Making the Invisible Visible: An objective measure of cognitive workload for signallers

Currently, the UK has tools to measure a signaller’s physical workload objectively and to measure their cognitive workload subjectively. However, these workload measures can give conflicting information i.e. an assessment... [ view full abstract ]

Currently, the UK has tools to measure a signaller’s physical workload objectively and to measure their cognitive workload subjectively. However, these workload measures can give conflicting information i.e. an assessment will indicate a low observed physical workload but the signaller will report a high cognitive workload. To overcome this, a tool using objective service pattern data has been created to gain a clearer understanding of signallers’ cognitive workload demands.

As part of the Thameslink Programme a significant timetable change had a major effect on the service patterns on panel 3 in the Victoria Area Signalling Centre (ASC) but with little difference in the number of trains. Traditional tools indicated that the physical workload would be manageable but signallers were concerned that the nature of the routes and the timing of the trains moving through their area of control would provide an additional level of complexity and require sustained attention throughout their shift.

The new timetable was reviewed to identify the characteristics of the train movements through the signaller’s area of control that would generate cognitive demands. Train movements were defined as two or more trains that are following or converging / crossing at a regulating location within a set time period which will require the signallers’ attention. This approach provides a temporal consideration of the train service for which the signaller is responsible.

The train movements within a one hour from the new timetable were analysed and sorted into the following categories:

 The type of train movement i.e. following trains or converging / crossing trains at a regulating location.
 The regulating locations where the train movements occur.
 The distribution of the train movements within a set time period.

The category assignment indicates the complexity of the train movement. An example of a train service pattern with a low complexity would consist of only following train movements, at the same regulating location, evenly distributed across a specified time period. An example of a high complexity train service pattern would consist of crossing or converging train movements, at different regulating locations, unevenly distributed across a specified time period.

A numerical value was provided for each train movement according to the category assignment which reflected the cognitive complexity that it would generate for the signaller.

When comparing the old and new timetable on the panel 3 at Victoria ASC, the physical workload tool predicted a 28% increase which reflected only the additional routing actions for the 3 additional trains. The new cognitive workload tool predicted a 353% increase in the complexity of the signallers’ task due to the significant change in service pattern routes.

Although the signallers had subjectively reported that the new timetable would have a significant impact on their workload, they had lacked any means to demonstrate their concerns. The objective complexity analysis method, based on the timetable data, enabled the signallers’ invisible concerns about cognitive workload to become visible. A dedicated assist turn was provided for panel 3 which has enabled manageable workload levels to be maintained.