Editing Crowd simulation (section)

=== Modeling individual behaviors ===

Hacohen, Shoval and Shvalb<ref>{{cite journal |last1=Hacohen |first1=Shlomi |last2=Shvalb |first2=Nir |last3=Shoval |first3=Shraga |title=Dynamic model for pedestrian crossing in congested traffic based on probabilistic navigation function |journal=Transportation Research Part C: Emerging Technologies |date=January 2018 |volume=86 |pages=78–96 |doi=10.1016/j.trc.2017.10.024 |bibcode=2018TRPC...86...78H }}</ref> formulated the drivers-pedestrians dynamics at congested conflict spots. In such scenarios, the drivers and/or pedestrians do not closely follow the traffic laws. The model is based on the Probabilistic [[Navigation function]] (PNF), which was originally developed for robotics motion planning. The algorithm constructs a trajectory according to the probability for collision at each point in the entire crossing area. The pedestrian then follow a trajectory that locally minimizes their perceived probability for collision.

Helbing proposed a model based on physics using a particle system and socio-psychological forces in order to describe human crowd behavior in panic situation, this is now called the Helbing Model. His work is based on how the average person would react in a certain situation. Although this is a good model, there are always different types of people present in the crowd and they each have their own individual characteristics as well as how they act in a group structure. For instance, one person may not react to a panic situation, while another may stop walking and interfere in the crowd dynamics as a whole. Furthermore, depending on the group structure, the individual action can change because the agent is part of a group, for example, returning to a dangerous place in order to rescue a member of that group. Helbing's model can be generalized incorporating individualism, as proposed by Braun, Musse, Oliveira and Bodmann.<ref name=":0" />

In order to tackle this problem, individuality should be assigned to each agent, allowing to deal with different types of behaviors. Another aspect to tackle this problem is the possibility to group people, forming these group causes people to change their behavior as a function of part of the group structure. Each agent (individual) can be defined according to the following parameters:

# Id – Agent identifier
# IdFamily – Identifier of the family. A family is a predefined group formed by agents who know each other
# DE – Dependence level of the agent which mimics the need for help. Values [0,1]
# AL – Altruism level representing the tendency to help other agents. Values [0,1]
# v<sub>i</sub> – Speed of the agent

To model the effect of the dependence parameter with ''individual agents.''

When evaluating the speed of the agent, it is clear that if the value of the dependence factor, DE, is equal to one, then the person would be fully disabled making him unable to move. If the dependence factor is equal to zero, then the person is able to run at his max speed.

[[Group formation]] is related to the Altruism force which is implemented as an interaction force between two or more agents who are part of the same family.

The places where this would be helpful would be in an evacuation scenario. Take for example, an evacuation of a building in the case of a fire. Taking into account the characteristics of individual agents and their group performances, determining the outcome of how the crowd would exit the building is critically important in creating the layout of the building.<ref name=":0">{{cite book |doi=10.1109/CASA.2003.1199317 |chapter=Modeling individual behaviors in crowd simulation |title=Proceedings 11th IEEE International Workshop on Program Comprehension |pages=143–8 |year=2003 |last1=Braun |first1=A. |last2=Musse |first2=S.R. |last3=De Oliveira |first3=L.P.L. |last4=Bodmann |first4=B.E.J. |isbn=978-0-7695-1934-0 }}</ref>