Prediction of Traffic Accident Severity Using Data Mining Techniques in Ibb Province, Yemen

Abstract

Traffic accidents are the leading causes beyond death; it is the concern of most countries that strive for finding radical solutions to this problem. There are several methods used in the process of forecasting traffic accidents such as classification, assembly, association, etc. This paper surveyed the latest studies in the field of traffic accident prediction; the most important tools and algorithms were used in the prediction process such as Backpropagation Neural Networks and the decision tree. In addition, this paper proposed a model for predicting traffic accidents based on dataset obtained from the Directorate General of Traffic Statistics, Ibb, Yemen.

Evaluation of Tree-Based Machine Learning Algorithms for Accident Risk Mapping Caused by Driver Lack of Alertness at a National Scale

“…Because RTA causes significant financial damage to countries and leads to many people's death every year [6], its modeling has always been a hot topic. In general, the available traffic accident models are in three categories of severity modeling [7][8][9], risk modeling [10,11], and frequency modeling [12,13]. These models aids in the prediction of road safety conditions by explaining the influence of several elements on accident occurrence [10].…”