Modeling the severity of pedestrian-vehicle accidents using multinomial logit regression

Pedestrian-vehicle accidents have a higher level of severity compared to other types of accidents. Therefore, the potential contributing factors at different severity levels of this type of accident should be identified so that appropriate measures can be taken for each of these factors.

In this paper, the multinomial logit (MNL) model is used to identify the factors affecting the severity of pedestrian-vehicle accidents. The Pedestrian-vehicle accident data from Highway Safety Information System (HSIS) of California from 2015 to 2017 is used in this article. The severity of injury is defined using the KABCO scale and is classified into five levels: fatal injury (K) and 4 injury levels including: severe or disabling injury (level 1, (A)), evident injury (level 2, (B)), minor or possible injury (Level 3, (C)) and no injury (Level 4, (O)).

The results show that the factors that significantly increase the risk of death and injury are: Drivers’ age (26 to 65 and over 65 years), weekday, low and medium annual average daily traffic (AADT), morning peak and daytime off peak and daylight.

The developed model and the results of the analysis offer effective solutions to reduce the severity of pedestrian traffic accidents and improve the safety performance of the traffic system. The results of this study can be useful for identifying the potential contributing factors to pedestrian-vehicle accidents in the real world. In addition, road safety experts and officials can use the results of this study to improve pedestrian safety.