Identification of high-risk roadway segments for wrong-way driving crash using rare event modeling and data augmentation techniques

•WWD crashes are relatively rare but more likely to produce fatalities and severe injuries than other types of crashes.•To solve the imbalanced dataset problem data augmentation and rare event modeling techniques were applied.•A hybrid data augmentation process was adopted in this study using SMOTE, ADASYN, Tomek links, and ENN.•An Autoencoder-based rare-event modeling technique was also used to overcome the imbalanced dataset problem.•Successful identification of WWD segments can facilitate the deployment of low-level and medium-level countermeasures. Wrong-Way Driving (WWD) crashes are relatively rare but more likely to produce fatalities and severe injuries than other crashes. WWD crash segment prediction task is challenging due to its rare nature, and very few roadway segments experience WWD events. WWD crashes involve complex interactions among roadway geometry, vehicle, environment, and drivers, and the effect of these complex interactions is not always observable and measurable. This study applied two advanced Machine Learning (ML) models to overcome the imbalanced dataset problem and identified local and global factors contributing to WWD crash segments. Five years (2015–2019) of WWD crash data from Florida state were used in this study for WWD model development. The first modeling approach applied four different hybrid data augmentation techniques to the training dataset before applying the XGBoost classification algorithm. In the second model, a rare event modeling approach using the Autoencoder-based anomaly detection method was applied to the original data to identify WWD roadway segments. A third model was applied based on the statistical method to compare the performance of ML models in predicting the WWD segments. The performance comparison of the adopted models showed that the XGBoost model with the Adaptive Synthetic Sampling (ADASYN) method performed best in terms of precision and recall values compared to the autoencoder-based anomaly detection method. The best-performing model was used for the feature analysis with an interpretable machine-learning technique. The SHapley Additive exPlanations (SHAP) values showed that high-intensity developed land use, length of roadway, log of Annual Average Daily traffic (AADT), and lane width were positively associated with WWD roadway segments. The results of this study can be used to deploy WWD countermeasures effectively.