Underground Space Planning Optimization Under the TOD Model Using NSGA-II: A Case Study of Qingdaobei Railway Station and Its Surroundings

Weiyan Kong (), Wenhan Feng and Yimeng Liu
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Weiyan Kong: School of Civil Engineering, Qingdao University of Technology, Qingdao 266525, China
Wenhan Feng: Department of Geography, Ludwig-Maximilians-Universität München (LMU), 80539 Munich, Germany
Yimeng Liu: DARe Hub, School of Engineering, Newcastle University, Newcastle NE1 7RU, UK

Sustainability, 2025, vol. 17, issue 21, 1-31

Abstract: Urbanization and the growing scarcity of surface land resources have highlighted the strategic importance of underground space as a critical component of sustainable urban infrastructure. This study presents a multi-objective optimization framework for underground infrastructure planning around transit hubs, aligning with the principles of Transit-Oriented Development (TOD). By integrating an agent-based model (ABM) with the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and incorporating the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), the framework forms a unified evaluation and optimization tool that accounts for user behavior while addressing competing objectives, including minimizing evacuation time and functional conflicts, maximizing functional efficiency, and reducing layout deviations. Using Qingdaobei Railway Station in China as a case study, the method yields notable improvements: a 15% reduction in evacuation time, a 16% increase in development benefits, and a more balanced spatial configuration. Beyond technical gains, the study also discusses station planning and governance under the TOD policy context, highlighting how integrated layouts can alleviate congestion, strengthen functional synergy, and support sustainable urban development.

Keywords: underground space planning; TOD; NSGA-II; TOPSIS; multi-objective optimization; urban mobility (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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