Machine Learning Approach for Mobility Context Classification using Radio Beacons
Résumé
The study of human mobility becomes more and
more crucial these days in transportation studies, urban planning,
crowd mobility behaviors, and even more. In this paper, we
propose a novel approach for studying human mobility by
building a light machine learning (ML) model using observation
of wireless networking information from WiFi and Bluetooth
low energy (BLE) that are today naturally present in everyday
devices such as mobile phones. Our goal is to build a mobility
classification system using communicating devices of any kind
with low processing complexity. However, we propose a new
approach for mobility classification using a real dataset of WiFi
and BLE beacons collected over one year for around 90 hours
in different scenarios and conditions. The first model (B-model)
aims to identify the status of a device if stationary or mobile.
Then a complementary model (M-model) is applied to determine
a more precise real-life situation of the device, which could be
a Home, Office, Bus, Train, etc. The results show that decision-
tree-based ensemble ML algorithms like LGBMClassifier and
XGBClassifier gave the best results, in terms of accuracy and
f1 score for both models with an accuracy of 99% and 94%
respectively, confirming the capability of classifying mobility
context from only WiFi and BLE data. We believe that such an
approach could be leveraged for studying human mobility and an
important step towards the large deployment of mobility-based
applications by leveraging everyday mobile phones.
Domaines
Informatique [cs]
Origine : Fichiers produits par l'(les) auteur(s)