Proceedings of the 5th International Symposium on Social Science (ISSS 2019)

Optimization of Emergency Evacuation Strategy Based on Social Force Model

Authors
Yahui Zhang
Corresponding Author
Yahui Zhang
Available Online 18 March 2020.
DOI
10.2991/assehr.k.200312.068How to use a DOI?
Keywords
social force model, bottleneck evacuation model, fluid theory
Abstract

In order to deal with the possible terrorist attacks and other emergencies, it is particularly important to develop a reasonable evacuation plan. We develop an evacuation model to solve this problem. The evacuation model is based on social force and bottleneck evacuation model. The simulation experiments show that the social force model proposed in this paper can reproduce the evacuation behavior of the crowd in emergency situations, and the bottleneck after the transformation greatly eases the congestion of pedestrians here. The passability of pedestrians at the bottleneck and the overall speed of travel have greatly improved.

Copyright
© 2020, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 5th International Symposium on Social Science (ISSS 2019)
Series
Advances in Social Science, Education and Humanities Research
Publication Date
18 March 2020
ISBN
10.2991/assehr.k.200312.068
ISSN
2352-5398
DOI
10.2991/assehr.k.200312.068How to use a DOI?
Copyright
© 2020, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Yahui Zhang
PY  - 2020
DA  - 2020/03/18
TI  - Optimization of Emergency Evacuation Strategy Based on Social Force Model
BT  - Proceedings of the 5th International Symposium on Social Science (ISSS 2019)
PB  - Atlantis Press
SP  - 379
EP  - 382
SN  - 2352-5398
UR  - https://doi.org/10.2991/assehr.k.200312.068
DO  - 10.2991/assehr.k.200312.068
ID  - Zhang2020
ER  -