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

Muneer A.S. Hazaaa, Redhwan M.A. Saadb, and Mohammed A. Alnaklani*a
a Faculty of Computer Sciences and Information Systems, Thamar University, Thamar, Yemen.
Email:This email address is being protected from spambots. You need JavaScript enabled to view it.
b Faculty of Engineering and Architecture, Ibb University, Ibb, Yemen.
Email:This email address is being protected from spambots. You need JavaScript enabled to view it.
*a Correspondence Author: Mohammed A. Alnakhlani, Email:This email address is being protected from spambots. You need JavaScript enabled to view it..

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 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.

Keywords: Traffic Accidents, Neural Network, Decision Tree, Back-Propagation Algorithm.

pdf ico FULL PAPER

 
 
 
 
 

Contact Us

Managing Editor of IJSECS
Faculty of Computing,
College of Computing and Applied Sciences

Universiti Malaysia Pahang
Lebuhraya Tun Razak
26300 Gambang,
Kuantan, Pahang Darul Makmur.

Tel: +609 549 2133
Fax: +609 549 2144
Email: ijsecsfskkp@ump.edu.my

Visitor Counter

0087494
Today
Yesterday
This Week
Last Week
This Month
Last Month
All days
53
89
534
598
1677
2658
87494