Identification of Significant Factors Contributing to Multi-attribute Railway Accidents Dataset (MARA-D) Using SOM Data Mining

Abstract

Although a lot of labor and financial forces have been put into safety work, railway accidents continue to be the major concern in China. The aim of this study is to identify the significant factors contributing to railway accidents and enable stakeholders to fully learn from accidents. The Cognitive Reliability and Error Analysis Method - Railway Accidents (CREAM-RAs) taxonomy framework was proposed to classify human, technology, and organization factors in railway accidents. To establish a Multi-attribute Railway Accidents Dataset (MARA-D), 392 railway accident reports were collected and collated under the CREAM-RAs framework. The data mining technique (Self-Organizing Maps – SOM) was adopted to convert MARA-D into 2-dimensional maps. The key accident causes were dug out and risk information was transmitted to various related railway departments. Thus, the relevant measures were raised to improve safety and promote health management of railways.