Freeway crashes represent a significant and persistent threat to road safety, resulting in both loss of life and extensive property damage. Effectively addressing this critical issue requires a comprehensive understanding of the factors contributing to these incidents and the ability to accurately predict crash severity under different traffic conditions. This study aims to improve the accuracy of crash classification by incorporating key traffic-related variables such as braking, weather conditions, and speed. To validate the effectiveness of proposed model, we utilize real-world crash data from Flint, Michigan. To achieve the objective, we employ an innovative Boosting Ensemble Learning approach, leveraging five advanced ensemble learning models: Gradient Boosting, Cat Boost, XGBoost, LightGBM, and SGD. Through the application of hyperparameter optimization techniques, we further enhance the performance of these models, improving their predictive capabilities. Our evaluation results demonstrated the effectiveness of our approach, with Gradient Boosting algorithms achieving an accuracy rate of up to 96% in crash classification. This research provides valuable insights into the potential of using Boosting Ensemble Learning as a tool for accurately and efficiently classifying freeway crashes across a spectrum of traffic conditions. Additionally, it sheds light on the nuanced variations in crash mechanisms observed when employing diverse ensemble learning models. The findings of this study underscore the significance of hyperparameter optimization as a critical factor in elevating the predictive precision of freeway crashes.

A multiuser driving simulator is used in this study to explore the relationship between road and weather conditions, vehicle speed, and crash potential due to driver’s behavior. In this study, ten driving simulator scenarios were conducted. A total of 10 participants were recruited from different age groups to use the driving simulator. This study aims toward finding the relationship between the change in speed limit in certain weather condition and the number of crashes that are likely to occur as a sequence to this change. The data obtained from the driving simulator was used to estimate the crash modification factors (CMFs). The expected CMF gives the notion on whether a treatment or a change in speed limit in a certain weather condition will lead to change in the number of crashes and consequently a change in the safety level. The resultant CMFs from this study are considered a new dimension that will increase road safety and reduce the rate of road traffic crashes.