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Work zone safety is influenced by many risk factors. Consequently, acomprehensive knowledge of the risk factors identified from crash data analysisbecomes critical in reducing risk levels and preventing severe crashes in workzones. This study focuses on the 2016 severe crashes that occurred in the Stateof Michigan (USA) in work zones along highway I-94. The study identified therisk factors from a wide range of crash variables characterizing environmental,driver, crash and road-related variables. The impact of these risk factors oncrash severity was investigated using frequency analyses, logistic regressionstatistics, and a machine learning Random Forest (RF) algorithm. It isanticipated that the findings of this study will help traffic engineers anddepartments of transportation in developing work zone countermeasures toimprove safety and reduce the crash risk. It was found that some of thesefactors could be overlooked when designing and devising work zone trafficcontrol plans. Results indicate, for example, the need for appropriate trafficcontrol mechanisms such as harmonizing the speed of vehicles before approachingwork zones, the need to provide illumination at specific locations of the workzone, and the need to establish frequent public education programs, flyers, andads targeting high-risk driver groups. Moreover, the Random Forest algorithmwas found to be efficient, promising, and recommended in crash data analysis,specifically, when the data sample size is small.

Impact of risk factors on work zone crashes using logistic models and Random Forest