Open AccessTraffic Accidents Classification and Injury Severity Prediction
Author(s) -
Shyam Sunder Pabboju*,
P.R.G. Varma,
Surya Prakash Jella
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.f3969.049620
Subject(s) - logistic regression , computer science , predictive modelling , traffic accident , road traffic , poison control , transport engineering , medical emergency , engineering , machine learning , medicine
Traffic accidents are one of the most life-threatening dangers to human being. Deaths and injuries due to traffic accidents have a great impact on society. Traffic accidents information and data provided by public can be useful to classify these accidents according to their type and severity, and consequently try to build predictive model. Detecting and identifying injury severity in traffic accidents in real time is primordial for speeding post-accidents protocols as well as developing general road safety policies. In this project we are using Logistic Regression algorithm to classify accident data. The data to be analysed is collected from various sources, is both structured and unstructured and has several attributes. In this project we are going to detect and analyse data together to generate decision trees that give insights on previous accidents.