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Navigation is the core behaviors for modeling occupancy dynamic. However, occupant

does not always perform this behavior. For example, if the number of occupants in a small area

is large, it is more likely that the occupants will be blocked by other occupants while trying to

move to their destinations. When this happens, the occupant should stop keeping moving to its

desired destination and try to avoid each other first. This comes to another important behavior in

modeling occupancy dynamics in a building: avoidance. The goal of avoidance behaviors is to

resolve the congestion caused by aggregation of large amount of occupants in a small area in the

building. Specifically, we developed a set of rules for each individual agent that captures the

avoidance maneuver of occupants while moving inside a building structure. The rules for agent

1. Keeping comfortable distance:

Let da,b denotes the distance an agent a is from its nearest neighbor b within a range

specified by r. If da,b is smaller than a threshold, then agent a will move towards the

opposite direction to b with speed s.

2. Reschedule route

If the distance to the nearest obstacle on the agent’s heading direction is larger than a threshold, then the agent moves to its next destination, this destination is generated using

waypoint graph and is stored as a queue.

If the distance to the nearest obstacle on the agent’s heading direction is smaller than the threshold. Then the agent scans an angle in the range -π/8 to -3π/8 and π/8 to 3π/8 from

its heading direction. By doing this, the agent seek for a direction that is clear of obstacle

and move towards that direction.

If the agent fails to find a direction by scanning, it wait for one time step.

If waiting time of the agent exceeds a threshold, it adds k points on the controversial

direction of its heading direction to see if it can move to sideways on these points; if it

finds one point where a side move to right or left can be scheduled, it add both that point

and a point to the right or left of that point to the temporary route. The agent will always

move towards route point of the temporary route first; after all the destination of

temporary route is reached, it moves to the destinations generated using way point graph.

If the agent fails to find a point among the k point on the controversial direction of its

heading direction that it can move to some direction that is clear of obstacle, it means this

agent is deeply blocked by other agent; it then scan an angle in the range -π/2 to -π and

This set of rules guaranteed that the agents avoid each other while moving even when congestion

happens.

The navigation behavior and avoidance behavior together define the behavior model

of the occupants. These are two basic behaviors that can represent the occupancy dynamics in the

building environment. In the simulation procedure, agent switches between these two behaviors

according to different situations. We notice that in the real world scenario, when a congestion

occurs in the building, occupants usually stop moving towards their original destination and try

to resolve the congestion first. This is because without resolving the congestion and make the

path clean of other agents, it is impossible to perform navigation behavior properly. Therefore, in

our effort for building the agent-based model, we set avoidance behavior has higher priority than

the navigation behavior so that when there is congestion ahead, agents stop performing

navigation behaviors and instead try to avoid each other so that they can make their way clean.

This is done by employ behavior selection mechanism that based on whether the agent is blocked

by another agent ahead. If it is blocked, then the agent choose to perform avoidance behavior.

The navigation behavior is only performed when there are no other agents on the moving

direction of the agent.

In this dissertation, the agent-based model represents the occupancy dynamic of the

building in most details. It has lowest abstraction level because it models agents’ attributes such as speeds, locations, intentions and interactions exactly as same as real world entities. While

using this model for estimating occupancy dynamic, the advantage is that since it is a high

resolution model, it is possible to also estimate occupancy at high resolution. However, there is a

problem that the resolution of estimation is sometimes limited by the resolution of the sensing

occupancy level in the building, using agent–based model will not provide expected estimation

outcome since gas sensor only measures the density of the various gas. It can only indirectly

indicate the distribution of people in the building rather than directly measure the positions of the

occupants. As a result, estimation using agent-based modeling and simulation may not produce

expected outcome and only adds to the complexity of the estimation. Furthermore, in the

scenario that massive occupants are getting involved, tracking exact position of each single agent

becomes impossible; it makes more sense to estimate the number of occupants in each specified

zones instead of estimating the position of each occupant. Therefore, low resolution models

which represent the occupancy dynamics at higher abstraction level are needed for

considerations mentioned above. In this dissertation, we build a coarse model for estimation at

higher abstraction level to deal with the scenario of large number of occupants get involved.

Instead of modeling the location information of occupant as each individuals’ exact position, the coarse model divide the environment into different regions and model the state of the system as

number of occupants in each region.