Giovanni Zara
Traffic Load Estimation from Structural Health Monitoring Sensors using Supervised Learning.
Rel. Daniele Jahier Pagliari. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering), 2021
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Abstract
In the last decades there has been an evolution in the transport system, both for carriage of passengers and goods. In fact, the use of vehicles has become essential in the life of most of the population. This evolution has led to an increasing number of vehicles in circulation with the consequent increase in traffic congestion in travel times, road accidents and environmental problems related to pollution, especially near the large cities where urban traffic is often related to goods transport. Various Traffic Load Estimation (TLE) approaches have been implemented, which are essential for analyzing and managing vehicular traffic in the critical nodes of the road infrastructure.
The standard approaches of TLE are based on the installation of dedicated sensors such as cameras or infrared sensors or by examining data from sensors present in smartphones
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