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| 001 | 887683 | ||
| 005 | 20210130010617.0 | ||
| 024 | 7 | _ | |a 2128/26103 |2 Handle |
| 037 | _ | _ | |a FZJ-2020-04344 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Schrödter, Tobias |0 P:(DE-Juel1)145189 |b 0 |e Corresponding author |
| 111 | 2 | _ | |a Fire and Evacuation Modeling Technical Conference |g FEMTC |c virtual |d 2020-09-09 - 2020-09-11 |w virtual |
| 245 | _ | _ | |a Modeling Waiting Behavior at Train Stations with Cellular Automaton |
| 260 | _ | _ | |c 2020 |
| 300 | _ | _ | |a 1-11 |
| 336 | 7 | _ | |a CONFERENCE_PAPER |2 ORCID |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
| 336 | 7 | _ | |a conferenceObject |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Conference Paper |2 DataCite |
| 336 | 7 | _ | |a Contribution to a conference proceedings |b contrib |m contrib |0 PUB:(DE-HGF)8 |s 1605002248_16238 |2 PUB:(DE-HGF) |
| 500 | _ | _ | |a online Proceedings of FEMTC 2020 |
| 520 | _ | _ | |a In general, pedestrian models describe the movement of pedestrians with varying level of detail. Often complex processes like way-finding in buildings or navigation through a crowd are solved in a simplified way by collision avoidance algorithms or social forces. For some applications, like the evacuation of a building the moving of pedestrians towards a goal is the substantial contribution determining the dynamic of the process. This changes in more general context, like pedestrians on platforms or airport gates, where waiting people restrict the space for the movement of the others. After reaching their waiting position, pedestrians don’t have any need to keep moving unless the event they are waiting for occurs, e.g. arrival of a train or boarding of the plane starts. In this regard, as opposed to a “moving dynamics” resulting from pedestrians evacuating a specific place, we focus on a “waiting dynamics” where pedestrians visit some temporary areas and wait for a certain amount of time.We model the process of finding the waiting positions, the pedestrians would like to retain throughout the waiting time. The decision will be based on a floor-field representing comfort values based on static and dynamic influences. We consider the geometry, the positions of the entrances/exits, and other constant parameters as static influence (Ezaki et al. (2016)). To simulate the real-time reaction of the pedestrians to the observed situations, the distribution and motion of the neighbors within a waiting area are considered dynamically. Since the pedestrians usually do not have a global overview of the region, an iterative approach is proposed. First the pedestrians only considers the parts of the geometry which are visible from the current position. If none of the comfort values in the visible area is above an individual threshold, the pedestrians moves through the geometry until such a position is found. |
| 536 | _ | _ | |a 511 - Computational Science and Mathematical Methods (POF3-511) |0 G:(DE-HGF)POF3-511 |c POF3-511 |f POF III |x 0 |
| 536 | _ | _ | |a KapaKrit - Optimizing the capacity of train stations in case of large-scale emergency evacuation events (jias72_20191101) |0 G:(DE-Juel1)jias72_20191101 |c jias72_20191101 |f KapaKrit - Optimizing the capacity of train stations in case of large-scale emergency evacuation events |x 1 |
| 536 | _ | _ | |0 G:(DE-Juel1)PHD-NO-GRANT-20170405 |x 2 |c PHD-NO-GRANT-20170405 |a PhD no Grant - Doktorand ohne besondere Förderung (PHD-NO-GRANT-20170405) |
| 700 | 1 | _ | |a Chraibi, Mohcine |0 P:(DE-Juel1)132077 |b 1 |
| 856 | 4 | _ | |u https://files.thunderheadeng.com/femtc/2020_d1-04-schrodter-paper.pdf |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/887683/files/2020_d1-04-schrodter-paper.pdf |y OpenAccess |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/887683/files/2020_d1-04-schrodter-paper.pdf?subformat=pdfa |x pdfa |y OpenAccess |
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| 913 | 1 | _ | |a DE-HGF |b Key Technologies |1 G:(DE-HGF)POF3-510 |0 G:(DE-HGF)POF3-511 |2 G:(DE-HGF)POF3-500 |v Computational Science and Mathematical Methods |x 0 |4 G:(DE-HGF)POF |3 G:(DE-HGF)POF3 |l Supercomputing & Big Data |
| 914 | 1 | _ | |y 2020 |
| 915 | _ | _ | |a OpenAccess |0 StatID:(DE-HGF)0510 |2 StatID |
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