Deep Learning, Machine Learning, or Statistical Models for Weather-related Crash Severity Prediction

Nearly 5,000 people are killed and more than 418,000 are injured in weather-related traffic incidents each year. Assessments of the effectiveness of statistical models applied to crash severity prediction compared to machine learning (ML) and deep learning techniques (DL) help researchers and practitioners know what models are most effective under specific conditions. Given the class imbalance in crash data, the synthetic minority over-sampling technique for nominal (SMOTE-N) data was employed to generate synthetic samples for the minority class. The ordered logit model (OLM) and the ordered probit model (OPM) were evaluated as statistical models, while random forest (RF) and XGBoost were evaluated as ML models. For DL, multi-layer perceptron (MLP) and TabNet were evaluated. The performance of these models varied across severity levels, with property damage only (PDO) predictions performing the best and severe injury predictions performing the worst. The TabNet model performed best in predicting severe injury and PDO crashes, while RF was the most effective in predicting moderate injury crashes. However, all models struggled with severe injury classification, indicating the potential need for model refinement and exploration of other techniques. Hence, the choice of model depends on the specific application and the relative costs of false negatives and false positives. This conclusion underscores the need for further research in this area to improve the prediction accuracy of severe and moderate injury incidents, ultimately improving available data that can be used to increase road safety

Data-driven exploration of traffic speed patterns to identify potential road links for variable speed limit sign implementation

ABSTRACTThe focus of this study is to identify potential road links suitable for implementing a variable speed limit (VSL) sign by analyzing real-world traffic speed data collected over one year in Charlotte, North Carolina, United States. Exploratory and bivariate analyses were conducted to examine variations in traffic speed patterns during weekdays and weekends across eight specific timespans. The results revealed that road links with lower posted speed limits consistently experienced traffic speeds exceeding the posted speed limits. The mean traffic speeds are generally close to the posted speed limits for road links with higher posted speed limits while the 85th percentile speeds exceeded the posted speed limits, indicating a potential need for VSL sign implementation. The road links with 40 mph or 50 mph posted speed limits have a unique pattern compared to road links of other posted speed limit clusters. The mean traffic speed on these road links decreased as the standard deviation increased. The findings contribute to an improved understanding of traffic speed patterns and provide valuable insights for implementing a VSL sign