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## WHAT IS IT?
A dynamic model about transit accidents influencied by alcohol consumption
## HOW IT WORKS
Based in the current week day, we estimate the probability of drivers to drink alcohol and become drunk.
With the drunk drivers population we can expect that they will crash with another drivers (drunk or not) with more probability than normal (sober drivers). If two or more drivers are in the same patch there is a probability that they will crash. If an accident occurs, there are two probabilities: die or be arrested. If the driver doesn't get arrested or die, he will go away.
If a policeman watch a drunken driver also exists an arrest probability.
When a driver is arrested he's taked out from the environment (he's in jail until random sentence of days passed)
Charts will display current day's information and there is a histogram to show the percent of accidents per day
We estimated four main equations to describe the behavior of the environment's participants.
One equations for estimate the sober population, another for drunk population, other for the arrested ones and the last for the accidents quantity.
In these equations we included six probabilities;
## HOW TO USE IT
## THINGS TO TRY
You can change the amount of ticks per day to get longer days.
## EXTENDING THE MODEL
It would be nice to set the drink probability (per day) or initial drivers population using the UI instead code/dynamic system setup,
## NETLOGO FEATURES
This model is based on the dynamic systems modelator
## RELATED MODELS
An Agent based model exists to represent the simulation more accurately (made by us).
## CREDITS AND REFERENCES
Alejandra Hidalgo & Francisco González  Complex Systems, UACh 2018 
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