• SMaRTE will build on the best practice in condition based maintenance adopted in other sectors and apply these, along with intelligent data analytics, to the maintenance of rolling stock. This will change the maintenance paradigm from taking action on failure to the prediction of failure and planning of maintenance activities accordingly, therefore moving from a preventative to a predictive maintenance regime. 
  • Whilst there is substantial evidence on the impact of fares, journey time, reliability and crowding on rail usages, some of the more subtle factors that may be preventing passengers using rail as part of their end-to-end journeys (or would persuade passengers to travel by rail) are much less well understood. The ambition of the human factors aspect of this project is to develop an innovative, multi-disciplinary approach to modelling the behaviour of travellers, using the findings to help refocus the role of technology and mobility to put the user back at the centre of the planning process, taking account of the changing role and availability of technology and underlying demographic trends.


This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No: 777627

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