Analysis of Subway Fault Data Based on Improved Apriori Algorithm

Abstract

The subway is a high-capacity, fast and punctual urban rail transit that usually operates underground or on elevated tracks. It has the characteristics of large capacity, fast speed, high punctuality, good safety, environmental protection and energy conservation. However, some malfunctions may occur during the operation process. However, there is currently no good solution to the problem of undefined subway fault data and diverse fault types. This article improves the Apriori algorithm and compares it with the FP Growth algorithm. Python is used to simulate fault scenarios, and the association rules between fault data are obtained through the improved algorithm. Through performance comparison, it can be concluded that the improved Apriori algorithm can provide a good reference for subway fault analysis.