According to the International Union of Railways the length of tracks maintained by the European railway sector exceeds 300.000 km operating more than 5 billion train-kilometres and offering services for more than 400 billion passenger-kilometres. A steady increase is expected for the next 30 years making railways a key-asset in the European transportation ecosystem. Railway systems are expected to increase their share in transportation by expanding their geographical reach and deliver innovative and integrated travel solutions for people and goods meeting the highest service standards in terms of safety and security. Although European Railways remain the safest in the world, according to data reported in 2017 by the ERA, there have been on average just over 2000 significant accidents each year on the railways of the EU Member States. The economic impact of these accidents has been estimated in the order of €1.61 billion for 2015.
To address railway safety challenges, UN Member States agreed on a set of measures promoting sustainable development ensuring access to safe, affordable, accessible and sustainable transport systems for all citizens by 2030. Besides the UN, the EC also released a new European Mobility Package setting a target for zero traffic fatalities and severe injuries by 2050. To achieve these goals, enhancement of the existing decision support systems with advanced data analytics and mathematical modelling tools are expected to play a key role. These tools can be used not only to predict future issues but also to provide solutions for preventing and solving them, proposing actions to enhance safety and reducing maintenance costs.
The Shift2Rail JU has established in its Multi Annual Action Plan that for “delivering the capabilities to bring about the most sustainable, cost-efficient, high-performing, time-driven, digital and competitive customer-driven transport mode for Europe,” among other characteristics, intelligent maintenance should be introduced to increase capacity and availability and to reduce maintenance costs. The S2R JU also identifies, among the key enabling technologies, machine learning (ML), artificial intelligence (AI) and big data analytics targeting predictive and possibly prescriptive maintenance in S2R demonstrators.
The overall DAYDREAMS objective was to move forward the integration and use of data and artificial/human trustworthy intelligence together with context-driven HMI for prescriptive Intelligent Asset Management Systems (IAMS) in railway by (i) advancing in maintenance approach towards prescriptive asset management, (ii) improving the decision-making process by developing multi-objective decision optimisation approaches taking into account all implications of IAMS decisions in the railway environment, and (iii) reinforcing the role of the person-in-the-loop by designing and developing advanced context-driven HMIs to allow context- and risk-aware multiple-options decision-making processes.
The HMI will allow the person-in-the-loop to: (i) properly access and visualise predictions/metrics and models, (ii) assess why and how the model predicts something, (iii) Steer models by setting parameters, and (iv) evaluate alternatives using parameter steering and extending this process through speculative execution.
