Savi use case examples

The Savi Rail Performance Model (SaviRPM) provides a powerful strategic decision tool helping train operating companies and Network Rail identify where there are potential risks to performance, and remove system fragility from timetables and service operation, creating a more robust and resilient system to improve service performance for passengers and freight customers.

SaviRPM has been used to:

• De-risk future timetables: by modelling new timetable options and measuring the capacity risks and performance opportunities.

1. Network Rail: Identify performance tipping points

2. Network Rail: Timetable performance and capacity risk

• Assess the potential performance impacts of new services, infrastructure and demand: by adding new services to timetables, and/or changing the underlying rail infrastructure, and changing passenger journey demand - then modelling services to discover any performance impacts and measure benefits.

1. GWR: Performance impacts of new Metro West services, a new station and more passengers

• Improve efficiency of the timetable change process: through early and rapid testing of timetable options to select the ones that perform best, which can then be taken forward into more detailed design.

1. Greater Anglia: December and May timetable option appraisal

• Tune timetables to improve performance: using dynamic running times to tweak service arrival and departure times.

1. Network Rail (Anglia routes): Timetable tuning to improve performance

• Maintain service performance with increases in passenger demand: by testing where increases in passenger volumes might require longer station stops to handle boarding and alighting.

1. RSSB and GWR: Increased risk of long dwell with passenger social distancing and peaks in demand

• Find and deal with the worst performing services: by looking for trains that have the most potential to cause significant delays to other services and passenger arrival lateness.

1. Greater Anglia: The role of ‘disruptive’ trains in poor rail performance

• Tackle the incidents that have the highest impact on service performance: by finding the root causes of delays, and cascading reactionary delays, and modelling interventions to find those that deliver the best performance improvement.

1. Greater Anglia: Finding the types of incidents that affect performance, the COVID lock down experience

• Improve passenger journey satisfaction: exploring the causes of, and ways to decrease passenger journey destination lateness.
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• Improve service performance with better stock and crew schedules: by modelling stock and crew resources as they are required by each service, observing where delays to inbound services could affect outbound services, and improving resource scheduling to mitigate this risk.
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We will be publishing more information about these use cases soon, together with examples of work done for train operating companies and Network Rail.