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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;
**d_p**:drink probability (per day drink probability)
**a_p**:arrest probability (probability to get arrested if drivers is drunk)
**s_p**:sober probability (probability of drunk drivers to become a sober one)
**cs_p**: crash sober probability (probability of sober drivers to crash)
**cd_p**: crash drunk probability (probability of drunk drivers to crash)
**f_d**:free day probability (probability of jailed drivers to become free)

## HOW TO USE IT
- Choose a day of week to start (**day_of_week**)
- Choose the amount of ticks per day (**ticks_per_day**)
- Click on **setup** button.
- Hit the **start** button.

## 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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