E – 3111 Machine Learning for Parking Layout Optimization

The optimization of parking layout design represents a critical challenge in urban planning and architectural practice. This comprehensive course introduces civil engineers, architects, and transportation planners to machine learning applications for parking facility optimization. Traditional parking design relies on manual calculations and iterative processes that are time-consuming and often fail to explore the full solution space. Machine learning offers transformative capabilities to revolutionize parking layout optimization through data-driven approaches that balance space efficiency, circulation flow, accessibility compliance, and user experience.

With urban land values reaching unprecedented levels and regulatory requirements continuously evolving, the economic and practical imperatives for optimal parking design have never been greater. This course examines how machine learning algorithms can process vast datasets encompassing parking utilization patterns, traffic simulations, and geometric constraints to generate optimized layouts achieving performance levels impractical through manual design. Participants will gain practical understanding of ML fundamentals, data collection strategies, algorithm selection, implementation approaches, and validation methods applicable to real-world parking projects.