The field of mobility represents one of the key aspects in the study of smart cities with increasing urbanization. In this article the phenomenon related to the flows of people moving within the city has been studied. In fact, the phenomena of road congestion and excessively crowded areas are increasing. The tools used in the monitoring phase concern Wi-Fi sniffing and video camera systems for the classification of vehicles, people, bicycles, and so on. Real-time monitoring allows citizens to know in advance the road conditions with a high level of reliability, overcoming the limits of a single technological system that offers imprecise monitoring. Results obtained from the systems based on a single technology have been merged into a machine learning solution using neural networks with a high degree of accuracy.

Vehicular and Pedestrian Traffic Monitoring System in Smart City Scenarios

Bertolusso M.;Giusto D. D.
2021

Abstract

The field of mobility represents one of the key aspects in the study of smart cities with increasing urbanization. In this article the phenomenon related to the flows of people moving within the city has been studied. In fact, the phenomena of road congestion and excessively crowded areas are increasing. The tools used in the monitoring phase concern Wi-Fi sniffing and video camera systems for the classification of vehicles, people, bicycles, and so on. Real-time monitoring allows citizens to know in advance the road conditions with a high level of reliability, overcoming the limits of a single technological system that offers imprecise monitoring. Results obtained from the systems based on a single technology have been merged into a machine learning solution using neural networks with a high degree of accuracy.
978-1-6654-4431-6
flow monitoring
IoT
Localization
Machine learning
neural network
Sniffing
Social impact
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11584/333467
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