001047376 001__ 1047376
001047376 005__ 20251215202210.0
001047376 0247_ $$2doi$$a10.1016/j.trc.2025.105395
001047376 0247_ $$2ISSN$$a0968-090X
001047376 0247_ $$2ISSN$$a1879-2359
001047376 0247_ $$2datacite_doi$$a10.34734/FZJ-2025-04264
001047376 037__ $$aFZJ-2025-04264
001047376 082__ $$a380
001047376 1001_ $$0P:(DE-Juel1)173880$$aXu, Qiancheng$$b0
001047376 245__ $$aHybrid machine learning and physics-based modeling of pedestrian pushing behaviors
001047376 260__ $$aAmsterdam [u.a.]$$bElsevier Science$$c2026
001047376 3367_ $$2DRIVER$$aarticle
001047376 3367_ $$2DataCite$$aOutput Types/Journal article
001047376 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1765781617_25387
001047376 3367_ $$2BibTeX$$aARTICLE
001047376 3367_ $$2ORCID$$aJOURNAL_ARTICLE
001047376 3367_ $$00$$2EndNote$$aJournal Article
001047376 520__ $$aIn high-density crowds, close proximity between pedestrians makes the steady state highly vulnerable to disruption by pushing behaviours, potentially leading to serious accidents. However, the scarcity of experimental data has hindered systematic studies of its mechanisms and accurate modelling. Using behavioural data from bottleneck experiments, we investigate pedestrian heterogeneity in pushing tendencies, showing that pedestrians tend to push under high-motivation and in wider corridors. We introduce a spatial discretization method to encode neighbour states into feature vectors, serving together with pedestrian pushing tendencies as inputs to a random forest model for predicting pushing behaviours. Through comparing speed-headway relationships, we reveal that pushing behaviours correspond to an aggressive space-utilization movement strategy. Consequently, we propose a hybrid machine learning and physics-based model integrating pushing tendencies heterogeneity, pushing behaviours prediction, and dynamic movement strategies adjustment. Validations show that the hybrid model effectively reproduces experimental crowd dynamics and fits to incorporate additional behaviours.
001047376 536__ $$0G:(DE-HGF)POF4-5111$$a5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x0
001047376 588__ $$aDataset connected to CrossRef, Journals: juser.fz-juelich.de
001047376 7001_ $$0P:(DE-Juel1)185878$$aÜsten, Ezel$$b1
001047376 7001_ $$0P:(DE-Juel1)185971$$aAlia, Ahmed$$b2
001047376 7001_ $$0P:(DE-HGF)0$$aHe, Biao$$b3$$eCorresponding author
001047376 7001_ $$0P:(DE-HGF)0$$aGuo, Renzhong$$b4
001047376 7001_ $$0P:(DE-Juel1)132077$$aChraibi, Mohcine$$b5$$ufzj
001047376 773__ $$0PERI:(DE-600)2015891-9$$a10.1016/j.trc.2025.105395$$gVol. 182, p. 105395 -$$p105395 -$$tTransportation research / Part C$$v182$$x0968-090X$$y2026
001047376 8564_ $$uhttps://juser.fz-juelich.de/record/1047376/files/preprint.pdf$$yOpenAccess
001047376 909CO $$ooai:juser.fz-juelich.de:1047376$$popenaire$$popen_access$$pVDB$$pdriver$$pdnbdelivery
001047376 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)185878$$aForschungszentrum Jülich$$b1$$kFZJ
001047376 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)185971$$aForschungszentrum Jülich$$b2$$kFZJ
001047376 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)132077$$aForschungszentrum Jülich$$b5$$kFZJ
001047376 9131_ $$0G:(DE-HGF)POF4-511$$1G:(DE-HGF)POF4-510$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5111$$aDE-HGF$$bKey Technologies$$lEngineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action$$vEnabling Computational- & Data-Intensive Science and Engineering$$x0
001047376 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2024-12-20
001047376 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2024-12-20
001047376 915__ $$0StatID:(DE-HGF)1160$$2StatID$$aDBCoverage$$bCurrent Contents - Engineering, Computing and Technology$$d2024-12-20
001047376 915__ $$0StatID:(DE-HGF)0600$$2StatID$$aDBCoverage$$bEbsco Academic Search$$d2024-12-20
001047376 915__ $$0StatID:(DE-HGF)0113$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2024-12-20
001047376 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2024-12-20
001047376 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
001047376 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bASC$$d2024-12-20
001047376 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2024-12-20
001047376 915__ $$0StatID:(DE-HGF)0420$$2StatID$$aNationallizenz$$d2024-12-20$$wger
001047376 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2024-12-20
001047376 920__ $$lyes
001047376 9201_ $$0I:(DE-Juel1)IAS-7-20180321$$kIAS-7$$lZivile Sicherheitsforschung$$x0
001047376 980__ $$ajournal
001047376 980__ $$aVDB
001047376 980__ $$aUNRESTRICTED
001047376 980__ $$aI:(DE-Juel1)IAS-7-20180321
001047376 9801_ $$aFullTexts