Nalin, Alessandro
(2025)
Smart cities and transport: evaluating and leveraging big data to address and promote sustainable and accessible urban mobility, [Dissertation thesis], Alma Mater Studiorum Università di Bologna.
Dottorato di ricerca in
Automotive engineering for intelligent mobility, 37 Ciclo.
Documenti full-text disponibili:
Abstract
The advent of Big Data has profoundly altered the landscape of data science, particularly in the context of smart cities. As organisations increasingly rely on data-driven insights, the challenges associated with Big Data, such as data privacy, ethical implications and the need for sophisticated analytical tools, become more pronounced. This doctoral thesis examines the complex and far-reaching effects of Big Data on the transportation sector, a pivotal element of urban infrastructure. The objective of this research is to conduct a comprehensive evaluation of the current state of Big Data, with a particular emphasis on the necessity for innovative data management and analysis approaches. The thesis is structured in such a way as to first present a comprehensive discussion of the concepts of smart cities and big data, with particular emphasis on the interdependencies between the two and the inherent drawbacks of each.
The following chapters examine the applications of Big Data in transportation, demonstrating how integrated data sources can address existing research gaps and enhance urban mobility. A substantial emphasis is placed on the issues of accessibility and equity in public transportation, with a view to exploring how Big Data can inform the provision of equitable services and improve the experiences of users. By critically examining analytical validation processes and the integration of diverse data sources, this research contributes to the ongoing discourse on the role of Big Data in fostering sustainable urban development. The findings demonstrate the potential of Big Data to revolutionise transportation systems, optimise resource allocation, and enhance the quality of life for urban residents. Ultimately, this thesis aspires to provide actionable insights and frameworks that facilitate the effective utilisation of Big Data in creating responsive, resilient, and equitable transportation solutions within Smart Cities.
Abstract
The advent of Big Data has profoundly altered the landscape of data science, particularly in the context of smart cities. As organisations increasingly rely on data-driven insights, the challenges associated with Big Data, such as data privacy, ethical implications and the need for sophisticated analytical tools, become more pronounced. This doctoral thesis examines the complex and far-reaching effects of Big Data on the transportation sector, a pivotal element of urban infrastructure. The objective of this research is to conduct a comprehensive evaluation of the current state of Big Data, with a particular emphasis on the necessity for innovative data management and analysis approaches. The thesis is structured in such a way as to first present a comprehensive discussion of the concepts of smart cities and big data, with particular emphasis on the interdependencies between the two and the inherent drawbacks of each.
The following chapters examine the applications of Big Data in transportation, demonstrating how integrated data sources can address existing research gaps and enhance urban mobility. A substantial emphasis is placed on the issues of accessibility and equity in public transportation, with a view to exploring how Big Data can inform the provision of equitable services and improve the experiences of users. By critically examining analytical validation processes and the integration of diverse data sources, this research contributes to the ongoing discourse on the role of Big Data in fostering sustainable urban development. The findings demonstrate the potential of Big Data to revolutionise transportation systems, optimise resource allocation, and enhance the quality of life for urban residents. Ultimately, this thesis aspires to provide actionable insights and frameworks that facilitate the effective utilisation of Big Data in creating responsive, resilient, and equitable transportation solutions within Smart Cities.
Tipologia del documento
Tesi di dottorato
Autore
Nalin, Alessandro
Supervisore
Co-supervisore
Dottorato di ricerca
Ciclo
37
Coordinatore
Settore disciplinare
Settore concorsuale
Parole chiave
Accessibility; Big Data; Cellphone data; GIS; Public Transportation; Smart City
Data di discussione
17 Marzo 2025
URI
Altri metadati
Tipologia del documento
Tesi di dottorato
Autore
Nalin, Alessandro
Supervisore
Co-supervisore
Dottorato di ricerca
Ciclo
37
Coordinatore
Settore disciplinare
Settore concorsuale
Parole chiave
Accessibility; Big Data; Cellphone data; GIS; Public Transportation; Smart City
Data di discussione
17 Marzo 2025
URI
Gestione del documento: