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Identification and analysis of offenders causing hit and run accidents using classification algorithms.
- Source :
- International Journal of Injury Control & Safety Promotion; Sep2022, Vol. 29 Issue 3, p360-371, 12p
- Publication Year :
- 2022
-
Abstract
- Hit-and-run crashes are significant concern for many countries. Due to lack of information of offending vehicles it is difficult to understand dynamics of these crashes to have a prevention plan. The paper aims to identify the impacting vehicle in hit-and-run crashes. We studied fatal road crashes of New Delhi for eleven years (2006–2016) and found that approximately 40% fatal crashes are hit-and-run with unknown impacting vehicles. We proposed a framework using eleven different machine learning-based classification algorithms – Logistic-Regression, KNN, SVM-Linear and RBF-Kernel, Naïve-Bayes, Random-Forest, DecisionTree, AdaBoost, Multilayer-Perceptron, CART and Linear-Discriminant-Analysis. We found SVM-linear-kernel gave best results. Results reveal that cars, buses, and heavy vehicles are involved vehicles in hit-and-run crashes. Buses were primary cause leading to 39% of hit-and-run during 2006-2009 thereafter cars increased drastically. Our framework is robust and scalable to any city. The outcomes provide inputs to traffic engineers for better policy prescription and road user safety. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 17457300
- Volume :
- 29
- Issue :
- 3
- Database :
- Complementary Index
- Journal :
- International Journal of Injury Control & Safety Promotion
- Publication Type :
- Academic Journal
- Accession number :
- 158878611
- Full Text :
- https://doi.org/10.1080/17457300.2022.2040541