e-ISSN : 0975-4024 p-ISSN : 2319-8613   
CODEN : IJETIY    

International Journal of Engineering and Technology

Home
IJET Topics
Call for Papers 2021
Author Guidelines
Special Issue
Current Issue
Articles in Press
Archives
Editorial Board
Reviewer List
Publication Ethics and Malpractice statement
Authors Publication Ethics
Policy of screening for plagiarism
Open Access Statement
Terms and Conditions
Contact Us

ABSTRACT

ISSN: 0975-4024

Title : Trends and Modeling of Traffic Accidents in Jordan
Authors : Nabil AlKofahi, Taisir Khedaywi
Keywords : Traffic accident, Jordan, Accidents trends, Traffic models, Safety.
Issue Date : Dec 2019-Jan 2020
Abstract :
Road traffic accidents globally responsible for the death of 1.35 million people in 2016, it considered the 9th leading cause of death for people of all ages in 2012-2016. The road traffic accidents considered the third cause of death in Jordan during the year 2010. According to this study, the traffic accident data in Jordan from 1981 to 2017 were analyzed. The traffic accidents in Jordan were increasing due to the increase in population and auto ownership, which increased from 15 in 1981 to 6.3 person/vehicle in 2017. The general trend of accidents was increasing from 13567 in 1981 to 150226 in 2017; resulted in 685 deaths and injuries of 16246 injuries, with an average of 104 thousand accidents /year for the last 17 years. In spite of growing the motorization level (# registered vehicles/1000 population) from 68 in 1981 to 157.5 in 2017, the severity rate (the total number of fatal and injury in the total accidents) decreasing from 0.718 in 1981 to 0.124 in 2017. According to this study, the relationships between traffic accidents and their caused factors seemed strength with R2 ? 0.93, where the rate of casualty accidents decreasing with R2 ? 0.80. The time factors considered the significant essential variable in most models, then the growth of auto ownership. The traffic accident rate was analyzed considering several indexes such as motorization and severity levels. Despite that the motorization index is increasing with time in a similar trend as the accident rate, the severity level is decreasing due to the reduction of casualty accidents. Most of the regression models (R2=0.900) obtained from these accidents data could be used to predict the accidents and other related variables in the future.
Page(s) : 1166-1181
ISSN : 0975-4024 (Online) 2319-8613 (Print)
Source : Vol. 11, No.6
PDF : Download
DOI : 10.21817/ijet/2019/v11i6/191106026