Optimizing Movement of Cooperating Pedestrians by Exploiting Floor-Field Model and Markov Decision Process

0474861 20250603150059.320170529235959.9 85020024751 000418403500023 10.1007/978-3-319-54084-9 Optimizing Movement of Cooperating Pedestrians by Exploiting Floor-Field Model and Markov Decision Process 11 s. P cav_un_epca*0474860978-3-319-54083-22194-1009Bayesian Statistics in Action241-251ChamSpringer International Publishing2017ArgientoR.LanzaroneE.VillalobosI. A.MatteiA. optimization of cooperating pedestrians floor-field model Markov decision process combination of transition probabilities cav_un_auth*0263972 Sečkárová Vladimíra Adaptivní systémy Department of Adaptive Systems AS AS UTIA-B Department of Adaptive Systems CZ Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0307172 Hrabák Pavel Adaptivní systémy Department of Adaptive Systems AS AS UTIA-B Department of Adaptive Systems CZ Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2017/AS/seckarova-0474861.pdf cav_un_auth*0292725 GA13-13502S GA ČR cav_un_auth*0331019 GA16-09848S GA ČR CZ Optimizing movement of pedestrians is a topic of great importance, calling for modeling crowds. In this contribution we address the problem of evacuation, where pedestrians choose their actions in order to leave the endangered area. To address such decision making process we exploit the well-known floor-field model with modeling based on Markov decision processes (MDP). In addition, we also allow the pedestrians to cooperate and exchange their information (probability distribution) about the state of the surrounding environment. This information in form of probability distributions is then combined in the Kullback–Leibler sense. We show in the simulation study how the use of MDP and information sharing positively influences the amount of inhaled CO and the evacuation time. cav_un_auth*0346501 Bayesian Young Statisticians Meeting, BAYSM 2016 20160619 20160621 Florence IT BC 10000 10200 10201 2018 2 PR RVO:67985556 http://hdl.handle.net/11104/0272094 cav_un_auth*0305160 Vysoké učení technické v Brně. Fakulta stavební VUT v Brně. FAST CZ S 3+4 Proceedings Paper Statistics Probability 3+4 Proceedings Paper Statistics Probability 3+4 Proceedings Paper Statistics Probability 0.217 Q4 2017 85020024751 SCOPUS PUBMED 000418403500023 WOS cav_un_epca*0474860 Bayesian Statistics in Action Springer International Publishing 2017 Cham 241 251 978-3-319-54083-2 2194-1009 340 Argiento R. 340 Lanzarone E. 340 Villalobos I. A. 340 Mattei A.