Modeling the self-organizing dynamics of pedestrian flow in subway stations under the constraints of guide barriers

With the rapid development of dense urban rail transit networks, subway stations are experiencing increasingly frequent large-scale passenger flow events, which has led to the adoption of pedestrian guide barriers as a standard practice for ensuring orderly movement in crowd management. However, the underlying mechanisms through which these barriers influence pedestrian flow dynamics remain insufficiently understood. Traditional social force models fail to capture pedestrians' adaptive path selection under such constraints, limiting their ability to simulate pedestrian trajectory distribution in varying crowd densities. To address this, we propose a Dynamic-Adaptive Social Force Model (DSFM) that integrates time-urgent behaviors, collision avoidance, and an adaptive path selection strategy based on spatial occupancy. Two typical scenarios, a serpentine barrier and a linear platform barrier, were simulated using real-world station data. The DSFM demonstrates significant superiority over the traditional Social Force Model (SFM). In parametric analyses of the serpentine barrier, the DSFM reduced average travel times by 11.7–36.4 % and traffic conflicts by 21.8–57.9 %. Furthermore, it accelerated spatial utilization, with the cumulative growth rate of channel occupancy peaking at 85.95 % relative to the SFM. In the high-density linear barrier scenario, the DSFM improved passage efficiency by 12.92 %. These results, validated across various crowd densities and geometries, confirm the DSFM's robustness and accuracy. This research provides a novel, validated simulation tool for optimizing guide barriers, enhancing both passenger flow management and station service quality.