Big Data in Small Tunnels : Turning Alarms Into Intelligence

Abstract: In this thesis we examine methods for evaluating a traffic alarm system. Nuisance alarms can quickly increase the volume of alarms experienced by the alarm operator and obstruct their work. We propose two methods for removing a number of these nuisance alarms, so that events of higher priority can be targeted. A parallel correlation analysis demonstrated significant correlation between single and clusters of alarms, presenting a strong cause for causality. While a serial correlation was performed, it could not conclude evidence of consequential alarms. In order to assist Trafikverket with maintenance scheduling, a long short-term model (LSTM) model, to predict univariate time-series of discretely binned alarm sequences. Experiments conclude that the LSTM model provides higher precision for alarm sequences with higher repeatability and recurring patterns. For other, randomly occurring alarms, the model performs unsatisfactory.