Urban freight transport is a critical yet complex component of city logistics, shaped not only by transport networks but also by the morphological structure of urban areas. Traditional forecasting models often neglect this spatial heterogeneity, relying primarily on traffic counts or infrastructure topology. This thesis proposes and evaluates ST-SimNet, a Spatio-Temporal Simulation Network designed to enhance freight flow prediction by integrating static urban morphology descriptors with dynamic freight data in a graph neural network framework. Focusing on the city of Amsterdam, the study explores the extent to which detailed urban morphology, including building features, land use, and spatial layout, can improve short-term freight flow forecasts at the road network level. Results demonstrate that incorporating static features significantly reduces error variance, improves peak hour prediction, and enhances node-level stability compared to dynamic-only baselines. Furthermore, analysis reveals that nodes with richer morphology information benefit most, while areas with sparse or noisy static features experience challenges that highlight opportunities for future refinement. The findings offer practical insights for integrating machine learning into digital twin platforms for urban mobility, providing a data-driven, spatially aware layer for freight forecasting in operational city planning systems. Limitations and future directions, including adaptive fusion mechanisms and cross-city generalisation, are discussed. Overall, ST-SimNet advances the integration of urban morphology into spatio-temporal predictive models and demonstrates its practical relevance for modern freight planning in complex urban environments.

The building sector represents the highest share of operational energy consumption across all sectors, with a significant portion attributed to the inefficiency of the existing building stock. In this context, building retrofit plays a crucial role in enhancing energy efficiency and reducing environmental impact. However, conventional models for assessing retrofit scenarios are highly computationally expensive, thereby slowing down the retrofit process. This research addresses this challenge by developing an AI-based surrogate model using Multi-Task Learning (MTL). The proposed MTL model significantly reduces computational costs while simultaneously predicting energy consumption, costs, embodied carbon, and thermal comfort. Additionally, Multi-Objective Optimization (MOO) and Multi-Criteria Decision Making (MCDM) techniques are employed to select optimal retrofit solutions Results demonstrate that the MTL model accelerates the retrofit simulation process from 90 minutes to just 2 seconds, highlighting its potential to streamline and enhance retrofit decision-making processes.