An enhanced layout planning approach for metro-led underground space integrating spatial morphology and function

Metro-led underground spaces (MUS) have gained significant importance in addressing deteriorating urban issues in high-density built environments. However, existing planning techniques for MUS lack could enable the increasingly complex spatial morphology and function assignment, resulting in poor performance of MUS development in an unintegrated manner. To bridge the research gap, an enhanced layout planning approach for MUS (ELPA-MUS) was systematically formulated. ELPA-MUS incorporated a digital interpretation framework for MUS layout, enabling simultaneous analysis of spatial morphology and function. The model transformed the layout planning task into a multi-objective optimization (MOO) problem with nine objective functions. The non-dominant sorting genetic algorithm III (NSGA-III) was employed to find the Pareto front in high dimensions. To enhance the practicality of ELPA-MUS, an ensemble method was proposed, combining subjective expertise and objective computational analytics. The model was applied to a case study in Jinan, China to demonstrate its applicability and rationality. Overall, the ELPA-MUS model provided a modifiable paradigm for intelligent layout planning of complex underground spaces and expanded the data-driven planning toolkits towards a more sustainable MUS development.