Effectiveness of resampling methods in coping with imbalanced crash data: Crash type analysis and predictive modeling

This study focuses on comparing the effects of resampling techniques on the performance of both machine learning and classical statistical models for classifying and predicting different crash types on freeways. Specifically, a mixed sampling approach featuring a cluster-based under-sampling coupled with three popular over-sampling methods (i.e., random over-sampling, synthetic minority over-sampling, and adaptive synthetic sampling) were investigated with respect to four crash classification models, including three ensemble machine learning models (CatBoost, XGBoost, and Random Forests) and one classic statistical model (Nested Logit). This study concluded that all three resampling methods consistently enhanced the performance of all models. Among the three over-sampling methods, the adaptive synthetic sampling approach performed best and tremendously improved the prediction of minority crash types without impeding the prediction of the majority crash type. This is likely due to the density-based approach of adaptive synthetic sampling in creating synthetic instances that are more congruent with the underlying manifold structure embodied in the high-dimensional feature space.PMID:34144225 | DOI:10.1016/j.aap.2021.106240

Source: Accident; Analysis and Prevention. - June 18, 2021 Category: Accident Prevention Authors: Clint Morris Jidong J Yang Source Type: research