Revealed preference data from WiFi traces for pedestrian activity scheduling

We use communication network infrastructure, in particular WiFi traces, to detect activity-episodes sequences in a pedestrian facility. Due to the poor quality of WiFi localization, a probabilistic method is proposed that infers activity-episodes locations and durations based on WiFi traces and calculates the likelihood of observing these traces in the pedestrian network, taking into account prior knowledge. The output of the method consists in generating lists of activity-episodes sequences with their likelihood. Results show that it is possible to predict the number of episodes, the activity-episode locations and durations, using activity locations on the map, WiFi measurements and capacity information. The output of our model is useful for modeling pedestrian activity scheduling and the impact of schedules on pedestrian travel demand.