Please wait a minute...
Front. Inform. Technol. Electron. Eng.  2013, Vol. 14 Issue (7): 477-485    DOI: 10.1631/jzus.CIDE1301
    
A review of behavior mechanisms and crowd evacuation animation in emergency exercises
Gao-qi He, Yu Yang, Zhi-hua Chen, Chun-hua Gu, Zhi-geng Pan
Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai 200237, China; Digital Media and Interaction Research Center, Hangzhou Normal University, Hangzhou 310036, China
Download:   PDF(0KB)
Export: BibTeX | EndNote (RIS)      

Abstract  Emergency exercises are an efficient approach for preventing serious damage and harm, including loss of life and property and a wide range of adverse social effects, during various public emergencies. Among various factors affecting the value of emergency exercises, including their design, development, conduct, evaluation, and improvement planning, this paper emphasizes the focal role of evacuees and their behavior. We address two concerns: What are the intrinsic reasons behind human behavior? How do we model and exhibit human behavior? We review studies investigating the mechanisms of psychological behavior and crowd evacuation animation. A comprehensive analysis of logical patterns of behavior and crowd evacuation is presented first. The interactive effects of information (objective and subjective), psychology (panic, small groups, and conflicting roles), and six kinds of behavior contribute to a more effective understanding of an emergency scene and assist in making scientific decisions. Based on these studies, a wide range of perspectives on crowd formation and evacuation animation models is summarized. Collision avoidance is underlined as a special topic. Finally, this paper highlights some of the technical challenges and key questions to be addressed by future developments in this rapidly developing field.

Key wordsEmergency exercises      Behavior mechanisms      Crowd evacuation animation      Collision avoidance     
Received: 29 December 2012      Published: 05 July 2013
CLC:  TP391  
Cite this article:

Gao-qi He, Yu Yang, Zhi-hua Chen, Chun-hua Gu, Zhi-geng Pan. A review of behavior mechanisms and crowd evacuation animation in emergency exercises. Front. Inform. Technol. Electron. Eng., 2013, 14(7): 477-485.

URL:

http://www.zjujournals.com/xueshu/fitee/10.1631/jzus.CIDE1301     OR     http://www.zjujournals.com/xueshu/fitee/Y2013/V14/I7/477


A review of behavior mechanisms and crowd evacuation animation in emergency exercises

Emergency exercises are an efficient approach for preventing serious damage and harm, including loss of life and property and a wide range of adverse social effects, during various public emergencies. Among various factors affecting the value of emergency exercises, including their design, development, conduct, evaluation, and improvement planning, this paper emphasizes the focal role of evacuees and their behavior. We address two concerns: What are the intrinsic reasons behind human behavior? How do we model and exhibit human behavior? We review studies investigating the mechanisms of psychological behavior and crowd evacuation animation. A comprehensive analysis of logical patterns of behavior and crowd evacuation is presented first. The interactive effects of information (objective and subjective), psychology (panic, small groups, and conflicting roles), and six kinds of behavior contribute to a more effective understanding of an emergency scene and assist in making scientific decisions. Based on these studies, a wide range of perspectives on crowd formation and evacuation animation models is summarized. Collision avoidance is underlined as a special topic. Finally, this paper highlights some of the technical challenges and key questions to be addressed by future developments in this rapidly developing field.

