Commit dfb4a0e2 authored by Duncan Deveaux's avatar Duncan Deveaux
Browse files

updated readme

parent e5ad0886
......@@ -22,20 +22,20 @@ Once the data has been generated, it can be analyzed and visualized with the fol
# Quickstart
```console
python3 parse_exits.py --location=[INSERT YOUR LOCATION HERE]
# This will generate the training data for roundabout exit probability model training.
# Pickle files will be generated in the exit_parse/ directory.
python3 parse.py --probability_weighting --location=[INSERT YOUR LOCATION HERE]
# The probability_weighting option is important, as it will allow the parse.py script to use the exit probability model to generate probability-weighted risk.
# Pickle files will be generated in the ttc_parse/ directory.
python3 analysis_exits.py --location=[INSERT YOUR LOCATION HERE]
# Will show visualization of the training data for the roundabout exit probability model.
# Will train the model and give its accuracy.
python3 analysis.py --location=[INSERT YOUR LOCATION HERE] --ttclimit=7.5 --cvtime=60.0
# Will show correlation analysis between TTC CV and risk.
# --ttclimit is the maximum TTC value in seconds that will be considered for computing the CV.
# --cvtime is time in terms of TTC data values that will be used to compute the CV. (e.g. compute the CV of TTC values over the last 60 seconds of TTC values).
$ python3 parse_exits.py --location=[INSERT YOUR LOCATION HERE]
This will generate the training data for roundabout exit probability model training.
Pickle files will be generated in the exit_parse/ directory.
$ python3 parse.py --probability_weighting --location=[INSERT YOUR LOCATION HERE]
The probability_weighting option is important, as it will allow the parse.py script to use the exit probability model to generate probability-weighted risk.
Pickle files will be generated in the ttc_parse/ directory.
$ python3 analysis_exits.py --location=[INSERT YOUR LOCATION HERE]
Will show visualization of the training data for the roundabout exit probability model.
Will train the model and give its accuracy.
$ python3 analysis.py --location=[INSERT YOUR LOCATION HERE] --ttclimit=7.5 --cvtime=60.0
Will show correlation analysis between TTC CV and risk.
--ttclimit is the maximum TTC value in seconds that will be considered for computing the CV.
--cvtime is time in terms of TTC data values that will be used to compute the CV. (e.g. compute the CV of TTC values over the last 60 seconds of TTC values).
```
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