WiFi Fingerprint Clustering for Urban Mobility Analysis

TL;DR

This paper introduces an unsupervised WiFi fingerprint clustering method to analyze urban mobility patterns, especially indoors, by identifying points of interest and micro-mobility using WiFi data combined with GPS.

Contribution

It presents a novel system architecture for WiFi-based POI detection and mobility analysis, addressing indoor and urban environments where GPS is unreliable.

Findings

Successful identification of indoor POI, neighborhood activity, and micro-mobility.

WiFi and GPS fusion enhances mobility insights beyond GPS alone.

Unsupervised learning effectively clusters WiFi fingerprints for urban mobility analysis.

Abstract

In this paper, we present an unsupervised learning approach to identify the user points of interest (POI) by exploiting WiFi measurements from smartphone application data. Due to the lack of GPS positioning accuracy in indoor, sheltered, and high rise building environments, we rely on widely available WiFi access points (AP) in contemporary urban areas to accurately identify POI and mobility patterns, by comparing the similarity in the WiFi measurements. We propose a system architecture to scan the surrounding WiFi AP, and perform unsupervised learning to demonstrate that it is possible to identify three major insights, namely the indoor POI within a building, neighbourhood activity, and micro-mobility of the users. Our results show that it is possible to identify the aforementioned insights, with the fusion of WiFi and GPS, which are not possible to identify by only using GPS.