Collaborative location collecting systems (CLCS) are collaborative systems where users collects location-based data. When these systems are gamified and aim to adapt the game elements to each user, it may require a user traveling behavior profile. This work presents two approaches of traveling user behavior profiling: a raw series built up with categorical data that describes the user’s activity in a period, and a timed series that is an enhanced version of the first that includes a representation of the non-activity time frames. The profiling of user traveling behavior can be used in adaptive gamification strategies. The approach is evaluated over a behavioral atoms dataset based on a year of Foursquare check-ins. The results showed that both approaches reflex different aspects of traveling user behavior, and also both could be used in a complementary manner.