TY - JOUR
T1 - Severity modeling of work zone crashes in New Jersey using machine learning models
AU - Hasan, Ahmed Sajid
AU - Kabir, Md Asif Bin
AU - Jalayer, Mohammad
AU - Das, Subasish
N1 - Publisher Copyright:
© 2022 Taylor & Francis Group, LLC and The University of Tennessee.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
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U2 - 10.1080/19439962.2022.2098442
DO - 10.1080/19439962.2022.2098442
M3 - Article
AN - SCOPUS:85134194187
SN - 1943-9962
VL - 15
SP - 604
EP - 635
JO - Journal of Transportation Safety and Security
JF - Journal of Transportation Safety and Security
IS - 6
ER -