Footfall Count Estimation Techniques Using Mobile Data

Accurate estimations of footfall count have several important uses, examples include monitoring crowd movement, allocation of emergency services, retail planning, transport planning, and so on. To estimate footfall count, data logs from telco's Location Based Services (LBS) system can be used....

Full description

Saved in:
Bibliographic Details
Main Authors: Yibin Ng, Yingchi Pei, Yunye Jin
Format: Conference Proceeding
Language:English
Subjects:
cellular networks
Estimation
footfall count estimation
Geospatial analysis
geospatial data
Global Positioning System
Mobile communication
Mobile computing
mobile data
Mobile handsets
Poles and towers
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Accurate estimations of footfall count have several important uses, examples include monitoring crowd movement, allocation of emergency services, retail planning, transport planning, and so on. To estimate footfall count, data logs from telco's Location Based Services (LBS) system can be used. LBS records indicate the cellular towers that mobile phone users are connected to at a given date and time. However, such data only indicates the geospatial coordinates of the cellular tower that a user is connected to and does not accurately reflect the actual geolocation of the user. In this work, we first build a dataset comprising of the observations (cellular tower locations) and the actual ground truth (corresponding GPS locations). Next, we propose several schemes to improve the accuracy of footfall count estimation given the cellular tower coordinates that mobile phone users are connected to. This includes estimating footfall count based on different grids, as well as redistributing observed locations to an area surrounding the cellular tower based on a random distribution. Through our experiments, we found that the most effective scheme is to firstly compute the posterior probability that a user is in an area, conditioned on her being connected to a particular cellular tower. After the posterior probability distributions are obtained, we redistribute these cellular tower points based on this distribution table. Our experimental results show that redistribution of the observed cellular tower points based on posterior probabilities is able to improve the footfall count estimation accuracy by up to 83.7%.
ISSN:2375-0324
DOI:10.1109/MDM.2017.49