PREDICTION OF TRAFFIC ACCIDENT SEVERITY USING DATA MINING TECHNIQUES IN IBB PROVINCE, YEMEN

PREDICTION OF TRAFFIC ACCIDENT SEVERITY USING DATA MINING TECHNIQUES IN IBB PROVINCE, YEMEN

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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...

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Bibliographic Details
Main Authors: Muneer A.S. Hazaa, Redhwan M.A. Saad, Mohammed A. Alnaklani
Format: Article
Language:English
Published: UMP Publisher 2019-02-01
Series:International Journal of Software Engineering and Computer Systems
Subjects:
Traffic Accidents
Neural Network
Decision Tree
Back-Propagation Algorithm
Online Access:http://journal.ump.edu.my/ijsecs/article/view/2724/444
Description
Summary: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 Back- propagation 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.
ISSN:2289-8522
2180-0650

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