Citation

Kaeeni S, Khalilian M, Mohammadzadeh J. Safety Sci. 2018; 110B: 3-10.

Copyright

(Copyright © 2018, Elsevier Publishing)

DOI

10.1016/j.ssci.2017.11.006

PMID

unavailable

Abstract

Safety plays important roles in railway transportation industry. Plan and development of safety system requires sufficient awareness on specific situations which creates unsafe conditions in railway network. Derailment accident is known as one of the most critical train accident. It is necessary that safety officials of this industry by taking advantage of the experiences of the past accidents prevent repeating it in the future. Using up-to-date tools and techniques can create different view from what has been presented by railway safety official. In this study, a derailment accident risk assessment classification model has been proposed, which may be used for safety system in railway network. Our model uses the cumulated data on the Iranian Railway accidents database. Three popular data mining techniques are used to our proposed model in two steps. In the first step, Artificial Neural Networks, Naïve Bays, and Decision Tree are utilized independently to predict the derailment accident risk, and each method produces the model of their prediction as a form of probabilities. In the second step, outcome for each model receives a weight based on its predicting accuracy by using genetic algorithm (GA), and makes the final decision for derailment accident risk assessment. To validate model efficiency, it was used for a sample in the Islamic republic of Iran Railway. In the end, it's shown this model presented high-quality information for predicting accident and GA (Genetic Algorithm) in second step has a significant role in performance improvements.

Language: en

Keywords

Data mining; Ensemble classification; Safety risk assessment