Optimizing Urban Layout: A System Dynamics, Artificial Intelligence, and 3D Urban Information Model Approach for Active Management of City Complexity

Abstract

This study focuses on the integration of System Dynamics (SD), Artificial Intelligence (AI) technology, and 3D Urban Information Modeling (CityGML) in the field of urban planning. It aims to optimize urban layout to cope with rapid urban growth, emphasizing resource allocation through a combination of greedy strategies and AI methods. The core contribution of this paper is to provide spatial allocation of feedback from simulation techniques i.e. SD using the application of genetic algorithms (GA) based on a greedy strategy. The methodology was implemented using a fictitious case study in Berlin, where new students’ dorms are required according to the growth in the number of students. The SD tool is used to simulate the growth over 5 years and determine the number of new dorms required. A genetic algorithm is used to optimize the planning objectives of allocating 16 new dorms in 186 possible locations. The algorithm effectively handles the feedback from SD and applies complex multi-objective optimization problems, addressing the challenge of accommodating a growing student population. It proposes strategic dormitory allocations that balance factors such as cost efficiency, campus accessibility, and proximity to public transportation systems. We use CityGML data to simulate and predict future urban transformation, providing dynamic and realistic representations for urban planning decisions and updating the original city models.