Abstract
The root cause of traffic accidents is hard to determine these days due to complex combination of characteristics like mental state of driver, road conditions, weather conditions, traffic, and violations of traffic rules to name a few. The deployment of machine learning classifiers has replaced traditional data mining techniques like association rule mining. Application of machine learning techniques in the field of road accidents is gaining popularity these days. This paper utilized four machine learning techniques viz. Naïve Bayes, k-Nearest Neighbours, Decision trees, and Support Vector Machines for evaluation of Punjab road accidents. This work had a challenge of performing parametric evaluation to extract highly relevant parameters especially for Punjab. The outcome of this study yields 12 most suitable parameters and maximum performance of 86.25% for Decision Tree classifier.
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Singh, J., Singh, G., Singh, P., Kaur, M. (2019). Evaluation and Classification of Road Accidents Using Machine Learning Techniques. In: Shetty, N., Patnaik, L., Nagaraj, H., Hamsavath, P., Nalini, N. (eds) Emerging Research in Computing, Information, Communication and Applications. Advances in Intelligent Systems and Computing, vol 882. Springer, Singapore. https://doi.org/10.1007/978-981-13-5953-8_17
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DOI: https://doi.org/10.1007/978-981-13-5953-8_17
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