# Integrating Decentralized Indoor Evacuation with Information Depositories in the Field
## 2 Related Work
### Evacuation Models and Crowd Dynamics
[22] identified two reasons that may cause people being injured or killed during evacuations: either due to failing to evacuate timely, or due to the crowd's behavior such as shuffling, pushing, crushing, and trampling. [PERSON] et al. [19] argued that most models for evacuation dynamics consider individuals as particles, so they added social identity to pedestrian simulation. They take helping others as an example to investigate whether psychological models and computer models of pedestrian motion can be combined so that simulation results correspond to observations from crowd psychology. [PERSON] et al. [10] found that standing pedestrians affect crowd dynamics strongly and should be considered when designing critical infrastructures such as railway stations. [PERSON] et al. [48] developed a hybrid model which combines a dynamic navigation field with a navigation graph. Space and time are discretized using cellular automata, which act as the underlying grid for constructing the navigation field. The dynamic navigation field employs the analogy of an expanding wave which estimates the travel times of pedestrians traveling between an origin and a given destination considering congestions. Such a dynamic navigation field allows evacuees (a) to follow a direction which is subject to minimization of estimated travel times, and (b) to steer around congestions even when the congestions are not visible from their actual position. However, [PERSON] et al. [48] aimed to provide a realistic simulation of crowd dynamics rather than optimizing evacuation paths, so they also added a navigation graph constructed on top of the scenarios' geometry which acts as a visibility graph to help distinguish between visible and invisible areas. In contrast to building evacuation, other research deals with optimizing traffic during evacuations on an urban scale [25; 55]. [PERSON] and [PERSON] [25] proposed a decentralized strategy that evacuated the maximum number of people at all times. [PERSON] and [PERSON] [20] proposed a network optimization-based methodology to support crowd movement during large public gatherings held in venues such as complex buildings, transportation stations, football stadiums, and commercial malls. [PERSON] et al. [40] presented an agent-based travel demand modeling system to generate household activity-travel plans for hurricane evacuation simulation, in which each agent represented a household. They considered the ultimate evacuation trips of a household as the result of a series of demands and the thereafter decisions, such as whether to evacuate, when, to where, by which mode, as well as pre-evacuation preparation activities such as purchasing fuel and food, or family gathering. Another model addressing the simulation of pre-evacuation behavior has been studied by [PERSON] et al. [56], which adopted Random Utility Theory. Compared with indoor evacuation which involves pedestrians, evacuation planning in urban areas involves different tasks: such as deciding shelter locations, routing for public transport and for individual traffic simultaneously [13]. A review of highway-based evacuation modeling and simulation has been provided as well [57]. This paper focuses on simulating the heterogeneous characters of each evacuee and their interactions by using an agent-based model. Each evacuee acts as an autonomous agent which independently explores the environment and acquires different knowledge. In particular, this paper aims to investigate optimal evacuation strategies when the density of evacuees is very low and chances for encounters are rare, thus crowd behavior has been neglected in all the simulations.