Traffic Accidents Classification and Injury Severity Prediction
Shyam Sunder Pabboju1, P.Satya Shekar Varma2, Surya Prakash Jella3
1Shyam Sunder Pabboju*, Assistant Professor, CSE, JNTUH, MGIT, Hyderabad, India.
2P.Satya Shekar Varma, Assistant Professor CSE, JNTUH, MGIT, Hyderabad, India.
3Surya Prakash Jella, student, CSE, JNTUH, MGIT, Hyderabad.
Manuscript received on March 15, 2020. | Revised Manuscript received on March 30, 2020. | Manuscript published on April 10, 2020. | PP: 845-849 | Volume-9 Issue-6, April 2020. | Retrieval Number: F3969049620/2020©BEIESP | DOI: 10.35940/ijitee.F3969.049620
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Traffic Accidents, Decision Tree, Logistic Regression, Injury Severity Prediction.
Scope of the Article: Network traffic characterization and measurements