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.
-
Supplemental Notes:
- This paper was sponsored by TRB committee AHB45 Standing Committee on Traffic Flow Theory and Characteristics.
-
Corporate Authors:
500 Fifth Street, NW
Washington, DC United States 20001 -
Authors:
- Poucin, Guilhem
- Farooq, Bilal
- Patterson, Zachary
-
Conference:
- Transportation Research Board 95th Annual Meeting
- Location: Washington DC, United States
- Date: 2016-1-10 to 2016-1-14
- Date: 2016
Language
- English
Media Info
- Media Type: Digital/other
- Features: Figures; References; Tables;
- Pagination: 20p
- Monograph Title: TRB 95th Annual Meeting Compendium of Papers
Subject/Index Terms
- TRT Terms: Activity choices; Campuses; Cluster analysis; Data collection; Facilities; Pedestrian movement; Pedestrians; Travel behavior; Wireless communication systems
- Geographic Terms: Montreal (Canada)
- Subject Areas: Data and Information Technology; Pedestrians and Bicyclists; Planning and Forecasting;
Filing Info
- Accession Number: 01594105
- Record Type: Publication
- Report/Paper Numbers: 16-5846
- Files: TRIS, TRB, ATRI
- Created Date: Mar 21 2016 4:40PM