% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Xu:1047376,
      author       = {Xu, Qiancheng and Üsten, Ezel and Alia, Ahmed and He, Biao
                      and Guo, Renzhong and Chraibi, Mohcine},
      title        = {{H}ybrid machine learning and physics-based modeling of
                      pedestrian pushing behaviors},
      journal      = {Transportation research / Part C},
      volume       = {182},
      issn         = {0968-090X},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {FZJ-2025-04264},
      pages        = {105395 -},
      year         = {2026},
      abstract     = {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.},
      cin          = {IAS-7},
      ddc          = {380},
      cid          = {I:(DE-Juel1)IAS-7-20180321},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511)},
      pid          = {G:(DE-HGF)POF4-5111},
      typ          = {PUB:(DE-HGF)16},
      doi          = {10.1016/j.trc.2025.105395},
      url          = {https://juser.fz-juelich.de/record/1047376},
}