Estimating Pedestrian Destinations Using Traces from WiFi Infrastructures
Gathering data about pedestrian origin, destination and route is difficult, particularly indoor and on a large scale. These data are important for route choice modeling, description of congestion, and flow estimation. Most data collection techniques are device-centric. In this paper, we focus on the communication network infrastructure and propose to use WiFi traces to generate pedestrian destinations. Due to the poor quality of WiFi localization, a probabilistic method is proposed that infers visited destinations 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 several candidate lists of destinations, and assigning the probability of each list being the true one. Results show that it is possible to predict the number of destinations, the time spent at it and the localization of it, discriminating intermediary signals from signals generated at destination.
KeywordsWiFi traces Pedestrian destinations Pedestrian network Probabilistic measurement model
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