Effects of demographic and driver factors on single-vehicle and multivehicle fatal crashes investigation with multinomial logistic regression

Wesley Kumfer, Dali Wei, Hongchao Liu

Research output: Contribution to journalArticle

2 Scopus citations

Abstract

Human error is often considered the leading cause of motor vehicle crashes. Although some research has been conducted to assess the influence of human factors, full driver impacts on crashes are rarely analyzed, especially on a large scale in the United States. This study sought to identify the driver behavior and demographic factors that affected the likelihood of a multivehicle or single-vehicle fatal crash. A multinomial logistic regression framework, including odds ratios, was used to analyze the variables from several states. A tiered model approach was adopted to find the variable effects for combined, urban, rural, undivided urban, divided urban, undivided rural, and divided rural data sets. Each model produced different significant demographic or driver variables, many being unique or contradictory to the expected results of other research. Gender, often seen as a major contribution to crash outcome, was significant only for the full and urban models and likely not an important variable for determining crash outcomes in rural areas. Distracted driving and failing to make avoidance maneuvers were notably significant across various roadway types. Contrary to other studies, age, licensure, restraint use, and driving at certain times of the day were not found to be significant factors for either single- or multivehicle fatal crashes. Last, some previous conclusions about the number of occupants were refuted. These results may help safety analysts improve crash analysis and prevention methods.

Original languageEnglish
Pages (from-to)37-45
Number of pages9
JournalTransportation Research Record
Volume2518
DOIs
StatePublished - 2015

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