In high-density crowds, close proximity between pedestrians makes the steady state highly vulnerable to disruption by pushing behaviors, potentially leading to serious accidents. However, the scarcity of experimental data on pushing behaviors has hindered systematic investigations into the underlying mechanisms and the development of accurate models. Using behavioral data from bottleneck experiments, we analyze the heterogeneity of pedestrians’ internal pushing tendencies, revealing that pedestrians tend to push under high-motivation conditions and in wider corridors. In addition, we introduce a spatial discretization method to encode the state of pedestrian neighbors into feature vectors, serving together with pedestrian internal pushing tendency as the input of random forest classifiers to predict whether a pedestrian would engage in pushing behaviors. By analyzing speed-headway relationships, we reveal that pushing behaviors correspond to an aggressive space-utilization movement strategy. Consequently, we propose a hybrid machine learning and physics-based model integrating the heterogeneity of internal pushing tendencies, the random forest-based prediction of pushing behaviors, and multiple movement strategies associated with pushing and non-pushing behaviors. The proposed model is calibrated using experimental data, and parameter sensitivity analysis is conducted. Validation results demonstrate that the hybrid model effectively reproduces experimental crowd dynamics, particularly in high-motivation scenarios. Moreover, the hybrid structure of the proposed model is suitable for incorporating additional behaviors, providing a solid foundation for advancing the understanding and simulation of complex pedestrian dynamics.

العنوان

ِAhmed Alia

الناشر

Elsevier Ltd

بلد الناشر

المملكة المتحدة

Indexing

Thomson Reuters

معامل التأثير

8,0

نوع المنشور

إلكتروني فقط

المجلد

182

السنة

2025

الصفحات

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