Prediction of Severity of an Accident Based on the Extent of Injury using Machine Learning
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© 2022 by IJCTT Journal
Volume-70 Issue-9
Year of Publication : 2022
Authors : Surendra Kumar Reddy Koduru
DOI :  10.14445/22312803/IJCTT-V70I9P106

How to Cite?

Surendra Kumar Reddy Koduru, "Prediction of Severity of an Accident Based on the Extent of Injury using Machine Learning," International Journal of Computer Trends and Technology, vol. 70, no. 9, pp. 43-49, 2022. Crossref, https://doi.org/10.14445/22312803/IJCTT-V70I9P106

Abstract
Accidents are currently regarded as the most disturbing cause in many countries. Several deaths have been recorded for generating massive deaths throughout numerous countries just as a result of road accidents that predominantly occur during traffic. Vehicle accidents are the leading cause of deception, distress, and fatality. The majority of accidents occur only over a long period from various countries and are referred to as unsafe or dangerous conditions associated with large volumes of traffic, particularly vehicle traffic. Exploring the causes of these incidents can help identify the most important features in determining the accident`s severity.

Almost all the repercussions, such as light conditions, speed zones, part of the injury, climate, and so on, are also participating in and closely linked to the cause of traffic accidents, of which only a few are emphasized and addressed in accident criticality rules. The overall goal of this study is to measure the severity of traffic accidents that occur. The key directing vectors are the accident attributes, which include the part of the slight, car allocation on the highway, and ecologically responsible properties, all of which help the output results about the strong levels of the accident criticality classes.

Keywords
Severity prediction, Machine learning, Accident prediction, Road accidents, Traffic system design.

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