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An important component in the occupancy estimation framework proposed in this

dissertation is the model which serves as prior knowledge of the patterns of occupants’ movement so that location of occupants can be inferred when data from sensors are not reliable.

There are majorly two categories of models that can serve this purpose. An agent based model

models each individual occupant as an autonomous agent with its own rules of performing

behaviors and interacting with others. A coarse model models the occupants as entities flowing

in a network of nodes, where each node represents a room or a corridor and each edge represents

the connectivity among the rooms and corridors. In this section, we give a literature review on

2.2.1 Agent-based models

Agent-based model is a useful and prominent tool to study people’s movement behavior in

various environments. Numerous research on building such model have been conducted by

previous researchers. In [49], the author builds an agent-based model that simulates the

movement behaviors of pedestrian in an urban environment with facilities such as church, park,

monument, post office and library. The agent-based model is featured with two different

behaviors. One is random walk, meaning that the agent moves on the street casually without a

clear purpose; the other is purposely movement, meaning that the agent has a predefined

destination to move towards to. Experiments show that in the random walk case there is strong

correlation between local integration and movements of pedestrian where this correlation is weak

in the purposely walk case. In [50], the author builds an agent-based model based on

psychological factors. Specifically, the psychological factors are classified into two classes,

psychophysical factor and psychosocial factor. The psychophysical factors include the range of the agent’s vision and the variation of their moving speed. The psychosocial factor include the goal for agent’s movement, for example, to reach a predefined destination or to avoid other agents; and the preference the agent chooses to accomplish his goal as fast as possible. The

behaviors of an agent are further categorized into three phases, one is strategic phase, another is

tactical phase and the final one is the operational phase. In strategic phase, a global destination is

determined for each of the agent so that agents always try to move towards their destinations. In

tactical phase, the agents try to avoid other agents and obstacles based on the psychological

factors introduced before. In the operational phase, the agent calculates the speed and direction

based on decision made by previous two phases to determine its movement. This model can

for simulating crowd at Tawaf area is presented. This model is featured with two levels of

movement of pedestrian, the macroscopic movements and microscopic movements. The

macroscopic movements define the way finding behaviors or navigation behaviors of pedestrians

while the microscopic movements define the interactions among pedestrians when they are

performing macroscopic behaviors. Such interactions include avoiding each other or help each

other, etc. The microscopic movements are carried out using a cellular automata and the

macroscopic movements are implemented using path tables. This work is still at its early stage

and author plans to implement several simulator in the later stages of the proposed work. In [52],

author proposes a layered approach to model the dynamics of pedestrian crowd. The surface of

the 2D environment is divided into different layers to indicate the occupancy, the position of

static obstacles and possibly the dynamic obstacles situated in the environment. These layers

contain the necessary information for agent to consult for navigation purposes. Finally, the agent

utilizes the layered environment and uses Markov decision process and semi-Markov decision

process approach to find the correct move given its occupying cell. In [53], author builds an

agent based model based on cellular automata to simulate pedestrian’s behavior. This approach uses a matrix to represent the agents’ preferred movements on all directions and uses dynamic floor field to model the trace of the pedestrians’ movement. It uses static field to represent attractive point such as exit on the map. To model the comfortable distance the pedestrian seeks

to keep from others, the author uses the concept of repulsion field which defines the force the

pedestrian agent puts upon its neighbor cells. These concepts together determines the transition

probability an agent moves from one cell to another. In [54], author proposes a model for

simulating the pedestrian crowd dynamics. This model is featured with an environment model

top layer, a route map is utilized to represent the structure and connectivity among regions in the

map. The second layer consists of a navigation map describing the paths to round over obstacles

in each region. At the lowest layer, the description about the information of each objects is

presented. The route map is a topological graph implemented with entries and exits in the regions

representing the nodes of the graph. The distance between the nodes of the graph becomes the weight of the edge of the graph. To decide a path of an agent, the agent’s destination and position are added to the topological graph and a shortest path algorithm is utilized over the graph to find

a suitable path for the agent. This approach is similar to the approach to plan path and navigation

for the agent proposed in this dissertation. Author in [54] also utilizes social force theory to

model the avoidance and interaction between agents.

In most of the work listed in this section, the agent-based model is constructed with two

different components, one is a model for the environment including obstacles and topological

structure of the environment, the other is a model for the decision and action of the agent

including macroscopic behavior such as navigation and microscopic behavior such as avoiding

each other. Inspired by these works, in this dissertation, the agent-based model proposed as a

high-resolution and low abstraction level model also contains a model for describing the

properties of the environment and a model to define the agent’s behavior such as navigation and avoiding each other. Furthermore, we adopt a rule-based approach to define the agents’ decision

on how to perform the behaviors under different circumstances.

2.2.2 Coarse Models

Coarse models in pedestrian simulation include those model the pedestrian moving

dynamics as a queuing network and those model the environment as a graph. It yields great

Some previous research have been conducted in building and utilizing coarse models. In [55],

the author discusses both of the drawback of using merely coarse grained network models and

merely fine grained network models to simulate the occupants’ behaviors in evacuation scenario and proposes to combine the two approaches together to increase the accuracy of the model and

at the same time reduce computational cost. In the simulation, the unprotected area are modeled

using fine network. In the fine network modeling approach, the whole area is divided into a

number of grid cells. Each cell can be occupied by exactly one occupant. By doing this, the

author aims at simulating the interactions among occupants and the impact of the interactions on

evacuation speed of the evacuees. The protected area is modeled using coarse network, where

each node of the network represents a room or a segment of the corridor. Since movements of

evacuees in the protected network are more orderly organized, simulation of interactions among

evacuees are not necessarily needed. Instead of using a coordinate to indicate the occupant’s

position as in fine node, the coarse node uses a one dimension value named “distance” to represent the position of the occupant. Experiment shows that the integrated approach has better

computational performance than using just fine network and can represent more details than

using just coarse network approach. In [56], the author develops a model based on network to

reproduce the pedestrian’s behaviors such as collection, spillback and dissipation. The target environment is divided into grid cells with hexagon shapes. A variable called “potential” is

utilized to indicate the distance of each cell to the exit and the minimum number of movements

an occupant must perform to reach the exit. The grid cell representation of the building structure

is then translated into a graph whose vertex represents each cell in the hexagon grid and edge

represents the pedestrian flow from one cell to another. The directed edges of the graph are

potential to a vertex of cell that has a lower potential. The result of simulations reveals that this

model can realistically simulate the behavior of collection, spillback and dissipation of

pedestrians. In[57], author proposes to use state dependent queuing network to represent and

simulate the flow dynamics of the pedestrians. The author proposes to divide modeling

procedure into three different stages: the representation stage, analysis stage and synthesis stage.

In the representation stage, the network topology of the underlying building or facility is created

with the rooms and corridors being treated as the nodes in the network. In the analysis stage, the

performance characteristics of the queuing network is determined. In the final stage of synthesis,

once the performance characteristics are determined in the analysis stage, the performance of a

queuing network can be assessed and evaluated to solve various design problem.

Coarse models listed in this section normally involves modeling the environment as a

graph with edges and vertex or a network with routes and nodes. The benefit of doing this is to

reduce the computational complexity of the simulation. Coarse model is suitable in the condition

that a large number of pedestrian or occupants are involved. Under such circumstances, agent-

based model may require more computational resources which sometimes is impossible. In this

dissertation, we proposed a simple graph model which models the building structure as a set of

vertex and edges, and models the occupants as flows in the network. This model is more efficient

than the agent-based model when large number of occupants get involved.