Novel Insights for Railroad Maintenance Using Big Data Analytics

Abstract

Train accidents can be attributed to human factors, equipment factors, track factors, signaling factors, and Miscellaneous factors. Not only have these accidents caused damages to railroad infrastructure and train equipment leading to excessive maintenance and repair costs, but some of these have also resulted in injuries and loss of lives. Big Data Analytics techniques can be utilized to provide insights into possible accident causes, thus resulting in improving railroad safety and reducing overall maintenance expenses as well as spotting trends and areas of operational improvements. We propose a comprehensive Big Data approach that provides novel insights into the causes of train accidents and find patterns that led to their occurrence. The approach utilizes a combination of Big Data algorithms to analyze a wide variety of data sources available to the railroads, and is being demonstrated using the FRA train accidents/incidents database to identify factors that highly contribute to accidents occurring over the past years. The most important contributing factors are then analyzed by means of association mining analysis to find relationships between the cause of accidents and other input variables. Applying our analysis approach to FRA accident report datasets we found that railroad accidents are correlating strongly with the track type, train type, and train area of operation. We utilize the proposed approach to identify patterns that would lead to occurrence of train accidents. The results obtained using the proposed algorithm are compatible with the ones obtained from manual descriptive analysis techniques.