Statistical analysis of factors associated with recent traffic accidents dataset: a practical study. (8th February 2023)

• Record Type: Journal Article
• Authors: Imreizeeq, Emad; Al-Karaki, Jamal N.; Gawanmeh, Amjad
• Is Part Of: International journal of quality engineering and technology. Volume 9:Number 1(2023)
• Page Start: 1
• Page End: 19
• Publication Date: 2023-02-08
• Subjects: logistic regression -- traffic accidents -- accident data analysis -- chi-square test; Engineering -- Quality control -- Periodicals; Systems engineering -- Periodicals; 620.0045
• Languages: English
• ISSNs: 1757-2177

Abstract

In this paper, we propose a logistic model to fit accidents dataset of 10, 000 road crash incidents for the Emirate of Abu Dhabi published in 2020. After cleaning up the dataset, we use descriptive and inferential statistical tools to study the attributes of each variable. Then, we identify the main independent variables that can be incorporated in a general logistic regression model which also includes the interactions between them. Our analysis using the significance level of (alpha = 0.05) found that there is a reduced logistic regression model that can fit the data in which the 'location of accident' can be represented using 'type of accident' and the 'age' of people involved in the accidents. Moreover, the results show that the interaction terms are not significant to be included in the model. Furthermore, the study shows that the odds for accidents by young age group (less than 40 years old) in external streets is 27% higher than the odds for internal streets, and that the odds for sequential type accidents in external streets is 13% higher than the odds for internal streets.