This thesis develops agent-based models of land cover change and mobility inside the canton of Geneva, and demonstrates that, given the establishment of dedicated infrastructure, agent-based models become self-adaptive digital twins of urban systems, which foster communication around its inner workings, state and future trajectories among a wide variety of audiences, and provide valuable guidance for policy-making.
Working towards the sustainability of our cities is one of the greatest challenges of this century, and its assessment requires the development of tools dedicated to holistically capturing the complexity of such urban systems. It is not sufficient to analyse separately each of their components (such as urban mobility, land cover change, etc.), because the interactions between these components are of capital importance to the evolution of the system as a whole.
Agent-based models are highly capable at integrating key characteristics of these complex systems, such as individual behaviours and population heterogeneity, and hold great value in the eyes of policy-makers in their ability to provide insights for decision-making processes. However, research in the agent-based modelling field has encountered a wide array of hurdles, ranging from its data hungriness, and the lack of standardised infrastructure for real time incorporation of real-world data, to drowning out the user in a wealth of parameters to calibrate and output data to visualise efficiently.
In this thesis, we develop three models of urban mobility, commuting patterns and land cover change due to urban expansion, for the canton of Geneva and the Greater Geneva region. We identify four major shortcomings of the current state of the art in the agent-based modelling field and show that our studies adequately address them.
This thesis highlights the strengths of agent-based models in capturing the complexity and emergent phenomena of urban systems. These models integrate the heterogeneity of populations, individual behaviours, and enable the exploration of various hypothetical scenarios, providing valuable insights for policy-making. The works conducted during this thesis make use of additional techniques, such as the DPSIR framework for supporting the establishment of evidence-based model evolution rules, and knowledge graphs, for enhancing communication about the research paradigm among project members and other audiences (such as the general public), as well as for streamlining the design of hypothetical scenarios to explore in the model.
