Extraction and Analysis of Risk Factors from Chinese Railway Accident Reports

Abstract

Learning and getting more information from past accident records to understand the accidents deeply are important to prevent future accidents. Most Chinese railway accidents are recorded in the form of text reports and the information about text reports is often underutilized due to the lack of effective mining and analysis tools. In this study, text mining and natural language process (NLP) techniques were used to analyze railway accident reports. More specifically, the multichannel convolutional neural network (M-CNN) and conditional random field (CRF) model were designed to extract accident risk factors. The experimental results shows that our system achieves good performance and can effectively extract risk factors from the accident reports. At the same time, the main risk factors leading to accidents are summarized from four aspects. The system can be used to solve problem areas and strengthen the safety management of the railway industry.