Starting from the issue of the ecological transition in the field of mobility and the challenge of widespread adoption of environmentally friendly transportation options, the thesis aims at developing optimal schemes for the design of fair mobility policies. The work here presented firstly analyzes the importance of proposing personalized incentives to people to promote the use of green technologies among them by accounting for individual socio- economic characterization. After that, it is discussed the relevance to account for social justice, for a fair and inclusive transition to green mobility solutions. The thesis proposes a data-driven network-based human-centered approach to model the adoption of Car-sharing services, referencing a European Commission survey on transport and mobility. The data of this survey is used to both characterize in a simple way each person’s propensity towards shared mobility and build a multi-agent network used in this work to study the diffusion of sharing mobility services. Moreover, we exploit a Linear Quadratic Regulator to obtain control schemes for the design of optimal policies and try to innovatively introduce the concept of fairness for social justice directly within the design process, to accomplish not only an ecological, but also a just transition.
Partendo dal tema della transizione ecologica nel campo della mobilità e dalla sfida della diffusione dell’adozione di opzioni di trasporto ecologiche, la tesi mira a sviluppare schemi ottimali per la progettazione di politiche di mobilità eque. Dopo aver analizzato l’importanza di proporre incentivi alle persone per promuovere l’uso di tecnologie verdi, viene discussa la necessità di tenere conto della giustizia sociale, per una transizione equa e inclusiva verso soluzioni di mobilità sostenibile. Facendo riferimento a uno studio portato avanti della Commissione Europea sui trasporti e la mobilità, la tesi, dunque, propone un approccio data-driven basato sull’utilizzo di reti e human-centered per riuscire a modellare l’adozione dei servizi di Car-sharing. I dati di questa indagine sono utilizzati sia per caratterizzare in modo semplice ed efficace la propensione di ogni persona verso la mobilità condivisa e sia per costruire la rete multiagente che verrà usata in questo lavoro. Inoltre, facendo riferimento a un regolatore lineare quadratico, la tesi propone schemi di controllo per la progettazione di politiche ottimali e cerca di introdurre in modo innovativo il concetto di equità per la giustizia sociale nel processo di progettazione, per realizzare una transizione che non sia solo ecologica, ma giusta.
Control-enabled policy design for fostering sustainable and just mobility habits
Guagliardi, Oriana
2022/2023
Abstract
Starting from the issue of the ecological transition in the field of mobility and the challenge of widespread adoption of environmentally friendly transportation options, the thesis aims at developing optimal schemes for the design of fair mobility policies. The work here presented firstly analyzes the importance of proposing personalized incentives to people to promote the use of green technologies among them by accounting for individual socio- economic characterization. After that, it is discussed the relevance to account for social justice, for a fair and inclusive transition to green mobility solutions. The thesis proposes a data-driven network-based human-centered approach to model the adoption of Car-sharing services, referencing a European Commission survey on transport and mobility. The data of this survey is used to both characterize in a simple way each person’s propensity towards shared mobility and build a multi-agent network used in this work to study the diffusion of sharing mobility services. Moreover, we exploit a Linear Quadratic Regulator to obtain control schemes for the design of optimal policies and try to innovatively introduce the concept of fairness for social justice directly within the design process, to accomplish not only an ecological, but also a just transition.File | Dimensione | Formato | |
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2023_05_Guagliardi_Tesi_01.pdf
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Descrizione: Testo tesi
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2023_05_Guagliardi_Executive Summary_02.pdf
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Descrizione: Executive summary
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https://hdl.handle.net/10589/208835