Benchmarking on railway safety performance using Bayesian inference, decision tree and petri-net techniques based on long-term accidental data sets

Abstract

Not only has the railway accidental prevention been a prime focus, but it has also become a key challenge for the industry in recent years. For many decades, rail authorities have attempted to significantly improve rail safety, whilst facing various passengers’ risks and uncertainties. The overarching goal of this study is to develop a new posterior probability model to quantify uncertainties for benchmarking. This is the world's first to establish new insights from the benchmarking of risk and safety across different rail networks. The insights will point out the advantages and practicability of launching safety policies and reducing railway accidents for other rail networks. The new model has been developed using unparalleled long-term accidental data sets, including ‘a trailer an accident’ and ‘causes of the accident’. The investigation adopts a Bayesian approach (via Python) to codify the novel model. The new findings lead to the better understanding into the uncertainty of railway accidents. Five notable rail networks have been selected as case studies. This study has also compared the effectiveness of the decision tree and Petri-net models using the posterior probability and number of injuries and fatalities. Based on the benchmarking outcomes, Chinese and Japanese railway systems denote the lowest risk over other networks, followed by Spanish, French and South Korean rail networks. The study also demonstrates that the novel benchmarking criteria can effectively measure and compare any rail networks’ risk and uncertainties. Its adoption will lead to performance improvement in terms of safety, reliability and maintenance policies of railway networks globally.