Driven by dynamic road networks: Enhanced 3D indoor pathfinding for emergency response

To address the critical failure of indoor navigation networks during fire or explosion incidents in complex urban public buildings, this study introduces a Building Information Modeling (BIM)-semantics-enhanced dynamic topology road network (DTRN) framework for three-dimensional emergency pathfinding. Departing from conventional static graphs, the proposed method dynamically reconfigures the constrained Delaunay triangulation (CDT) in response to the spatio-temporal evolution of hazards, enabling real-time, safety-oriented and scalable path optimization. Experiments conducted on a three-storey office building under simulated fire-spread scenarios demonstrate that DTRN achieves a balanced trade-off between accuracy and efficiency: the relative path deviation is confined to 5.2%, the average computational time is 0.125 s, and the path length converges to the ground-truth value after four iterations, with the absolute error decreasing from 5.4 to 1.7 m. Moreover, DTRN supports mission-driven pathfinding that enforces traversal of emergency-equipment nodes; although this increases path length by approximately 3.5%, it significantly improves rescue effectiveness. The proposed framework offers a new paradigm for semantically aware and adaptively responsive emergency pathfinding in complex indoor environments.