Data Insights from RSSB: shaping smarter, safer rail
There are millions more of these figures – rail is data rich. And they’re not just a curiosity. When we combine and analyse data from around the industry, it can deliver answers to the questions that are key to running the network more efficiently and safely.
That’s why RSSB’s data analysis is so important to rail.
We’ve built our Data Insights (DI) analytics capability over the years through our work on Risk and Safety Intelligence using data from the Safety Management Intelligence System (SMIS) and Precursor Indicator Model (PIM).
But now, as the amount of available data increases, we’re expanding our capabilities far beyond safety. As we broaden our horizons, we’re focusing on the complex analyses that generate insights the rail industry can act upon. We do this by aggregating multiple large datasets, then using our expertise to apply AI and machine learning.
Why our data expertise is key
The data we start with isn’t always neat and tidy – and different datasets don’t always fit together easily. But our DI experience and expertise lets us make sense of such messy data.
In just one example, our recent analysis of depot safety combined disparate data from a huge range of sources: SMIS; TRUST train movements; ORR depot stewardship scores; weather data; depot utilisation data; and workforce breakdowns of hours and shift times.
Putting actionable information first
We don’t go on fishing expeditions. We prioritise our work according to its likely benefit to the industry, answering specific questions. And we engage with our Members during analyses, as they understand the data’s operational context and can help us identify the most meaningful insights.
This guarantees that our efforts support industry decision making and provide direction for risk management activities – helping rail work ever smarter.
What do we do?
Every day, we work on active, urgent pieces of analysis – like data insight sprints to analyse key safety risk areas and support delivery of Leading Health and Safety on Britain’s Railway (LHSBR). These sprints combine our knowledge and expertise with the application of data science’s leading analytical tools and techniques.
These analytical deep dives can deliver surprising insights:
- DI contributed to our Whole System Risk Model, which has allowed analysis of rainfall patterns and earthwork failures to calculate the probability of earthwork failure during extreme weather events, minimising disruptive blanket speed restrictions without compromising safety
- Signals Passed At Danger (SPAD) analysis revealed that the number of SPADs rises during heatwaves
- Depot safety analysis has shown when accidents are most likely (around 10am, as trains return from rush hour), allowing these to be addressed
- And finally, we’ve revealed that the morning rush hour isn’t just for people… the number of animal incursions on the line (mainly sheep, cows and deer) peaks at 8am!
We need your suggestions
The more DI work we undertake, the more industry can understand the patterns behind rail network operations – and the more we can all act on that knowledge.
Which is why we’re so keen to get Members involved.
Together, we can zero in on rail’s biggest problems – and use the power of data to solve them.