My master thesis proposes a general methodology to model pedestrian destination choice from WiFi localization in multimodal transport facilities (e.g., airports, railway stations). It is based on the output of [Danalet, A., B. Farooq and M. Bierlaire (2014) A Bayesian approach to detect pedestrian destination-sequences from WiFi signatures, Transportation Research Part C: Emerging Technologies, 44, 146–170.] method to generate candidates of activity-episode sequences from WiFi measurements, locations of activities on a map and prior information. Destination choice is nested to the activity choice. An individual first chooses an activity ([Danalet, A. and M. Bierlaire (2015) Importance sampling for activity path choice, paper presented at the 15th Swiss Transport Research Conference (STRC), Monte Verità, Ascona, Switzerland.]), and then selects the destination where to perform it. We propose an approach to model destination choice accounting for panel nature of data. We compare static, dynamic strict exogenous and dynamic model with two different agent effect corrections inspired by [Wooldridge, J. M. (2002) Simple solutions to the initial conditions problem in dynamic, nonlinear panel data models with unobserved heterogeneity, Journal of applied econometrics, 44, ISSN 1753-9196.] method. In a case study using WiFi traces on EPFL campus, we focus on one activity type: catering. The choice set contains 21 alternatives on campus (restaurants, self-services, cafeterias, ...). Our models reveal that the choice of a catering facility depends mostly on habits (e.g., where an individual ate the previous time), distance to walk from the previous activity-episode (calculated with a weighted shortest path algorithm) but less on destination specific determinants (e.g., price, capacity). The models are successfully validated using the same WiFi dataset and we forecast possible changes concerning catering destinations on the campus.