Pedestrian behavior during evacuation has been formulated using various arbitrary microscopic methods to investigate the performance of crowd dynamics while their custom rules result in low visual realism in simulation due to the complexity of intrinsic decision logic of human. Statistical analysis is an effective way to reveal the motion pattern and path planning behavior of pedestrians whose main idea is to approach the trajectory and social attributes data of pedestrians extracted from evacuation drills as much as possible. In this study, we present a data-driven based microscopic pedestrian-simulation model with continuous-space representation to explore the potential of integrating empirical analysis into crowd simulation to enhance the authenticity of decision making. This method extracts the pedestrian’s decision mode and smoothly applies it in the crowd dynamics model. Instead of navigating agents by arbitrary regulations, the desired direction of pedestrians during the motion is arranged by machine learning (ML) algorithms. The path decision module trained with actual pedestrian data improves the compatibility of the model in the application of various spatial scenarios and no longer suffers from tedious parameter fine-tuning work. To completely describe the information precepted by pedestrians, a polygon segmentation module is developed to divide the visual field of pedestrians and identify the mutual visibility among them. This module filters out the information that can be perceived by pedestrians in real situations, thereby bridges the gap between statistical analysis and numerical simulation methods. We compare different ML approaches for route-choice behavior prediction and discuss the relative importance of its influencing variables under different scenarios. Inferring the perception of social interactions from disaggregate choice data, the scope of effectiveness of conformity behavior and crowd-aversion are also discussed. The simulation results are compared with experimental data, illustrating the model’s capability to accurately reproduce the observed flow motion in various scenarios with moderate modification in physical environment initialization.

疏散期间的行人行为已经使用各种任意微观方法来研究人群动力学的性能，而由于人类内在决策逻辑的复杂性，它们的自定义规则导致模拟中的视觉真实感较低。统计分析是揭示行人运动模式和路径规划行为的有效方法，其主要思想是尽可能接近从疏散演习中提取的行人轨迹和社会属性数据。在这项研究中，我们提出了一种基于数据驱动的具有连续空间表示的微观行人模拟模型，以探索将经验分析整合到人群模拟中以提高决策真实性的潜力。该方法提取行人的决策模式并将其平滑地应用到人群动态模型中。不是通过任意规则来导航代理，而是通过机器学习 (ML) 算法来安排行人在运动过程中所需的方向。用实际行人数据训练的路径决策模块提高了模型在各种空间场景应用中的兼容性，不再受累于繁琐的参数微调工作。为了完整地描述行人感知到的信息，开发了多边形分割模块来划分行人的视野并识别他们之间的相互可见性。该模块过滤掉了真实情况下行人可以感知的信息，从而弥合了统计分析和数值模拟方法之间的差距。我们比较了用于路线选择行为预测的不同 ML 方法，并讨论了其影响变量在不同场景下的相对重要性。从分解的选择数据推断社会互动的感知，还讨论了从众行为和人群厌恶的有效性范围。将仿真结果与实验数据进行比较，说明模型在物理环境初始化中进行适度修改的情况下，能够准确再现各种场景中观察到的流动运动。还讨论了从众行为和人群厌恶的有效性范围。将仿真结果与实验数据进行比较，说明模型在物理环境初始化中进行适度修改的情况下，能够准确再现各种场景中观察到的流动运动。还讨论了从众行为和人群厌恶的有效性范围。将仿真结果与实验数据进行比较，说明模型在物理环境初始化中进行适度修改的情况下，能够准确再现各种场景中观察到的流动运动。