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Cross-Cutting Activities

IP Coordinator: Richard French - BT


Total Project Value:
€ 769 958,75
from 01/09/2017 to 31/10/2019
S2R (Of H2020) co-funding:
€ 769 958,75
Daniel Johnson
Leeds University
Complementary projects:
Project website:


SMaRTE Smart Maintenance and the Rail Traveller Experience brings together two related but distinct areas of research. Smart maintenance and human factors are concerned with digitisation and the use of information to enhance decision making, either by industry players in respect of maintenance decisions, or by users of the system in employing smart applications to navigate the rail system and its interaction with other modes.

Smart Maintenance
The Smart maintenance aspect of the project is concerned with the use of information and modelled relationships to better target maintenance where it is needed, based on condition to reduce life cycle costs and generate other benefits including improved vehicle availability and performance or reliability. Objectives include:

  • Review and benchmark of current CBM practices in other sectors, namely the aeronautical sector;
  • Development and integration of reliability ontology;
  • Development and integration of predictive tools for current and future condition of train passenger components;
  • Development of optimization tools to support decision making;
  • Application of CBM model to two real-world case studies on train passenger components.

Human Factors
The human factors research is concerned with understanding the behaviour of users and in particular the factors that deter users from accessing rail, viewing the passenger experience as an end-to-end journey where rail will be only be one part. The aim is then to make recommendations on how to decrease the cognitive effort for individuals using rail services, through planning, booking tickets, integrating it with access to the station and onward mobility at the destination in different journey contexts. Objectives include:
  • Review of demographical and societal factors affecting transport use, usability and attitudes towards transport.
  • Realize an Experience Map project, which considers passengers as individuals behaving in the real context while performing the activities to reach their prefixed objectives.
  • Identification of the physical and planning factors and their relative importance in the journey to identify the resistance at each step of the journey, broken down by demographic groups and mode/journey purpose.
  • Estimation of attrition factors for each activity in the journey, to quantify those potential customers lost at each step of the journey due to unfulfilled usability requirements.
  • Integrate the outcomes of the research into a vision and roadmap of measures to simplify the end-user experience of planning and undertaking a trip that includes a rail journey.

CCA integration
  • The human factors work links to Work area 6 of the CCA-Cross Cutting Activities in the S2R MAAP, Human Capital, which aims to bridge the gap between changes in the railway and other sectors imposed by rapid technological advances and substantial demographic change. Specifically within this work programme this call links to customer oriented design of mobility.
  • The smart maintenance work links to work area 3 of the CCA-Cross Cutting Activities, specifically sub work area 3.3, Smart Maintenance.

Results and Publications

D2.1 Implementation of MSG-3 in other sectors


D2.2 Techniques to Support the Implementation of Smart Rolling Stock Maintenance


D2.3 CBM-Model Case Study Reports


D3.1 Factors affecting train use


D3.2 Experience Map in passenger journeys


D3.3 Passenger Survey Report


D3.4 Smart Journey Vision


D4.2 Impact Assessment and Barriers Final Report (M24) (1)


D5.1 Set up Public Website


D5.3 Report on Dissemination and Exploitation Activities and Project reports achievements for


All deliverables, results and publications herewith provided reflects only the author's view and the S2R JU is not responsible for any use that may be made of the information it contains.

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