Looking at Precursors to Anticipate SPAD Risk

Companies are increasingly looking at their operations to assess their resilience and identify their vulnerabilities. Rather than seeing SPADs in isolation, with unrelated causes, companies are looking across their incidents, network and people to understand the precursors to SPAD risk. This article provides some examples of work done, and guidance to help your work in this field.

Change in the industry and SPAD peaks

Sometimes operational reviews are triggered by a peak in SPAD events. For example, Period 3 in the spring of 2018 saw a significant increase in the number of SPADs compared to previous years. A review of 35 of these SPAD investigations was carried out using the 10-incident factor framework. This identified that several changes across the industry coincided and contributed to this peak. These included: re-signalling schemes, issues with vegetation management and timetable changes. 

Incident history and SPAD risk 

The need to look at a driver’s incident history was underlined by a RAIB investigation following an 8 mph collision between a passenger and an empty coaching stock train. The report identified that ‘the driver had a previous operational history indicative that he was prone to lapses in concentration, and that this had not been identified by [the] competence management system’. 

Research looking at the relationship between station-stopping incidents and SPADs at two large train operating companies (TOCs) identified that drivers’ station-stopping incident rates could predict the likelihood of them being involved in a SPAD. This analysis took account of driver experience, by calculating incidents rate per month, and using that statistic for correlation. There were strong similarities in the causes for both SPADs and station stopping incidents—braking late, or not at all. These were mostly caused by distractions, forgetting, or incorrectly anticipating a signalling sequence or stopping pattern (these are slip/lapse errors). By focussing efforts on managing the error type, improvements should follow. However, the study did not consider other factors such as how many stop aspects, or timetabled stops drivers were exposed to—clearly this may affect the correlations reported.

Use of on-train monitoring recorders data 

A study looking at on-Train Monitoring Recorder (OTMR) data provided valuable insight into driver actions across four areas: train handling, compliance with rules, vigilance and efficiency. The approach has the potential to look at driving styles and spot drivers who might be at risk before incidents happen.   

Exposure to red aspects

The Red Aspect Approaches to Signals (RAATS) tool was developed to build a better understanding of SPAD risk by looking at the number of times a signal is approached at red. This data helps to understand the likelihood of a SPAD and for normalisation of SPAD data. This tool is relatively new, and experience is being collected on how it can be used.

Consider the following when looking at precursors in your operations:

  • Are you looking at a real precursor, or something that looks like one?  Time of day is a good example of a poor SPAD risk precursor. Knowing that SPAD spikes peaks in the mornings and evenings is probably just a reflection of the larger number of train services running, and therefore the greater chance of SPADs happening.
  • It’s often necessary to normalise a precursor. For example, to identify whether inexperienced drivers are having more SPADs than more experienced drivers, you need to account for differences in the amount of driving. Without doing so the data may show that more experienced drivers have more incidents, but this would simply be due to the fact that they have been driving for longer. One way to normalise the data is to calculate a SPAD rate, such as the number of incidents divided by number of shifts that they have driven in a fixed period, such as two years. Then you can start to make comparisons. If you don’t normalise precursors, your conclusions might be misleading.
  • Think about the underlying causes to your precursors. Continuing the example about driver experience, let’s assume that inexperienced drivers are having comparatively more SPADs, why is this?  Is there something wrong in their initial training, the amount of support they have, the focus of assessments, and what can be learnt from their more experienced colleagues?
  • The different challenges across your network. For example, where are your platform end signals and Start Against Signal SPAD risks? Are you working with affected depots to reinforce correct use of DRA?
  • Consider a range of explanations for any patterns you see. If there appears to be a problem at a depot, consider:  are there many new drivers, vacancy levels, gaps or changes in management?
Haven’t found what you’re looking for?
Get in touch with our Principal Human Factors Specialist for further information.
Philippa Murphy
Tel: 020 3142 5641
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