Introduction: In the context of rapid urbanization and increasing traffic congestion, there is an urgent need for efficient solutions to improve urban transportation. This study aims to design and evaluate an urban rail transit route planning system that integrates a 3D Geographic Information System (GIS) with Building Information Modeling (BIM) to optimize rail line design and alleviate urban traffic pressure. Methods: The proposed system utilizes the K-means clustering algorithm as its core computational method to classify and process multi-source urban data. This data is then integrated with GIS technology to construct a detailed 3D virtual city model. The system performs spatial analysis to identify optimal rail transit corridors, considering factors such as population density, land use, and existing infrastructure. Functional tests were conducted to evaluate the system's stability and reliability. Results: Comparative analysis with traditional design methods demonstrated the system's superior performance. The system achieved a solution completeness of 97%–100% (compared to 85%–94% for traditional methods), scientificity of 95%–100% (compared to 70%–80%), and reduced the design cycle to 1–2 days (from 7–10 days). Design costs were also significantly lower. User satisfaction with the system reached 86%, though only 24% expressed willingness to adopt it, primarily due to institutional preferences for traditional, higher-budget approaches. Discussion: The results confirm the technological advancement and feasibility of the 3D GIS–BIM-integrated system for urban rail transit planning. However, the low adoption intention reflects the influence of non-technical factors, such as budget flexibility, institutional inertia, and risk aversion among decision-makers. This underscores the importance of addressing human and organizational factors in the implementation of innovative technological solutions. Conclusion: The urban rail transit line design system based on 3D GIS and BIM demonstrates significant advantages in completeness, scientificity, efficiency, and cost-effectiveness. Despite its technical superiority, broader adoption requires addressing practical implementation barriers and enhancing stakeholder confidence in data-driven planning tools.
Bian et al. (Fri,) studied this question.