Road fatalities pose significant public safety and health challenges worldwide, with pedestrians being particularly vulnerable in vehicle-pedestrian crashes due to disparities in physical and performance characteristics. This study employs explainable artificial intelligence (XAI) to identify key factors contributing to pedestrian fatalities across the five U.S. states with the highest crash rates (2018-2022). It compares them to the five states with the lowest fatality rates. Using data from the Fatality Analysis Reporting System (FARS), the study applies machine learning techniques-including Decision Trees, Gradient Boosting Trees, Random Forests, and XGBoost-to predict contributing factors to pedestrian fatalities. To address data imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) is utilized, while SHapley Additive Explanations (SHAP) values enhance model interpretability. The results indicate that age, alcohol and drug use, location, and environmental conditions are significant predictors of pedestrian fatalities. The XGBoost model outperformed others, achieving a balanced accuracy of 98 %, accuracy of 90 %, precision of 92 %, recall of 90 %, and an F1 score of 91 %. Findings reveal that pedestrian fatalities are more common in mid-block locations and areas with poor visibility, with older adults and substance-impaired individuals at higher risk. These insights can inform policymakers and urban planners in implementing targeted safety measures, such as improved lighting, enhanced pedestrian infrastructure, and stricter traffic law enforcement, to reduce fatalities and improve public safety.

Analyzing Factors Influencing Driver Willingness to Accept Advanced Driver Assistance Systems Hannah Musau a,, Nana Kankam Gyimah a, Judith Mwakalonge a, Gurcan Comert b, Saidi Siuhi a a Department of Engineering, South Carolina State University, Orangeburg, South Carolina, USA, 29117 b Department of Computational Engineering and Data Science, North Carolina A&T State University, Greensboro, North Carolina, US, 27411Abstract Advanced Driver Assistance Systems (ADAS) enhance highway safety by improving environmental perception and reducing human errors. However, misconceptions, trust issues, and knowledge gaps hinder widespread adoption. This study examines driver perceptions, knowledge sources, and usage patterns of ADAS in passenger vehicles. A nationwide survey collected data from a diverse sample of U.S. drivers. Machine learning models predicted ADAS adoption, with SHAP (SHapley Additive Explanations) identifying key influencing factors. Findings indicate that higher trust levels correlate with increased ADAS usage, while concerns about reliability remain a barrier. Findings emphasize the influence of socioeconomic, demographic, and behavioral factors on ADAS adoption, offering guidance for automakers, policymakers, and safety advocates to improve awareness, trust, and usability. Introduction Human factors are the leading cause of road crashes, contributing to over 90% of incidents either alone or alongside failures in vehicles or infrastructure [1].

This study investigates crash severity risk modeling strategies for work zones involving large vehicles (i.e., trucks, buses, and vans) when there are crash data imbalance between low-severity (LS) and high-severity (HS) crashes. We utilized crash data, involving large vehicles in South Carolina work zones for the period between 2014 and 2018, which included 4 times more LS crashes compared to HS crashes. The objective of this study is to explore crash severity prediction performance of various models under different feature selection and data balancing techniques. The findings of this study highlight a disparity between LS and HS predictions, with less-accurate prediction of HS crashes compared to LS crashes due to class imbalance and feature overlaps between LS and HS crashes. Combining features from multiple feature selection techniques: statistical correlation, feature importance, recursive elimination, statistical tests, and mutual information, slightly improves HS crash prediction performance. Data balancing techniques such as NearMiss-1 and RandomUnderSampler, maximize HS recall when paired with certain prediction models, such as Bayesian Mixed Logit (BML), NeuralNet, and RandomForest, making them suitable for HS crash prediction. Conversely, RandomOverSampler, HS Class Weighting, and Kernel-based Synthetic Minority Oversampling (K-SMOTE), used with certain prediction models such as BML, CatBoost, and LightGBM, achieve a balanced performance, defined as achieving an equitable trade-off between LS and HS prediction performance metrics. These insights provide safety analysts with guidance to select models, feature selection techniques, and data balancing techniques that align with their specific safety objectives, offering a robust foundation for enhancing work-zone crash severity prediction.

In this paper, we present an automated machine learning (AutoML) approach for network intrusion detection, leveraging a stacked ensemble model developed using the MLJAR AutoML framework. Our methodology combines multiple machine learning algorithms, including LightGBM, CatBoost, and XGBoost, to enhance detection accuracy and robustness. By automating model selection, feature engineering, and hyperparameter tuning, our approach reduces the manual overhead typically associated with traditional machine learning methods. Extensive experimentation on the NSL-KDD dataset demonstrates that the stacked ensemble model outperforms individual models, achieving high accuracy and minimizing false positives. Our findings underscore the benefits of using AutoML for network intrusion detection, as the AutoML-driven stacked ensemble achieved the highest performance with 90\% accuracy and an 89\% F1 score, outperforming individual models like Random Forest (78\% accuracy, 78\% F1 score), XGBoost and CatBoost (both 80\% accuracy, 80\% F1 score), and LightGBM (78\% accuracy, 78\% F1 score), providing a more adaptable and efficient solution for network security applications.