This thesis develops models of activity and destination choices in pedestrian facilities from WiFi traces. We adapt the activity-based travel demand analysis of urban mobility to pedestrians and to digital footprints. We are interested in understanding the sequence of activities and destinations of a pedestrian using discrete choice models and localization data from communication antennas. Activity and destination choice models are needed by pedestrian facilities for decision aid when building new infrastructure, modifying existing infrastructures, or locating points of interest. Understanding demand for activities is particularly important when facing an increasing number of visitors or when developing new activities, such as shopping or catering. Data from existing sensors, such as WiFi access points, are cheap and cover entire facilities, but are imprecise and lack semantics to describe moving, stopping, destinations or activities carried out at destinations. Thus, understanding pedestrian behavior first requires to observe the actual behavior and detect stops at destinations, and second to model the behavior. Part I of this thesis focuses on activity-episode sequence detection. We develop a Bayesian approach to merge raw localization data with other data sources in order to take into account the imprecision and describe activity-episode sequences. This approach generates several activity-episode sequences for a single individual. Each activity-episode sequence is associated with a probability of being the true sequence. The prior represents the attractivity of the different points of interest surrounding the measurement and allows the use of a priori information from other sources of data (register data, point-of-sale data, counting sensors, etc.). Part II proposes models for activity and destination choices. The joint choice of activity type and activity timing is modeled by seeing a sequence of activity episodes as a path in an activity network. Time is considered as discrete. Unlike traditional models, our model is not tour-based, starting and ending at the home location, since the daily ``home''activity is meaningless in our context. The choice set contains all combinations of activity types and time intervals. The number of different paths is thus very large (increasing with time resolution and disaggregation of types of activities). Inspired by route choice models, we use a Metropolis-Hastings algorithm for the sampling of paths to generate the choice set. An importance sampling correction of the utility allows the estimation of unbiased model parameters without enumerating the full choice set. While the activity path model describes the choice of an activity type in time, the location where the activity is performed is modeled with a destination choice model conditional on the activity type. Our approach accounts for the panel nature of the data and deals with serial correlation between error terms. Using real WiFi data collected on the EPFL campus, we detect pedestrian activity-episode sequences, estimate an activity path choice model and develop a destination choice model for a specific activity type: eating. Knowing that the individual has decided to eat, which restaurant does she choose? This conditional destination choice model includes in its utility the cost of menus, available types of foods and drinks, distance from a previous activity episode, socioeconomic characteristics and habits.