TY  - JOUR
AU  - Xu, Qiancheng
AU  - Üsten, Ezel
AU  - Alia, Ahmed
AU  - He, Biao
AU  - Guo, Renzhong
AU  - Chraibi, Mohcine
TI  - Hybrid machine learning and physics-based modeling of pedestrian pushing behaviors
JO  - Transportation research / Part C
VL  - 182
SN  - 0968-090X
CY  - Amsterdam [u.a.]
PB  - Elsevier Science
M1  - FZJ-2025-04264
SP  - 105395 -
PY  - 2026
AB  - In high-density crowds, close proximity between pedestrians makes the steady state highly vulnerable to disruption by pushing behaviours, potentially leading to serious accidents. However, the scarcity of experimental data has hindered systematic studies of its mechanisms and accurate modelling. Using behavioural data from bottleneck experiments, we investigate pedestrian heterogeneity in pushing tendencies, showing that pedestrians tend to push under high-motivation and in wider corridors. We introduce a spatial discretization method to encode neighbour states into feature vectors, serving together with pedestrian pushing tendencies as inputs to a random forest model for predicting pushing behaviours. Through comparing speed-headway relationships, we reveal that pushing behaviours correspond to an aggressive space-utilization movement strategy. Consequently, we propose a hybrid machine learning and physics-based model integrating pushing tendencies heterogeneity, pushing behaviours prediction, and dynamic movement strategies adjustment. Validations show that the hybrid model effectively reproduces experimental crowd dynamics and fits to incorporate additional behaviours.
LB  - PUB:(DE-HGF)16
DO  - DOI:10.1016/j.trc.2025.105395
UR  - https://juser.fz-juelich.de/record/1047376
ER  -