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Archivio digitale delle tesi discusse presso l’Università di Pisa

Tesi etd-07072022-101558


Tipo di tesi
Tesi di laurea magistrale
Autore
ALIYEV, GURBAN
URN
etd-07072022-101558
Titolo
Analysis of Vehicular Emissions on The Road Network from Vehicles’ GPS Tracks
Dipartimento
INFORMATICA
Corso di studi
DATA SCIENCE AND BUSINESS INFORMATICS
Relatori
relatore Dott. Pappalardo, Luca
correlatore Dott. Nanni, Mirco
Parole chiave
  • analysis
  • vehicles
  • emissions
  • gps
Data inizio appello
22/07/2022
Consultabilità
Non consultabile
Data di rilascio
22/07/2092
Riassunto
In an increasingly urbanising world, vehicular emissions constitute a significant part of urban air pollution and create a health risk for a significant share of the world population. In order to analyse vehicular pollution, a number of studies try to quantify these emissions using different techniques, while many of them rely on traditional methods (remote sensing stations). However, traditional methods are not reliable as they do not capture full driving cycles of cars or focus only on few vehicles. In this thesis, GPS trajectories and a microscopic model are used to estimate and analyse carbon dioxide (CO2) emissions from thousands of vehicles in eleven cities of the domain area, Tuscany region in the central Italy. Our research discovers gross polluters, i.e. a small subset of vehicles contributing to the biggest share of emissions. We also identify heavily polluted roads which suffer the biggest portion of vehicles’ emissions. This study is enriched by a rich comparative analysis of the CO2 emissions distribution across the roads and cars of eleven urban areas in the domain area.
Our results show high heterogeneity in CO2 distribution per car and road in each city. However, CO2 distributions across roads have higher inequality in comparison to those across cars. We also rank cities by measurements of inequality in CO2 distribution, both across cars and roads. In the end, we discuss those cities which distinguish with highest or lowest rankings in both categories of CO2 distribution. The framework and results obtained in this thesis can be helpful for emission reduction policies, to simulate scenarios of reduction in emission by targeting gross polluters and grossly polluted roads.
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