Machine Learning-based Urban Mobility Monitoring System

This article analyzes how streams of people move around a city. Wi-Fi sniffing techniques and camera systems are used for classifying vehicles, people, bicycles and scooters. The system is able to detect the presence of people by sniffing the mac address of the smartphone's Wi-Fi radio interfac...

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Bibliographic Details
Published in2022 IEEE 19th Annual Consumer Communications & Networking Conference (CCNC) pp. 747 - 748
Main Authors Bertolusso, Marco, Spanu, Michele, Popescu, Vlad, Fadda, Mauro, Giusto, Daniele
Format Conference Proceeding
LanguageEnglish
Published IEEE 08.01.2022
Subjects
Cameras
Convolutional neural networks
Economics
flow monitoring
IoT
Localization
Machine learning
Motorcycles
neural network
Recurrent neural networks
Smart cities
Sniffing
Social impact
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Summary:This article analyzes how streams of people move around a city. Wi-Fi sniffing techniques and camera systems are used for classifying vehicles, people, bicycles and scooters. The system is able to detect the presence of people by sniffing the mac address of the smartphone's Wi-Fi radio interface and with the use of cameras to monitor the presence of people, vehicles, and other means of transport, classifying them through the contribution of neural networks. The proposed solution has a high level of reliability overcoming the limits of a single technological system that offers imprecise and inaccurate monitoring.
ISSN:2331-9860
DOI:10.1109/CCNC49033.2022.9700694