Abstract
The increasing complexity of urban environments necessitates advanced tools for planning and design. This paper investigates the synergistic integration of Machine Learning (ML) techniques with traditional Parametric Urban Planning (PUP) models. While parametric modeling offers powerful capabilities for generative design and complex spatial analysis, its integration with ML can unlock new frontiers in predictive analytics, pattern recognition, and adaptive decision-making. We explore how ML algorithms can augment PUP models by learning from vast urban datasets to predict outcomes, identify optimal design parameters, and adapt to dynamic urban conditions. The methodology involves a review of existing literature, the development of a conceptual framework for ML-PUP integration, and an analysis of potential applications. Our findings highlight the transformative potential of this integration across various urban planning domains, including land-use optimization, transportation forecasting, and environmental impact assessment. Specifically, ML can enhance the predictive accuracy of parametric models, enabling more robust scenario planning and risk assessment. The study presents a conceptual model and discusses the challenges and opportunities associated with implementing such integrated systems, emphasizing the need for interdisciplinary collaboration and robust data infrastructure. This research contributes to the evolving discourse on intelligent urban planning by providing a roadmap for leveraging the combined strengths of ML and parametric design.