Keywords: urban rail systems, decision-making, energy saving, sensor data, traction energy, auxiliary energy.

Urban rail systems are important to metropolitan mobility but are also major energy consumers, particularly in cold climate regions like Stockholm. Growing availability of high resolution sensor data from modern fleets gives new opportunities to analyse energy use, identify efficiency measures and support evidence based decision making for energy saving. This paper presents a structured, data driven decision support method developed through a close collaboration between university, transport administration and train operator. The method integrates quantitative modelling which consists of train traction simulation, thermodynamic modelling, infrastructure heating analysis, and power supply network simulation, and qualitative tools, like the Business Model Canvas. It organizes the evaluation of candidate measures into a five step flow covering identification, pre study, test planning, implementation decision and continued follow up. Case study on the Stockholm commuter trains demonstrates substantial energy saving potential across multiple domains: thermodynamic measures, e.g., optimizing setpoint temperature and implementing energy saving parking mode to reduce heat loss, operational strategies, e.g., eco driving driving and modest timetable slack to reduce traction energy. Together, these results show that sensor data driven analysis can enable targeted, cost effective and scalable improvements in energy efficiency of urban rail systems, providing a practical pathway from analytics to implementation.

download the full-text of this paper (PDF, 7 pages, 363 Kb)

go to the previous paper
go to the next paper
return to the table of contents
return to the volume description