IntroductionThis paper presents a multi-agent based framework to simulate human and social behaviors during emergency evacuations. Among the many regulatory provisions governing a facility design, one of the key issues identified by facility managers and building inspectors is safe egress. Design of egress for places of public assembly is a formidable problem in facility and safety engineering. There have been numerous incidents reported regarding overcrowding and crushing during emergency situations [1]. In addition to injuries and loss of lives, the accompanying post-disaster psychological suffering, financial loss, and adverse publicity have long-term negative effects on the affected individuals and organizations -the survivors, the victims' families, and the local communities.Among the many factors including overcrowding and evacuation incidents, researchers have come to realize that understanding human and social behaviors in emergencies is crucial to improve crowd safety in places of public assembly [2][3][4][5][6]. In particular, 'nonadaptive crowd behaviors' are recognized to be responsible for the death and injury of most victims in crowd disasters [7]. Nonadaptive crowd behaviors refer to the destructive actions that a crowd may experience in emergency situations, such as stampede, pushing, knocking, and trampling on others. Studying nonadaptive crowd behaviors in emergency situations is difficult since it often requires exposing real people to the actual, possibly dangerous, environment. A good computational tool that takes into consideration the human and social behavior of a crowd could serve as a viable alternative.Commercially available computational tools for the simulation and design of emergency exits exist. However, most of the current computational tools focus on the modeling of spaces and occupancies but rarely take into consideration of human and

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There exist a wide variety of computational tools for the simulation and design of exits. However, due to the scarcity of behavioral data, these tools rely heavily on the assumptions about human individual and social behaviors. Many of these assumptions have been found inconsistent or incorrect. This paper presents a multi-agent based framework for studying human and social behavior during building emergency evacuations. A prototype system has been developed, which is able to demonstrate some emergent human social behaviors, such as competitive, queuing, and herding behaviors.

Emergency evacuation (egress) is considered one of the most important issues in the design of buildings and public facilities. Given the complexity and variability in an evacuation situation, computational simulation tool is often used to help assess the performance of an egress design. Studies have revealed that social behaviors can have significant influence on the evacuating crowd during an emergency. Among the challenges in designing safe egress thus include identifying the social behaviors and incorporating them in the design analysis. Even though many egress simulation tools now exist, realistic human and social behaviors commonly observed in emergency situations are not supported. This paper describes an egress simulation approach that incorporates research results from social science regarding human and social behaviors observed in emergency situations. By integrating the behavioral theories proposed by social scientists, the simulation tool can potentially produce more realistic predications than current tools which heavily rely on simplified and, in most cases, mathematical assumptions.

In this paper, we consider path protection in wavelength-routed networks with shared risk link groups (SRLGs). Specifically, we study diverse routing, where two paths without sharing any SRLG have to be found between each pair of source-destination nodes, and its applications in dynamic shared protection as well. For the NP-complete diverse routing problem, a heuristic method is proposed, which steadily outperforms an existing algorithm within the first few iterations. When more iterations of calculations are allowed, we demonstrate that the two different algorithms perform nearly the same. This interesting observation helps to achieve some insight into how to further improve the performance of the heuristics in the future.