| | Study | Conflict type | Interaction type | Methods | Identifying near-crashes | Location | Independent variable | Evaluation metric |
| | Formosa et al. [50] | Rear-end, lane changing | Vehicles-vehicles | Deep learning (DP) | Time headway, lateral acceleration, lateral distance, spacing, speed, etc. | Roadway segment | 26 variables including SSMs | Classification: accuracy, AUC, precision, recall, and false alarm ratio | | Osman et al. [51] | Rear-end | Vehicles-vehicles | KNN, random forest, SVM, decision trees, Gaussian NB, and AdaBoost | Defined in the SHRP2 NDS database | Roadway segment | Std of kinematic data | Classification: accuracy, recall, precision, and F1 | | Essa and Sayed [52] | Rear-end | Vehicles-vehicles | The safety performance functions (SPFs) were developed using the full Bayesian approach | TTC, MTTC, and DRCA | Signalized intersections | Traffic volume, shock wave area, maximum queue length, backward-moving shock wave speed, platoon ratio | Regression: goodness of fit, the scaled deviance (SD), and the Pearson chi-squared (χ2) | | Ma et al. [53] | Rear-end, lane changing | Vehicles-vehicles | Multivariate linear regression | TTC | Expressway diverging areas and ramps | Traffic volume, speed, etc. | Regression: goodness of fit |
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