Measuring Workload Weak Resilience Signals at a Rail Control Post

Abstract

OCCUPATIONAL APPLICATIONS This article describes an observational study at a rail control post to measure workload weak resilience signals. A weak resilience signal indicates a possible degradation of a system's resilience, which is defined as the ability of a complex socio-technical system to cope with unexpected and unforeseen disruptions. A method based upon a weak resilience signal framework introduces a new metric, stretch, to measure the signals. Stretch is a subjective or an objective reaction of the system to an external cluster event and is an operationalization of variables in an earlier stress–strain model. The stretch ratio between the subjective and objective stretch are used to identify workload weak resilience signals. Weak resilience signals identified during real-time operation revealed obstacles that influence the resilience state and enabled actions to anticipate and mitigate changes to maintain the resilience of the system. TECHNICAL ABSTRACT Background: Continuous performance improvement of a complex socio-technical system may result in a reduced ability to cope with unexpected and unforeseen disruptions. As with technical and biological systems, these socio-technical systems may become “robust, yet fragile.” Resilience engineering examines the ability of a socio-technical system to reorganize and adapt to the unexpected and unforeseen. However, the resilience doctrine is not yet sufficiently well developed for designing and achieving those goals, and metrics are needed to identify resilience change. Purpose: A new approach was explored to identify changes in the resilience of a rail system around the workload boundary to anticipate those changes during normal operations and hence improve the ability to cope with unexpected and unforeseen disruptions. Methods: A weak resilience signal framework was developed with a resilience-state model for a railway system, resulting in a generic, quantifiable, weak resilience signal model. Two workload measurements (i.e., external cognitive task load and integrated workload scale) were combined into a new metric called stretch. Heart rate variability was used for correlation and validation. An observational study was used to measure workload weak resilience signal through workload quantification at an operational rail control post. Results: A theoretical resilience-state model for a railway system was developed and used to generate a generic quantifiable weak resilience signal model, forming a weak resilience signal framework that is the basis for a method to measure workload weak resilience signal through a new metric called stretch with three variations: objective stretch, subjective stretch, and stretch ratio. A component of the subjective stretch is the integrated workload scale, for which a real-time tool was developed for measuring and monitoring. Workload weak resilience signals identified at a rail control post triggered analysis to reveal anticipated obstacles. Conclusions: A resilience-state model of a rail system can be used to quantify workload weak resilience signals. Stretch ratio differences represent changes of the workload state used to measure workload weak resilience signals that aid in revealing obstacles jeopardizing the resilience state.