Analysis and Prediction of Road Accident Severity Using Binary Logit Model: A Case Study of Road Traffic Accident Data in Canada 2019

Nemati, Sayed Amin; Amin, Reza; Khodaii, Ali

doi:10.22034/cpj.2023.409086.1219

Analysis and Prediction of Road Accident Severity Using Binary Logit Model: A Case Study of Road Traffic Accident Data in Canada 2019

https://doi.org/10.22034/cpj.2023.409086.1219

Volume 5, Issue 6 - Serial Number 52
September 2023

Pages 45-57

Document Type : Research Article

Authors

Sayed Amin Nemati ¹

Reza Amin ²

Ali Khodaii ³

¹ Transportation Engineering, Civil and Environmental Engineering, Amirkabir University of Technology, Tehran, Iran

² M.Sc. graduate, Department of Civil and Environment, AmirKabir University of Technology, Tehran, Iran

³ Full professor, Department of Civil and Environment, AmirKabir University of Technology, Tehran

Abstract

The present study focuses on the binary logit model for predicting the probability of driver fatalities in road accidents. The study utilizes data from road traffic accidents in Canada. These data were collected by the highway police in 2019 and recorded in the National Collison Database (NCBD). The dependent variable in this model is the severity of accidents, which is a binary variable representing driver fatalities and injuries. The independent variables include vehicle types, vehicle age, days of the week, time intervals, same-direction and opposite-direction collisions, intersections, weather conditions, driver age, and gender. By analyzing the data and estimating the parameters, the model can predict up to 41% of the variations in the dependent variable. In the model validation stage, the data were divided into two parts, with 70% used for modeling and 30% for validation. McFadden's pseudo were used to evaluate the model's performance. The models were constructed using SPSS and Nlogit6.0 software. The results demonstrate that the model fits well with the data and has the capability to predict changes in accident severity. Consequently, the study indicates that variables such as road dryness, midnight time interval, and vehicle age contribute to an increase in driver fatalities, while variables such as light duty vehicles, school buses, and same-direction collisions contribute to a reduction in fatalities.

Keywords

Binary logit model

probability of accident occurrence

accident severity

road accidents

types of vehicles

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