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

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, the authors acquired and analyzed three years (2016–2018) of work zone crash data from the state of New Jersey. The authors 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.

• Record URL:
  • https://doi.org/10.1080/19439962.2022.2098442
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/19439962
• Supplemental Notes:
  • © 2022 Taylor & Francis Group, LLC and The University of Tennessee 2022. Abstract reprinted with permission of Taylor & Francis.
• Authors:
  • Hasan, Ahmed Sajid
  • Kabir, Md Asif Bin
  • Jalayer, Mohammad
  • Das, Subasish
• Publication Date: 2023-6

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 604-635
• Serial:
  • Journal of Transportation Safety & Security
  • Volume: 15
  • Issue Number: 6
  • Publisher: Taylor & Francis
  • ISSN: 1943-9962
  • EISSN: 1943-9970
  • Serial URL: http://www.tandfonline.com/loi/utss20#.U2qTJPldWCm

Subject/Index Terms

• TRT Terms: Computer models; Crash severity; Injury severity; Machine learning; Work zone safety
• Geographic Terms: New Jersey
• Subject Areas: Construction; Highways; Safety and Human Factors;

Filing Info

• Accession Number: 01885789
• Record Type: Publication
• Files: TRIS
• Created Date: Jun 22 2023 10:10AM