New modelling work and research for RSSB have shown that although social distancing might increase dwell times, the overall performance impact is not significant nor widespread.
As train operators welcome passengers back to the railway, the industry needed to understand the impact of increasing numbers and continued social distance to better manage network performance. Good performance would play a key role in rebuilding passenger confidence in the railway.
Here is a link to a short RSSB article and video about the impact of social distancing on train service performance.
This RSSB led research modelled the effects of social distancing at the Platform Train Interface (PTI). The work involved two tools:
- RateSetter, a pedestrian modelling tool developed by University of Sheffield, focussed on design-solutions to PTI, platforms and station challenges
- SaviRPM, Risk Solutions’ rail performance model that can quickly test new timetables, identify pinch points and suggest changes to enhance the feasibility, robustness and resilience of rail services.
Ratesetter showed that, not surprisingly, social distancing increases the boarding and alighting time. But the question is: could this impact dwell times and cause potential delays on individual train services? Also, could this lead to cascading delays across the rail network?
To answer this question, RSSB asked Risk Solutions to use SaviRPM to identify where and how performance risks might emerge. The analysis concluded that:
- The impact of social distancing is larger for more heavily loaded doors (those with more than 10 passengers boarding and alighting). Therefore, as passenger number increases and if social distance is followed, the impact is likely to increase.
- Longer boarding and alighting time is likely not to be a widespread risk, but localised at certain ‘hot spots’. These ‘hot spots’ are stations characterised by timetabled dwell times of 1 minute or less.
- The impact on overall service performance using the CP6 metric (>1 min late) will vary for different operators, routes and service groups. However, the impact is larger on frequently stopping local or commuter services with shorter timetabled dwells. Based on the modelling these can experience in the range of 2% to 7% fewer on-time service stops averaged across a full 24-hour period.
This preliminary modelling and data analysis show that the overall performance impact at a system level is not large, in the region of 1.5% on average. However, there may be circumstances that cause more significant impact at a local level or for particular service groups. Therefore, operators were advised to continue to gain better intelligence of their network by collecting data and understanding where localised conditions might create these lateness hotspots.
“Savi RPM is a very powerful tool. Its flexibility means it can support rapid exploration and insight extraction for possible events and novel scenarios. Getting new insight so rapidly enables the team to pivot and focus on the key issues as they emerge, directing resources to where it matters most” Robert Staunton, Research and Innovation Account Manager, RSSB
SaviRPM is a sophisticated simulation and performance modelling application developed by Risk Solutions and available to the rail industry. It enables rail performance teams to generate better understanding and management of performance and capacity risk, using cutting edge methods including: big data analysis, agent based modelling, and interactive visualisations.
Savi 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.
Savi is unique in two ways:
- it introduces typical incidents and delays to modelled services, providing more realistic interactions between services
- through interactive visualisations, developed by City, University of London, it allows users to identify risk and model potential ways to reduce risk and improve performance, to find effective solutions.
Here is a short introductory presentation from Jonathan describing what Savi can do.