Periodic Reporting for period 2 - SMaRTE (Smart Maintenance and the Rail Traveller Experience)
Okres sprawozdawczy: 2018-09-01 do 2019-10-31
The Human factors research (WP3) is concerned with understanding factors that deter users from rail. The aim is to recommend how to decrease the physical and cognitive effort for individuals using rail in different journey contexts.
Why important for society?
• By understanding user behaviour throughout the journey, improvements in passenger experience that lead to increased use of rail can be identified and societal benefit valued. These benefits could take the form of increased rail revenues, user benefits and external cost savings.
• For Smart Maintenance, there are potential societal benefits around better reliability and availability of rolling stock, alongside environmental benefits associated with component usage.
To improve current railway maintenance systems, through integration of predictive data analysis algorithms and online optimization tools within an improved Condition Based Maintenance strategy.
To understand current and future needs of passengers, and identify aspects which could be improved and simplified.
In Task 2.2 a conceptual CBM system for rolling stock was introduced consisting of two tasks: (1) condition monitoring and (2) maintenance decision support. Data is collected to diagnose and identify the root causes of system failures. Following this, prognosis techniques were applied to predict occurrence and degradation of failures.
Issues relating to interoperability of data within a CBM system were discussed, along with characteristics of typical condition data. A data model for the CBM system is proposed using an ontology approach to ensure system data is interoperable.
Work conducted in consultation and collaboration with IMPACT-2.
Task 2.3 reviewed data processing/extraction techniques to support this prognostic-based approach, including Statistical Analysis and Machine Learning. A second task proposed techniques, including: Linear Mixed Models, Survival Models and Markov Decision Process (MDP), to derive optimal maintenance decision map to assess feasibility of proposed maintenance strategy.
Task 2.4 applied these techniques. Firstly, regression and neural network techniques were applied to diagnostic data for a traction and braking system, demonstrating feasibility for predicting failures. Secondly, techniques were applied to wheelset condition data to demonstrate potential for supporting maintenance decision making and optimisation. This included statistical modelling of wheelset degradation, survival modelling and a Markov Decision Process. These provided a robust method for deriving a CBM strategy map for wheelset re-profiling.
In WP3, Task 3.1 reviewed demographic and societal factors affecting rail use. Task 3.2 focused on evaluating and mapping passengers’ experiences based on consultation with passengers and stakeholders. The outputs led to development of an Experience map tracing the passenger journey cycle, identifying where needs are not met.
Task 3.3 was based on a travel survey, to define influence of key factors behind choice of rail. Findings identified key gaps in rail provision and barriers for non-rail passengers.
Task 3.4 added expert opinion to synthesise a “Smart Journey” vision, with a “Railmap” of recommendations to simplify and improve user experience under different futures.
Recommendations included actions to improve affordability and ticket flexibility, safety and security and facilities around stations, rolling stock comfort and trip planning tools. More costly recommendations included improving reliability, frequency and first and last mile travel experience.
WP4 Impact Assessment
The impact of interventions in WP2 and WP3 was demonstrated through a set of KPIs and a business and financial case.
An impact assessment of condition-based wheelset maintenance activities, operationalised by relaxing the wheelset turning interval, indicated a business and financial case can be made for such CBM interventions. Lifetime cost reductions up to 35% on preventive and corrective wheelset maintenance realised and up to 3% of total preventive maintenance costs annually can be saved. Potential for cost reductions depends on the component under consideration.
A second case study identified reductions of annual preventive maintenance costs up to 1% when the maintenance interval for sliding doors was relaxed.
An impact assessment examined demand, revenue and welfare implications of suggested improvements in rail passenger experience from WP3. Impacts gauged through scenarios applied to 3 case studies based on Leeds and Manchester. We used valuations and sensitivities from an extensive literature search.
Our basic scenario looked at 10% improvements in quantifiable aspects of rail service quality. A second scenario added 10% reductions in Access/ Egress costs/times and a third examined lower cost solutions.
Results show scope for extensive benefits with 25-37% demand uplifts where all identified improvements implemented. Uplifts lowest for light rail and highest for longer distance. The lower cost scenario found uplifts of 9-12%. Whilst uplifts are driven by fare and GJT changes, our findings suggest role for ‘softer’ factors such as crowding, vehicle cleanliness, station environment and first/ last mile experience.
Dissemination and Promotion
Developed dissemination plan, website, identity set, brochure and newsletters and a data management plan. Collaboration between IMPACT-2 and SMaRTE implemented. Advisory Group established. Dissemination activities conducted at various congresses and conferences.
Further Exploitation activities:
• Continued collaboration with IMPACT-2.
• Output dissemination through (UKRRIN) Centre of Excellence in Rolling Stock and at Railway Industry Association (RIA) event (December 2019)
• New Shift2Rail IP5 project, LOCATE, will utilise outputs for application to locomotive bogie condition maintenance.
• WP3 based papers produced for TRA 2020
• WP3 findings will inform impact assessment of digital technologies in Shift2Maas project.
• Increased availability of rolling stock due to less unplanned maintenance and down time
• Improvements in usability
• Reduced rolling stock maintenance costs, component costs and infrastructure damage
• Estimation of cost savings from improvements in maintenance procedures.
• Links with IMPACT-2 mean WP2 techniques will potentially be applied to a wider range of components/systems, supporting development of guidance on CBM-based maintenance (to rolling stock and infrastructure) and potentially inform European standards.
• WP3 delivers a holistic understanding of the rail journey process, identifying usability issues and needs through passengers’ experience, surveying and focus groups.
• The Smart Journey Vision demonstrates achievable methods to improve usability and increase attractiveness of rail.
• Evaluation of social impacts from increased use of rail.