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Lianghai Jin [1; 2] and Mingzhang Xiang [1; 2] and Shu Chen [1; 2] and Xiazhong Zheng [1; 2] and Ruojun Yao [3] and Yangao Chen [4]

Academic Editor: Cengiz Çinar

1, Hubei Key Laboratory of Construction and Management in Hydropower Engineering, Yichang, Hubei 443002, China, ctgu.edu.cn

2, College of Hydraulic & Environmental Engineering, China Three Gorges University, Yichang, Hubei 443002, China, ctgu.edu.cn

3, China Institute of Water Resources and Hydropower Research, Beijing 100038, China, iwhr.com

4, Sinohydro Bureau 7 Co., Ltd., Chengdu, Sichuan 610081, China, sinohydro.com

Received Mar 19, 2017; Revised Sep 2, 2017; Accepted Sep 17, 2017

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

1. Introduction

When a large number of people out of control swarm into an evacuation passageway, they will be firmly stuck in there and will bring about an arching effect. The arching effect is produced frequently in emergency evacuations. Due to the arching effect, crowd evacuation fails now and then. In 1994, an art show at the Karamay oil field in China caused a fire to form an arch of 800 students in the evacuation exit due to the poor evacuation strategy, and eventually 323 people died in the blaze. The arching effect in emergency evacuations is a problem that cannot be fenced all the time during the researching process of emergency management [1]. Therefore, untangling the arching effect is of great significance for crowd evacuation safety and reducing the occurrence of secondary disasters such as trampling.

Up to now, many mathematical models have been developed to simulate the pedestrian evacuation process inside buildings. Classically, these models can be categorized into two types: continuous and discrete. The social force model [2–4] is widely used as a representatively continuous model, in which the interactions between occupants and various environmental stimuli are quantified as force. The representatives of the discrete models are a cellular automaton model [5] and a lattice gas model [6]. These models after calibration can usually obtain satisfactory simulation results and are able to reproduce various behaviors and self-organization phenomena, such as the “herding” behavior [7], arching effect [8], and “faster-is-lower” phenomena [9]. For the...

  

   

  

  

 

 

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