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Managing disruption through real-time prediction and personalised messaging

For years inaccurate transport predictions have often led to a level of distrust between rail passengers and the operators providing them with service information.

To combat this, we are working with our partners at Birmingham University as part of a Rail Safety Standards Board (RSSB) project, to build on the industry’s capability to deliver accurate disruption information to passengers.

Together we have created a machine learning (ML) prediction model that is deployed to our unique real-time data processing platform. The model uses a vast array of real-time and historic data sources, that may influence delays and disruption, in order to generate more accurate delay predictions on a huge scale. These data sources include:

  • Train movement and operational data
  • Weather data
  • Passenger demand
  • Cascading disruption

We also explored the impact of improved real-time delay and disruption messaging, and how it can influence passenger behaviour and improve the customer experience.

Through our unique Passenger Connect information service, that delivers personalised disruption messaging to passengers, we are exploring how the timing and wording of delay and disruption communications can help set rail passenger’s expectations, and even improve operational efficiency across the network.

We are currently undertaking a real-world customer trial with LNER and will be collating direct customer feedback at scale to help refine and develop the solution further. Watch this space for the results from the customer trial.