Respuesta :
Discussing about classification of Traffic Crash Injury Severity, Machine Learning Algorithms, There has been little research done on causal analysis and categorization of injury severity using non-parametric approaches for road accidents.
What is Casual Analysis?
- The suggested severity categorization approach produces three groups for collisions with fatalities and serious injuries (KA), crashes with minor injuries (BC), and crashes with only property damage (PDO).
- Granger Causality assisted in determining the components that have the greatest impact on collision severity, but learning-based models performed differently in their ability to predict the severity classes.
- The speed limit, road surface and weather conditions, traffic volume, the presence of work zones, the presence of personnel in work zones, and high occupancy vehicle (HOV) lanes were among the elements found by Granger causality analysis as having the greatest impact on crash severity.
- The classifiers' prediction performance produced a range of outcomes for the various classifications.
What is Machine Learning?
- With the use of machine learning (ML), which is a form of artificial intelligence (AI), software programs can predict outcomes more accurately without having to be explicitly instructed to do so. In order to forecast new output values, machine learning algorithms use historical data as input.
The small corpus of research on causal analysis and classification prediction of traffic crash injury severity using non-parametric methodologies is augmented by the findings of this study.
Know more about machine learning with the help of the given link:
https://brainly.com/question/16042499
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