12.3 million segments belonging to 1.3 million users. This large number of users and mobility data assured the redundancy of motion patterns related to each nationality. Therefore, the classification task was not performed on the basis of regular schedules of single user behavior, but purely founded on the collective motion of millions of people. Table 1 summarizes the characteristics of the pre-processed dataset.