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A multinomial logit model of pedestrian-vehicle crash severity in North Carolina
This article develops a multinomial logit (MNL) model to investigate and identify significant contributing factors that determine the pedestrian-vehicle crash severity in North Carolina, United States. Pedestrian-vehicle crash data from Highway Safety Information System (HSIS) database from 2005 to 2012 are collected and used in this study. Crash injury severities are classified into five categories: no injury (property damage only), injury class 3 (possible injury), injury class 2 (evident injury), injury class 1 (disabling injury), and fatality. A preferred multinomial logit model is developed using SAS PROC MDC procedure and marginal effects are also calculated. The results show that the factors that significantly increase the probability of fatalities and disabling injuries include: driver’s physical condition (bad condition), vehicle type (motorcycle and heavy truck), pedestrian age (26–65 and over 65), weekend, light condition (dawn, dusk and dark), roadway characteristics (curve), roadway surface (water), roadway class (NC route) and speed limit (35–50 mph and above 50 mph). The developed model and analysis results provide insights on developing effective countermeasures to reduce vehicle-pedestrian crash severities and improve traffic system safety performance. Keywords: Multinomial logit model, Pedestrian-vehicle crashes, Severity
A multinomial logit model of pedestrian-vehicle crash severity in North Carolina
This article develops a multinomial logit (MNL) model to investigate and identify significant contributing factors that determine the pedestrian-vehicle crash severity in North Carolina, United States. Pedestrian-vehicle crash data from Highway Safety Information System (HSIS) database from 2005 to 2012 are collected and used in this study. Crash injury severities are classified into five categories: no injury (property damage only), injury class 3 (possible injury), injury class 2 (evident injury), injury class 1 (disabling injury), and fatality. A preferred multinomial logit model is developed using SAS PROC MDC procedure and marginal effects are also calculated. The results show that the factors that significantly increase the probability of fatalities and disabling injuries include: driver’s physical condition (bad condition), vehicle type (motorcycle and heavy truck), pedestrian age (26–65 and over 65), weekend, light condition (dawn, dusk and dark), roadway characteristics (curve), roadway surface (water), roadway class (NC route) and speed limit (35–50 mph and above 50 mph). The developed model and analysis results provide insights on developing effective countermeasures to reduce vehicle-pedestrian crash severities and improve traffic system safety performance. Keywords: Multinomial logit model, Pedestrian-vehicle crashes, Severity
A multinomial logit model of pedestrian-vehicle crash severity in North Carolina
Zhen Chen (author) / Wei (David) Fan (author)
2019
Article (Journal)
Electronic Resource
Unknown
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