An intelligent layout planning model for underground space surrounding metro stations based on NSGA-II

This study presents an intelligent layout planning model based on nondominant sorting genetic algorithm (NSGA) II algorithm to develop an optimal planning scheme for underground space surrounding metro stations (USSMS). The layout planning issue of USSMS is converted into a multi-objective optimization problem concerning land use conflicts, development benefits, and development scale. A decision-making method for planning scheme selection from the Pareto front determined by NSGA-II is developed using the technique for order preference by similarity to ideal solution (TOPSIS). An evaluation framework consisting of three spatial and functional indicators is proposed to determine the underground connectivity priority. A case study in Shanghai demonstrates the performance of the proposed model. Results show that the actual land use of USSMS is highly consistent with the optimal planning scheme, whereas the development intensity can be further improved. Moreover, a case study in Qingdao is also presented to illustrate the model application in the urban expansion scenario. The proposed model expands the data-driven planning toolkit to enhance the rational use of USSMS.