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Public defence in Spatial planning and transportation engineering, M.Sc. Serio Agriesti

Lowering modeling barriers for activity-based large-scale urban modeling

Public defence from the School of Engineering, Department of Built Environment

Title of the thesis: Expanding the applicability of largescale transportation models for the assessment of disruptive mobility technologies

Doctoral student: M.Sc. Serio Agriesti

Opponent: Associate Professor Emmanouil Chaniotakis, University College London, UK

Custos: Associate Professor Claudio Roncoli, School of Engineering, Department of Built Environment

Lowering modeling barriers for activity-based large-scale urban modeling

As the world grows more complex and most aspects of daily life become fluid and more subject to change, transportation is no exception. Urbanization, sprawling, inequalities and climate change are only a few of the challenges currently facing transportation planners, institutions and public bodies. Moreover, historical patterns become less reliable as disruptions that once were counted in decades are now happening every few years.

To address all these challenges, it is of utmost importance to adapt our modeling approaches to be more flexible and to frame changes in attitudes, utilities, and goals in the urban population. The presented work focuses on developing and providing multiple solutions applicable to large-scale urban transportation models. Multiple subjects are tackled, namely: generating an anonymized dataset for a synthetic population with focus on workplaces; calibrating the behavioral parameters characterizing each mobility choice through machine learning; integrating an activity-based model with a supply one.

Finally, the developed solutions are exploited on a real case study (the city of Tallinn, Estonia) to assess the impacts of disruptive mobility services in large-scale urban scenarios.

Keywords: Traffic modeling; Activity-based modeling; Calibration; Land use; Behavioral modeling; Automated driving; Traffic simulation

Thesis available for public display 10 days prior to the defence at: https://aaltodoc.aalto.fi/doc_public/eonly/riiputus/

Contact information of doctoral student: 

Email [email protected]
Mobile +358413138738
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