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 ecosystem1. According to, “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 EU Agency for Railways, there have been on average just over 2 000 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 EUR 1.61 billion for 2015.
To address railway safety challenges, United Nations (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 European Commission 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 European Shift2Rail (S2R) Joint Undertaking (JU) has established in its Multi Annual Action Plan (MAAP) 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: TD3.6 (DRIMS - Dynamic Railway Information Management System), TD3.8 (IAMS - Intelligent Asset Management Strategies), and TD4.6 (Business Analytics Platform).
The overall DAYDREAMS objective is to move forward, also w.r.t. the S2R JU vision, in 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:
O1.Advancing in maintenance approach by moving from preventive and predictiveasset management towards prescriptive asset management;
O2.Largely improving the decision-making process by developing multi-objectivedecision optimisation approaches thus taking into account all possible(often conflicting) implications of IAMS decisions in the railway environment(e.g., on Traffic Management System, Energy, Freight, etc.);
O3. Reinforcing the roleof the person-in-the-loop by designing and developing advancedcontext-driven Human Machine Interfaces (HMIs) to allow context- andrisk-aware multiple-options decision-making processes supported by theinformation on data sensitivity and robustness. The HMI will allow theperson-in-the-loop to: o Properly access and visualise predictions/metricsand models;
Assess why and how the model predicts something (“opening the black-box”);
Steer models by setting parameters;
Evaluate alternatives using parameter steering and extending this process through speculative execution
The DAYDREAMS objective will be assessed by validating the proposed solution using the following two-step approach:
O4. The validation of TRL 4 approaches
(developed prescriptive asset management - O1 - and multi-objective decision optimisation - O2) using several internal scenarios and one scenario provided by IN2SMART2 (CFM project);
O5. The validation of the DAYDREAMS methodologies (O1, O2 and O3) integrated into a TRL 5 prototype
using at least two scenarios
: at least one internal scenario and the IN2SMART2 scenario. The validation will cover both the performances of the prototypes
and their trust for future adoption in multi-actors environments.
The validation will be carried out by defining evaluation and validation metrics and KPIs
- Strictly linked to one or more asset management problems to be solved by the involved Infrastructure Managers (IMs) and related baselines;
- Quantified and measurable (e.g., reducing maintenance cost by 10% with respect to current costs while keeping the same safety levels; reducing the operators’ workload by 25%);
- Referred to high-level KPIs defined in the S2R IMPACT2 (CFM project);
- Useful to address multi-objective optimisation.