Investigating the Contributing Factors to Crashes With and Without the Presence of Work Zone Workers Using Machine Learning Techniques

As the nation’s roadways continue to deteriorate, the presence of work zones on US highways is anticipated to increase, highlighting the crucial need for the safety of both work zone workers and road users. This study combined descriptive statistics and Shapley feature important analysis to examine work zone crash data in Ohio from 2019 to 2023. The goal was to identify the factors contributing to crash severity with and without the presence of work zone workers. Various machine learning models, including k-nearest neighbors, random forest, eXtreme gradient boosting, and Light gradient boosting machines, were employed to predict crash outcomes across three data sets. LightGBM emerged as the best-performing model. Shapley values were then utilized to interpret the contributing factors to crash injury severity. The analysis indicated that shoulder and lap belt use consistently reduced crash severity across all data sets. Multi-vehicle crashes, sideswipes, angles, and rear-end crashes were among the variables that had an increasing influence on crash severity across the three data sets. The partial dependence plot revealed that the mobile work zone type significantly influenced worker-present crash severity, while out-of-state drivers were a significant factor in non-worker-present crashes. The findings of this study are intended to guide transportation practitioners and policymakers in enhancing work zone safety.