Transforming the way rail forecasts revenue in post-pandemic Britain

A blog by Ana Chan, Revenue Forecasting Lead

12 September 2024

Predicting the future has always been a challenging task (just ask the mythological Trojan, Cassandra), but the pandemic has made the job of rail forecasters harder still.

That’s because forecasters use past precedent and patterns as their starting point, before overlaying the anticipated impact of future events and trends to make a well-informed prediction about what might happen next.

Take a simple example. More people tend to go on day trips and holidays over the summer, when school is out. This means the railway should expect to serve more leisure travellers and fewer commuters over the summer season. But if there were a heatwave or very wet weather, revenue is likely to dip compared to the baseline as more families choose to stay home rather than going on big days out.

Understanding where passengers are travelling to and how often they make that journey, and therefore how much money the railway can expect to earn from passenger journeys in the medium-term helps leaders to plan where they target investment. But when the pandemic hit, it changed much of the conventional wisdom underpinning rail forecasting.

Since 2020, we’ve seen long-lasting changes to the way people travel. Taking a birds-eye view of the big macro trends, leisure travel has returned to pre-pandemic level and growth has remained strong while business travel is not as popular as it was pre-pandemic. And many have a choice to work from home and therefore train travel has become more discretionary.

The pandemic introduced unprecedented uncertainty and meant that tried and tested models (which relied heavily on past data) struggled to accurately predict what people might do in the future. The railway needed a new model, fast.

A new model for a ‘new normal’

Created in 2021, the National Industry Revenue Model (NIRM) was built to adapt to rail’s ‘new normal’ and offers a clearer, more holistic view of the industry's financial outlook.

Covering both Department for Transport and privately-owned train operators, it blends historical data with analysis of current market trends and economic outlooks that are likely to impact demand across the rail network. From this model, GBRTT produces a national rail passenger revenue forecast.

The national forecast shows how much money we’d expect the railway to generate over the next five years without significant intervention. It gives leaders a view of the ‘baseline’ or underlying growth rates for business, leisure, and commuter revenue, based on evidence and latest insights. But it also helps them to identify and evaluate “interventions” or steps the industry could take to grow revenue and to close the subsidy gap.

Different parts of the industry have different uses for these forecasts. For example, the internal team uses NIRM’s baseline to test the impact of rail initiatives on demand and revenue. Train operators and Network Rail teams are able to use the NIRM to generate dedicated, localised forecasts for their own markets – forecasts that help inform and measure the impact of their own interventions. We work with DfT to share latest insights, market trends and forecasting approach.

We update the model quarterly to:

1. Reflect the latest market conditions and passenger behaviours, making links between new information and the likely impact on rail revenue.
2. Compare old forecasts against actual outcomes to identify discrepancies. This process helps us to improve the base by “normalising” it (removing one-off events) and applying adjustments to enhance future forecasts. For example, last year, we applied an uplift to the commuter market to reflect the upward trend in commuting frequency.
3. Incorporate new evidence and insight, for example we undertake customer surveys to understand how passengers react to rail fares increases and increased cost of living.
4. Capture industry-level revenue forecasts that can be further segmented by geography and journey purpose.

This ongoing refinement helps ensure that the NIRM remains relevant and accurate.

What’s next?

The NIRM is part of a suite of tools that are bringing robust, timely information from across track and train together, to help senior rail leaders to make the best whole-system decisions for customers and taxpayers.

We’re working to make the model more transparent and useful with each iteration. We have created easy-to-navigate dashboard and are starting to share with industry partners, which make the model’s insights more accessible to decision-makers across the industry . We will work with train operating companies, Network Rail and the government to understand further requirements and development of the tool. We look forward to using NIRM to further inform the work of Shadow Great British Railways.

We’ll also continue to work closely with local teams to help them get the most out of the model’s insights, as they work to grow revenue, improve performance and reduce net subsidy.