Efficient Fusion Decision System for Predicting Road Crash Events: A Comparative Simulator Study for Imbalance Class Handling

Abstract

Road crash events are a fact of life. Although significant progress have been made in adopting machine learning techniques for analyzing road crashes, there has been limited emphasis on evaluating crash events within data fusion systems. The primary purpose of this study is to outline and validate a comparative safety analysis of an ensemble fusion system founded on the use of different base classifiers and a meta-classifier to procure more efficient crash prediction. Three categories of features namely vehicle-telemetry, driver-inputs and environmental-conditions have been collected using a driving-simulator in order to identify the crash strongest precursors through feature extraction technique. Furthermore, optimized strategies using AdaBoost, XGBoost, RF, GBM, LightGBM, CatBoost and KNN techniques were implemented to establish effective predictions within a fusion-based system. To ensure that the proposed system provide superior decisions given the infrequent nature of crash events, an imbalance-learning approach was conducted based on three resampling strategies: over-sampling, under-sampling and SMOTE-Tomek-Links. The findings depict that the superior performance has been attained when adopting LightGBM, CatBoost and KNN as base classifiers along with SMOTE-TL as balancing technique and XGBoost as meta-classifier with 89.19% precision, 96.77% recall and 92.83% f1-score. To our knowledge, there has been a limited interest, if not at all, at endorsing a fusion-based system examining the impact of real-time features' combinations on the prediction of road crashes while providing a critical analysis of class-imbalance. Overall, the findings emphasized the relevance of the explanatory features and can be endorsed in designing efficient intelligent transportation systems.