Pedestrian Activity Pattern Mining in WiFi Network Connection Data
This article proposes a methodology to mine valuable information about pedestrian use of a facility based only on the WiFi network connection history data. Data are collected in Concordia University, Montreal, Canada. Working with a limited set of information, the authors tried to characterize the different pedestrian activity patterns in an analytic way without the prior knowledge of the different locations covered by the WiFi connection data. The goal of this research is to develop an analytical tool that is spatially transferable to different facilities. Moreover it is able to distinguish the main pedestrian activity patterns by looking at the WiFi network logs only. The methodology is based on the identification and generation of pertinent variables for data clustering and time space activity identification. A K means clustering algorithm is then used for the construction of a set of 6 activity patterns associated with activities in a campus context. The authors then discuss a few potential additional applications by analysing the inter access point behaviour that WiFi connection data offer, as well as the challenges caused by space time inaccuracies.