Predicting the Severity of Motor Vehicle Accident Injuries in Adana-Turkey Using Machine Learning Methods and Detailed Meteorological Data

Cigdem ACI; Cevher OZDEN

doi:10.18201/ijisae.2018637934

	Predicting the Severity of Motor Vehicle Accident Injuries in Adana-Turkey Using Machine Learning Methods and Detailed Meteorological Data Cigdem ACI, Cevher OZDEN
DOI: http://dx.doi.org/10.18201/ijisae.2018637934
Cigdem ACI http://orcid.org/0000-0002-0028-9890 Mersin University, Turkey \| caci@mersin.edu.tr Cevher OZDEN http://orcid.org/0000-0002-8445-4629 Çukurova University, Turkey
Abstract Traffic accidents are among the most important issues facing every nation in the world as they cause many deaths and injuries as well as economic losses every year. In this study, the traffic accidents that took place in Adana, have been classifiedaccording to injury severity (i.e. fatal or non-fatal) and the factors affecting the accident outcome are investigated. The study included the traffic accident reports kept by Regional Traffic Division and the weather data provided by the Regional Directorate of Meteorology during 2005-2015. Five major machine learning methods (i.e. k-Nearest Neighbor, Naive Bayes, Multilayer Perceptron, Decision Tree, Support Vector Machine) and one statistical method, Logistic Regression, were employed for prediction models and performances of the models as well as the effective parameters were compared. The main objective of the study is to determine how important weather and other phenomena are for the occurrence of traffic accidents. Decision Tree, k-Nearest Neighbor and Multilayer Perceptron based models yielded higher accuracy in classification of accidents compared to other models. Furthermore, in Area Under Curve based analysis of factor importance, it was determined that Mean Cloudiness, Existence of Traffic Control and Ground Surface Temperature had higher positive effects, while Maximum Temperature and Weather (kept by traffic officers) parameters decreased the accuracy of models. Keywords injury severity; machine learning; prediction; predictor importance; traffic accident
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