K-means clustering-enhanced 3D GIS–BIM integration for urban rail transit planning: evaluating design performance, cost efficiency, and stakeholder adoption

Rapid urbanization has intensified traffic congestion and strained transit infrastructure. This study presents an integrated system combining three-dimensional Geographic Information Systems (3D GIS), Building Information Modelling (BIM), and K-means clustering to enhance urban rail transit planning. The model enables data-driven route optimization and spatial analysis, achieving over 95% accuracy in clustering and reducing design time and cost by up to 80% and 60%, respectively. A case study validates the system's technical robustness and planning efficiency. However, stakeholder feedback highlights an adoption gap: while 86% expressed satisfaction with the system's outputs, only 24% indicated readiness to adopt it in practice. This underscores persistent sociotechnical barriers such as digital familiarity, institutional inertia, and trust in automation. The findings affirm the system's potential for scalable, cost-effective transit design, while emphasizing the need for user-centred strategies to support implementation.