Research on key risk chain mining method for urban rail transit operations: A new approach to risk management

Abstract

To ensure the safety of urban rail transit operations and uncover the transmission dynamics of risk sources, a key risk chain mining method for urban rail transit operation is proposed. Firstly, the H-Apriori association rule algorithm is proposed for the characteristics of low frequency but high riskiness of high hazard degree risk sources in urban rail transit operation, which adds a new hazard degree evaluation index to the traditional Apriori algorithm and couples with support degree two-dimensionally to mine the strong association rules among risk sources. Secondly, we construct a weighted risk network with risk sources as network nodes and strong association rules as network edges, and propose a key risk chain mining method for urban rail transit operation based on path search theory to mine key risk chains from the weighted risk network. Finally, using the actual urban rail transit operation data of a city in China as an example, a total of 17 key risk chains are mined, and then 5 key risk sources and 8 key chain break locations are obtained by riskiness and frequency analysis of key risk chains, and control plans are proposed. The research outcomes introduce a novel approach to mining risk chains in urban rail transit operations, shedding light on the propagation mechanisms, triggering probabilities, and degrees of unsafety associated with risk sources. The results not only provide theoretical support but also offer methodological guidance for pinpointing locations of risk chain breaks and refining the control of risk sources.

Keywords

• Urban rail transit
• Risk chain
• H-Apriori association rule algorithm
• Key risk source
• Risk control plan