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