Data Mining Approach to Explore the Contributing Factors to Fatal Wrong-Way Crashes by Local and Non-Local Drivers

Abstract

Despite significant research efforts into wrong-way driving crashes, the fatality rate in the United States remains persistently high year after year. However, few studies have concentrated on how the driver’s familiarity with the road affects wrong-way driving. This study aims to examine if there is a difference in contributing factors to fatal wrong-way driving crashes by local and non-local drivers by utilizing Fatality Analysis Reporting System (FARS) data from 2016 to 2020. Descriptive statistics were first used to give insight into the data, and then the association rule mining method was applied to help uncover the hidden connections between contributing factors to wrong-way driving crashes for both local and non-local drivers. The findings indicated that several factors, including intoxicated drivers, an urban environment, and late-night hours from 12 A.M. to 6 A.M., play a significant role in causing local wrong-way driving crashes. On the other hand, non-lighted conditions in a rural setting significantly contributed to fatal wrong-way driving crashes by non-local drivers.

Keywords

• wrong-way driving crashes
• local and non-local drivers
• crash factors
• association rule mining