RateSetter: Improving passenger boarding rate and reducing risk at the Platform Train Interface
University of Sheffield
We are planning to validate the Ratesetter approach with train operators: if you would like to be involved please get in touch at email@example.com We are also discussing knowledge sharing opportunities with rail industry colleagues at the Railway Technical Research Institute (RTRI) in Japan, with a view to comparing and validating the approach and data.
Data from CCTV on current trains and platforms has been analysed using novel parallel computing techniques to identify the combinations of platform and train features that set the flow rate of passengers. Optimisation techniques including genetic algorithms were applied to find the strongest combination of train and platform design with CCTV data used to validate people flows for existing fleets, giving confidence in predictive application for novel train and platform designs. Outputs of the project focus on quick win retro-fit options for improving existing trains and platforms, and more radical options for future stations and fleets.