PhD Oral Exam - Ahad Farnoodahmadi, Individualized Program in Engineering

When studying for a doctoral degree (PhD), candidates submit a thesis that provides a critical review of the current state of knowledge of the thesis subject as well as the student’s own contributions to the subject. The distinguishing criterion of doctoral graduate research is a significant and original contribution to knowledge.

Once accepted, the candidate presents the thesis orally. This oral exam is open to the public.

Abstract

Urban regions are increasingly confronted with complex and interconnected challenges related to mobility, land use, infrastructure provision, and carbon emissions. While data availability and computational tools have expanded rapidly, urban planning and transportation decision-making remain constrained by fragmented analytical approaches that often address accessibility, mobility behaviour, and environmental impacts in isolation. As a result, policy interventions frequently fail to capture the dynamic interactions and feedback mechanisms that shape long-term urban sustainability outcomes.

This thesis addresses these limitations by proposing an integrated, AI-deriven decision-support framework that combines Geographic Information Systems (GIS), System Dynamics Modelling (SDM), and data-driven methods to evaluate sustainable urban development strategies at the neighbourhood and city scale. The overarching aim of the research is to bridge static spatial analysis with dynamic behavioural and infrastructural processes in order to support scenario-based planning and carbon emissions mitigation in complex urban systems.

The research is structured around three complementary contributions. First, a System Dynamics–GIS framework is developed to assess the long-term impacts of transit-oriented development and parking policies on travel behaviour and transportation-related emissions, highlighting the role of feedback loops between urban form, mobility demand, and car dependency. Second, the concept of proximity planning is operationalized through high-resolution accessibility analysis, with a particular focus on walkable access to electric vehicle charging infrastructure for elderly populations, demonstrating how equity-oriented spatial indicators can inform infrastructure planning. Third, a data-driven modelling approach is introduced to estimate active mobility demand using bike-sharing data, where machine learning models are integrated into a stock-and-flow structure to dynamically link built environment characteristics, mobility adoption, and emissions outcomes.

By applying the proposed framework to multiple case studies on the Island of Montreal, Canada, the thesis demonstrates how integrated modelling can reveal trade-offs and synergies across urban density, accessibility, mobility behaviour, and carbon emissions that are not observable through single-method approaches. The results highlight the value of combining spatial analysis, behavioural modelling, and dynamic simulation to support evidence-based urban policy design. This research contributes to the advancement of interdisciplinary urban analytics by offering a scalable and transparent framework for evaluating sustainable mobility and land-use strategies under conditions of uncertainty and complexity.