Quantification analysis of potential risk in railway accidents: A new random walk based approach

Safety and efficiency are the fundamental goals to improve the transportation capacity and service level of railway system. Moreover, the accurate description and in-depth exploration of potential risks are essential in reducing the railway accidents. However, there have been limited publications that focus on quantitatively analyzing the potential risk of railway accidents by using the random walk method. In this paper, a new random walk method named Comprehensive-Biased Random Walk with Different Restart (CBDRWR) is proposed in analyzing the potential risk of railway accident generation process. Combined with the established accident causation network, we give each node a different restart probability and comprehensively improve the biased transition probabilities. According to the solution algorithm of CBDRWR equation, we propose four evaluation indexes for quantifying and analyzing the potential risk of accident occurrence. In the case study, the proposed method is used to analyze the Federal Railroad Administration (FRA) dataset. The experimental results verify that the proposed method can effectively quantify the potential risk and quickly locate the key risk sources. Resultantly, this research can provide an accurate, scientific and reasonable basis for railway accident prevention and rescue.

Automated summary

This paper proposes a new random walk method, CBDRWR, to analyze the potential risk of railway accidents. By combining accident causation network, we assign different restart probabilities to each node and improve the transition probabilities. In the case study, the proposed method effectively quantifies the potential risk and identifies key risk sources.