Expanding the applicability of large-scale transportation models for the assessment of disruptive mobility technologies

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 approaches to be more flexible and to frame changes in attitudes, utilities, and goals in the urban population.The work presented in this thesis focuses on developing and providing multiple solutions with a specific focus on large-scale urban models. The main objective is to draw a road map of the major issues hindering modeling solutions able to tackle the challenges described above. Once that is accomplished, different methods are designed, developed, and tested on a real case study (Tallinn, the capital city of Estonia). First, we approach the problem of defining a synthetic population detailed enough to carry out large-scale behavioral studies, without infringing privacy constraints. We then use the resulting dataset to build an activity-based behavioral model for the whole city. We harness machine learning techniques to automate the calibration of the hundreds of behavioral parameters involved, a quantity not yet achieved in the state of the art. We then focus on integrating the behavioral model with state-of-the-art traffic assignment solutions, trying to forge a blueprint for any modeler wanting to expand an existing model (a problem quite common, as many urban traffic assignment models have been developed through the years and are not easily replaceable). An iterative approach is developed and tested, first to frame a baseline situation and then to forecast the impact of disruptive mobility services on both the demand and the supply. Finally, the large-scale urban architecture built by integrating behavioral and traffic assignment models is exploited to test, for the first time with the presented degree of detail, the impacts of both an optimization algorithm and a fairness pricing scheme on an (automated) on-demand system.

Department dc:contributor.department

Rakennetun ympäristön laitos

Grantor dc:publisher

Aalto University

Year dc:date.issued

2024

Author dc:creator

• Agriesti, Serio Angelo Maria

Advisors dc:contributor.advisor

• Nahmias-Biran, Bat-hen, Senior Lecturer, Tel-Aviv University, Israel
• Roncoli, Claudio, Prof., Aalto University, Department of Built Environment, Finland

Contributors dc:contributor

• Aalto-yliopisto
• Aalto University

Language dc:language.iso

en

Repository record dc:identifier.uri

https://aaltodoc.aalto.fi/handle/123456789/130836

OAI record oai:identifier

https://aaltodoc.aalto.fi/server/oai/request?verb=GetRecord&metadataPrefix=dim&identifier=oai%3Aaaltodoc.aalto.fi%3A123456789%2F130836