Title: Statistical analysis of factors associated with recent traffic accidents dataset: a practical study

Authors: Emad Imreizeeq; Jamal N. Al-Karaki; Amjad Gawanmeh

Addresses: Academic Support Department, Abu Dhabi Polytechnic, Institute of Applied Technology, P.O. Box 111499, Abu Dhabi, UAE ' Zayed University, P.O. Box 144534, Abu Dhabi, UAE; The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan ' College of Engineering and IT, University of Dubai, Dubai, UAE

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.

Keywords: logistic regression; traffic accidents; accident data analysis; chi-square test.

DOI: 10.1504/IJQET.2023.129117

International Journal of Quality Engineering and Technology, 2023 Vol.9 No.1, pp.1 - 19

Received: 08 Apr 2021
Accepted: 22 Oct 2021
Published online: 21 Feb 2023 *