RRUKA Platform Train Interface call for research – funded projects announced!

RSSB via the Rail Research UK Association (RRUKA) is funding four new feasibility studies in a bid to help reduce the number of incidents occurring at the platform train interface (PTI) when passengers are getting on and off trains.shutterstock_151956011

Last year, RRUKA launched the £500,000 Faster, Safer, Better: Boarding and Alighting Trains call for research. The call specifically sought novel ideas and innovative solutions to help the rail industry reduce dwell time, cater for increasing capacity and reduce safety risk at the PTI.

We invited academic researchers from a variety of disciplines to come together, hear about the issues and form consortia to develop new ideas in relation to one of two challenges:

  • Challenge A: Influencing and improving passenger behaviour at the PTI
  • Challenge B: Future design for faster and safer boarding and alighting

We received an excellent response from the academic community with 24 proposals submitted following the networking and information day held in May.

 


We are delighted to announce that four projects have now been successful in obtaining funding for their projects. They will each assess the feasibility of their ideas over the course of the next twelve months, working closely with industry supporters and subject matter experts.

 

  • Using real-time data on train consist and loading to influence passenger positioning and boarding behaviour at the PTI
    University of Surrey and Loughborough UniversityThis project investigates how to improve the information provided to passengers about an approaching train to influence their behaviour on the platform. Knowing the facilities available on each carriage before a train arrives could help passengers decide where to wait on the platform. The project will explore from the passenger’s perspective, what specific combinations of information about an approaching train could be beneficial, consider how this varies across certain passenger types, and evaluate whether making this additional information available to passengers has a real effect on their behaviour.A technical prototype will be created to demonstrate how to automatically capture the accurate and up-to-date data about the make-up of the train needed to provide this enhanced passenger information.

 

  • Intelligent computer vision agents optimising PTI safety and train dwell times
    Lancaster University, Digital Rail Ltd and NTTX LtdThis project suggests novel, complementary, improvements to the systems available to train drivers and platform staff to reduce PTI FWI (Fatality Weighted Injuries) incidents and benefit the dispatch process as a whole. It will augment CCTV available to personnel with an autonomous computer vision agent that detects and identifies potential PTI incidents, so that platform staff are able to also flag potential incidents and direct colleagues to investigate and assist.

 

  • Feasibility study of a kneeling train
    Loughborough University and CoCatalyst LimitedThis study is to investigate the possibility of modifying or designing trains such that they have the ability to ‘kneel’ – in that the height of the vehicle and lateral position can be adjusted to move closer to the station. The goal of this is to remove (or at least reduce) gaps and steps that occur between the platform and the vehicle body, resulting in faster, safer and more accessible train boarding and alighting.A detailed rail simulation model will be produced that will allow investigators to manipulate the vehicle and assess benefits of these movements.

 

  • RateSetter: Improving passenger boarding rate and reducing risk at the Platform Train Interface
    University of SheffieldThis project will apply data from CCTV on current trains and platforms with novel parallel computing techniques to identify the combinations of platform and train features that set the flow rate of passengers. Items to be studied included vehicle interior layout choices and platform management. Optimisation techniques including genetic algorithms will be applied to find the strongest combinations 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 will focus on quick win retro-fit options for improving existing trains and platforms, and more radical options for future stations and fleets.

Projects are due for completion in 2018, find out how you can keep informed of the developments via the links below.


More information

For any queries, or if you would like to know more, please contact us.

 

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