Machine learning based prediction of rail transit signal failure: A case study in the United States

Abstract

Signals are an important part of the urban rail transit system. Signals being in functioning condition is key to rail transit safety. Predicting rail transit signal failures ahead of time has significant benefits with regard to operating safety and efficiency. This paper proposes a machine learning method for predicting urban rail transit signal failures 1 month in advance, based on records of past failures and maintenance events. Because signal failure is a relatively rare event, imbalanced data mining techniques are used to address its prediction. A case study based on data provided by a major rail transit agency in the United States is developed to illustrate the application of the proposed machine learning method. The results show that our model can be used to identify approximately one-third of signal failures 1 month ahead of time by focusing on 10% of locations on the network. This method can be used by rail transit agencies as a risk screening and ranking tool to identify high-risk hot spots for prioritized inspection and maintenance, given limited resources.