Comillas Pontifical University. Madrid (Spain)
September 17th, 2025
Summary:
Eco-driving and operation design, including energy recovery systems such as regenerative braking, are key techniques for improving efficiency in railway systems. These strategies, combined with advanced optimisation models, have demonstrated significant potential in several railway scenarios, as highlighted in the following studies. The first part of the research study positions eco-driving as a cornerstone of energy reduction in the railway sector and a vital tool for advancing the Sustainable Development Goals in transportation. However, its application in real-world scenarios is often impacted by uncertainties arising from climatological factors, which are frequently disregarded in train driving optimisation models. This work introduces an eco-driving model developed to generate efficient driving commands while accounting for climatological uncertainties, such as temperature, pressure and wind. These uncertainties are modelled using fuzzy numbers, and the optimisation problem is solved with a Genetic Algorithm employing fuzzy parameters, utilising an accurate railway simulator. Applied to a realistic high-speed railway scenario in Spain, the model demonstrates that energy savings can increase from 29.76% to 34.70% when climatological factors are considered for the summer scenario. Moreover, an energy variation of 5.31% is observed between summer and winter scenarios, while punctuality constraints are fully satisfied. This model provides operators with an effective tool to better estimate and optimise energy consumption by adapting driving commands to prevailing climate conditions. The following part of the thesis addresses metropolitan railway operation. This type of system, so widely used in large cities, is a significant energy consumer and a promising target for the implementation of energy efficiency techniques. This work integrates three main strategies: eco-driving, timetable design and regenerative braking, into a simulation-based model to optimise operation while minimising energy consumption. Additionally, the model incorporates the rolling stock required to meet service demand, given its substantial impact on operational costs. Efficient driving commands are developed using a MOPSO (Multi-Objective Particle Swarm Optimisation) algorithm and timetable optimisation is achieved by considering the proposed energy regeneration model and the number of trains required for periodic service. Applied to the Madrid Underground, the model achieves energy savings of up to 24.79% compared to the fastest cycle time, while adhering to operational criteria such as time margins for delay recovery. The study further demonstrates that the GLMO-NSGA-III (Grouped and Linked Mutation Operator – Nondominated Sorting Genetic Algorithm III) algorithm is the most effective for timetable design in this context. Together, these studies illustrate the significant benefits of combining eco-driving, operational planning, and energy regeneration techniques in railway systems, ranging from high-speed networks to metropolitan lines, maximizing energy efficiency and enhancing sustainability.
Descriptors: Traffic engineering, Railway services, Simulation
Keywords: eco-driving; energy efficiency; fuzzy logic; simulation; railway operation; high-speed railway; Energy-efficient train timetable; regenerative braking energy; automatic train operation (ATO); rolling stock
Citation:
M. Blanco-Castillo, "Efficient Railway Traffic Operation", PhD. dissertation, Comillas Pontifical University, Madrid, Spain, 2025.
