Contemporary urban life and functioning have become increasingly dependent on mobility. Having become an inherent constituent of urban dynamics, the role of urban moblity in influencing urban processes and morphology has increased dramat- ically. However, the relationship between urban mobility and spatial socio-economic structure has still not been thoroughly understood. This work will attempt to take a complex network theoretical approach to studying this intricate relationship through • the spatio-temporal evolution of ad-hoc developed network centralities based on the Google PageRank, • multilayer network regression with statistical random graphs respecting net- work structures for explaining urban mobility flows from urban socio-economic attributes, • and Graph Neural Networks for predicting mobility flows to or from a specific location in the city. Making both practical and theoretical contributions to urban science by offering methods for describing, monitoring, explaining, and predicting urban dynamics, this work will thus be aimed at providing a network theoretical framework for developing tools to facilitate better decision-making in urban planning and policy making.
Urban Structure and Mobility as Spatio-temporal complex Networks / Yeghikyan, Gevorg. - (2020 Oct 16).
|Titolo:||Urban Structure and Mobility as Spatio-temporal complex Networks|
|Relatore/i esterno/i:||Nanni, Mirco|
|Data di pubblicazione:||2020-10-16|
|Corso PhD:||Data Science|
|Settore Scientifico Disciplinare:||Settore ICAR/21 - Urbanistica|
Settore ICAR/05 - Trasporti
Settore ING-INF/05 - Sistemi di Elaborazione delle Informazioni
Settore SPS/10 - Sociologia dell'Ambiente e del Territorio
Settore MAT/06 - Probabilita' e Statistica Matematica
|Parole chiave (inglese):||urban mobility; machine learning; complex networks; socio-economic attributes; spatio-temporal activity; neural networks|
|Editore:||Scuola Normale Superiore|
|Appare nelle tipologie:||9.1 Tesi di Dottorato|