Railway accident prediction strategy based on ensemble learning. (October 2022)

• Record Type: Journal Article
• Authors: Meng, Haining; Tong, Xinyu; Zheng, Yi; Xie, Guo; Ji, Wenjiang; Hei, Xinhong
• Is Part Of: Accident analysis and prevention. Volume 176(2022)
• Publication Date: 2022-10
• Subjects: Accident prediction -- Ensemble learning -- Accident prevention -- Data imputation -- AdaBoost -- Bagging; Accidents -- Prevention -- Periodicals; Accident Prevention -- Periodicals; Accidents -- Prévention -- Périodiques; 363.106
• DOI: 10.1016/j.aap.2022.106817
• Languages: English
• ISSNs: 0001-4575

Abstract

Highlights: Railway accident prediction helps safety agencies formulate railway risk management strategies. Ensemble learning is employed for railway accident prediction without knowing the accident mechanism. An improved KNN data imputation algorithm is developed for solving the problem of missing data. The issue of imbalance dataset is resolved using AdaBoost-Bagging method. Feature ranking is conducted for accident cause analysis. Abstract: Railway accident prediction is of great significance for establishing an early warning mechanism and preventing the occurrences of accidents. Safety agencies rely on prediction models to design railroad risk management strategies. Based on historical railway accident data, an ensemble learning strategy for accident prediction is proposed. Firstly, an improved K-nearest neighbors (KNN) data imputation algorithm is proposed to solve the problem of missing data in the dataset. Then, to reduce the impact of imbalanced data on prediction performance, an AdaBoost-Bagging method is presented. Finally, according to the feature importance in the prediction model, accident features are ranked to identify new insights into the cause of the accident. The AdaBoost-Bagging prediction method is applied to the Federal Railroad Administration (FRA) dataset. The application results show that, compared with Artificial Neural Network (ANN), XGBoost, GBDT, Stacking and AdaBoost methods, AdaBoost-Bagging method has a smaller prediction error and faster inference time in predicting railway accidents. Accuracy, Precision, Recall and F1–score are 0.879, 0.879, 0.883 and 0.881 respectively, and the inference time is reduced by 23.38%, 12.15%, 6.66%, 3.17% and 11.41% respectively. The prediction method can well mine important features of railway accidents without knowing the accident mechanism or the relationship between various railway accidents and factors, e.g., the critic risk factors related to derailment and collision accidents are investigated in the prediction. The findings will be helpful to the prevention and management of railway accidents.