Severity modeling of work zone crashes in New Jersey using machine learning models

Ahmed Sajid Hasan, Md Asif Bin Kabir, Mohammad Jalayer, Subasish Das

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


In the United States, the probability of work zone crashes has increased due to an increase in renovation works by transportation infrastructures. The severity of work zone crashes is associated with multiple contributing factors such as the roadway’s geometric design features, temporal variables, environmental conditions, types of vehicles, and driver behaviors. For this study, we acquired and analyzed three years (2016–2018) of work zone crash data from the state of New Jersey. We investigated the performance of several machine learning methods, including Support Vector Machine, Random Forest, Catboost, Light GBM, and XGBoost to predict the type of injury severity resulting from work zone crashes. To evaluate models’ performances, some statistical evaluation parameters such as accuracy, precision, and recall scores were calculated. In addition, a sensitivity analysis was conducted to assess the impact of the most influential factors in work zone-related crashes. Random Forest and Catboost outperformed the other models in terms of predicting fatal, major, and minor injuries. According to the sensitivity analysis, crash type and speed limit were the most significantly associated variables with crash severity. The findings of this study are expected to facilitate the identification of appropriate countermeasures for reducing the severity of work zone crashes.

Original languageEnglish (US)
Pages (from-to)604-635
Number of pages32
JournalJournal of Transportation Safety and Security
Issue number6
StatePublished - 2023
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Transportation
  • Safety Research


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