关键词: Emergency exercises,  Behavior mechanisms,  Crowd evacuation animation,  Collision avoidance 
[1] Gopi Ram , Durbadal Mandal , Sakti Prasad Ghoshal , Rajib Kar . Optimal array factor radiation pattern synthesis for linear antenna array using cat swarm optimization: validation by an electromagnetic simulator[J]. Front. Inform. Technol. Electron. Eng., 2017, 18(4): 570-577.
[2] Lin-bo Qiao, Bo-feng Zhang, Jin-shu Su, Xi-cheng Lu. A systematic review of structured sparse learning[J]. Front. Inform. Technol. Electron. Eng., 2017, 18(4): 445-463.
[3] Yuan-ping Nie, Yi Han, Jiu-ming Huang, Bo Jiao, Ai-ping Li. Attention-based encoder-decoder model for answer selection in question answering[J]. Front. Inform. Technol. Electron. Eng., 2017, 18(4): 535-544.
[4] Rong-Feng Zhang , Ting Deng , Gui-Hong Wang , Jing-Lun Shi , Quan-Sheng Guan . A robust object tracking framework based on a reliable point assignment algorithm[J]. Front. Inform. Technol. Electron. Eng., 2017, 18(4): 545-558.
[5] Wen-yan Xiao, Ming-wen Wang, Zhen Weng, Li-lin Zhang, Jia-li Zuo. Corpus-based research on English word recognition rates in primary school and word selection strategy[J]. Front. Inform. Technol. Electron. Eng., 2017, 18(3): 362-372.
[6] . A quality requirements model and verification approach for system of systems based on description logic[J]. Front. Inform. Technol. Electron. Eng., 2017, 18(3): 346-361.
[7] Ali Darvish Falehi, Ali Mosallanejad. Dynamic stability enhancement of interconnected multi-source power systems using hierarchical ANFIS controller-TCSC based on multi-objective PSO[J]. Front. Inform. Technol. Electron. Eng., 2017, 18(3): 394-409.
[8] Li Weigang. First and Others credit-assignment schema for evaluating the academic contribution of coauthors[J]. Front. Inform. Technol. Electron. Eng., 2017, 18(2): 180-194.
[9] Hui Chen, Bao-gang Wei, Yi-ming Li, Yong-huai Liu, Wen-hao Zhu. An easy-to-use evaluation framework for benchmarking entity recognition and disambiguation systems[J]. Front. Inform. Technol. Electron. Eng., 2017, 18(2): 195-205.
[10] Jun-hong Zhang, Yu Liu. Application of complete ensemble intrinsic time-scale decomposition and least-square SVM optimized using hybrid DE and PSO to fault diagnosis of diesel engines[J]. Front. Inform. Technol. Electron. Eng., 2017, 18(2): 272-286.
[11] Yue-ting Zhuang, Fei Wu, Chun Chen, Yun-he Pan. Challenges and opportunities: from big data to knowledge in AI 2.0[J]. Front. Inform. Technol. Electron. Eng., 2017, 18(1): 3-14.
[12] Bo-hu Li, Hui-yang Qu, Ting-yu Lin, Bao-cun Hou, Xiang Zhai, Guo-qiang Shi, Jun-hua Zhou, Chao Ruan. A swarm intelligence design based on a workshop of meta-synthetic engineering[J]. Front. Inform. Technol. Electron. Eng., 2017, 18(1): 149-152.
[13] Yong-hong Tian, Xi-lin Chen, Hong-kai Xiong, Hong-liang Li, Li-rong Dai, Jing Chen, Jun-liang Xing, Jing Chen, Xi-hong Wu, Wei-min Hu, Yu Hu, Tie-jun Huang, Wen Gao. Towards human-like and transhuman perception in AI 2.0: a review[J]. Front. Inform. Technol. Electron. Eng., 2017, 18(1): 58-67.
[14] Yu-xin Peng, Wen-wu Zhu, Yao Zhao, Chang-sheng Xu, Qing-ming Huang, Han-qing Lu, Qing-hua Zheng, Tie-jun Huang, Wen Gao. Cross-media analysis and reasoning: advances and directions[J]. Front. Inform. Technol. Electron. Eng., 2017, 18(1): 44-57.
[15] Le-kui Zhou, Si-liang Tang, Jun Xiao, Fei Wu, Yue-ting Zhuang. Disambiguating named entities with deep supervised learning via crowd labels[J]. Front. Inform. Technol. Electron. Eng., 2017, 18(1): 97-106.