Evaluating underground space of rail transit hub evacuation under fire scenarios: Virtual reality meets agent-based simulation.

The rapid growth of urban rail transit has improved transportation efficiency but presents significant safety challenges, particularly in high-density transit hubs where aging infrastructure, overcrowding, and extreme events converge. Fires in such hubs create critical evacuation bottlenecks due to enclosed spaces and complex rescue environments. Existing methods of evacuation research lack the realism and adaptability required to address these dynamic scenarios, particularly for optimizing Selective Door Opening (SDO) strategies. To address these gaps, we developed a high-fidelity virtual environment replicating emergency scenarios in underground transit tunnels. A VR experiment was conducted to collect participants' evacuation trajectories and eye-tracking data under different SDO strategies. Behavioral mechanisms, movement dynamics, gaze patterns and evacuation speeds in different areas were analyzed, with bivariate Gaussian distributions fitted to describe visual perception regions of pedestrians. Features derived from the foregoing VR experiment informed agent-based simulations, enabling the quantitative evaluation of SDO performance across diverse emergency scenarios. The results of the VR experiment indicate that during evacuation, pedestrians exhibit significant differences in evacuation speed and gaze patterns across various spatial regions. In addition, pedestrians show notable variation in perception scale for different types of AOIs. Furthermore, the evaluation of SDO using agent-based simulation reveals that the effectiveness of SDO is influenced by the scenario issues, with passenger volume and the weighting of safety factors dynamically impacting the selection of the optimal SDO.