Predicting Factors of Vehicle Traffic Accidents Using Machine Learning Algorithms: In the Case of Wolaita Zone

Aklilu Elias Kurika¹ , Tigist Simon Sundado²

Section:Research Paper, Product Type: Journal-Paper
Vol.8 , Issue.4 , pp.105-115, Aug-2020

Online published on Aug 31, 2020

Copyright © Aklilu Elias Kurika, Tigist Simon Sundado . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
Vehicle traffic accident is the ultimate and major agenda for government in which special attention has been given to continuously reduce its occurrence and related risks. Wolaita zone is one of the major areas in which increased vehicle traffic accident occurs. Government and concerned bodies have given special attention to reduce accident rate in the country. By having this point as the motivating factor for study, this work tried to predict factors of vehicle accidents by using machine learning algorithms. We used unbalanced datasets with 1611 instances which was seven years data from 2005-2011 E.C. In order to analyze data and evaluate patters of datasets, KDD process model was applied. The learning algorithms applied for experiments were decision tree classifiers (J48, Random forest and Rep tree, Bayesian classifiers (Na?ve Bayes and Bayesian network). The experimental results, model evaluation and performance measurement shows that F-measure of J48 and Rep tree classifiers are comparatively similar i.e. 97.87% and 97.80% respectively and Random Forest tree performed less i.e. 90.9%. We identified the 1st experiment of J48 tree as the best model by performance and 23 best rules were generated from this experiment; best features were also identified. The most common victims, most commonly participated vehicles in accident and black spot areas for frequent accidents occurrences were identified. The findings of this study are significant for road and traffic authority and police commission for the revision and endorsement of the rules, regulations and standards related to traffic accidents; and therefore vehicle traffic accidents and related risks can be reduced generally in our country Ethiopia and specially at Wolaita Zone. We made accident data ready for further analysis in order to get most important patterns of datasets for any future researchers

Key-Words / Index Term :
Vehicle traffic accident, Decision Tree, Bayesian Classifiers, Machine Learning Algorithms, Performance measurement

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