Pablo Martínez Fernández

Dual-gauge railway systems are widely used in Spain to ensure interoperability between Iberian and standard gauges; however, the dynamic behavior of dual-gauge turnouts remains insufficiently characterized due to the scarcity of field measurements. This study presents an experimental campaign aimed at analyzing the vertical dynamic response of a dual-gauge turnout under real traffic conditions. The turnout was instrumented at seven critical sections using displacement transducers and accelerometers, together with a reference section on adjacent plain track, and a total of 68 train passages, including commuter and high-speed services, were analyzed according to train type and operating speed. The vertical displacement time histories and the acceleration signals recorded at the closure panel, together with their corresponding frequency spectra, are also examined to provide a complete characterization of the dynamic response in both the time and frequency domains.. Increasing operating speed leads to higher displacement levels and greater dispersion, whereas differences between train types primarily affect response magnitude rather than its spatial distribution. These findings demonstrate that the dynamic response of dual-gauge turnouts is mainly governed by local geometric discontinuities, providing experimental evidence to support condition monitoring strategies and the validation of vehicle–track interaction models.

La participación ciudadana es un elemento muy recomendable para el desarrollo e implantación de diferentes actuaciones relacionadas con la movilidad urbana, ya que permite conocer las necesidades y expectativas reales de la ciudadanía afectada e involucrarla en el proceso de toma de decisiones. Para facilitar la comprensión de estos procesos participativos a los alumnos del Máster en Transporte, Territorio y Urbanismo de la Universidad Politècnica de València, se ha propuesto un taller participativo en el que los alumnos asumen los los roles de los diferentes actores que intervienen en esta clase de procesos, y se ven enfrentados a la necesidad de valorar diferentes puntos de vista y expectativas potencialmente conflictivas. Los alumnos han participado en el taller con una actitud abierta, han expresado su satisfacción con la actividad realizada, y les ha servido como base para abordar los aspectos más teóricos de la participación ciudada en el ámbito de la movilidad urbana.

This study aims to optimize the Environmental Life Cycle Assessment (LCA) and Life Cycle Cost (LCC) of NiZn batteries using Pareto Optimization (PO) and Multi-objective Particle Swarm Optimization (MOPSO), which combine Pareto optimization and genetic algorithms (GA). The optimization focuses on the raw material acquisition phase and the end-of-life phase of NiZn batteries to improve their sustainability Key Performance Indicators (KPIs). The optimization methodology, programmed in MATLAB, is based on a formulation model of LCC and the environmental LCA, using data available from the Ecoinvent database, the OpenLCA software (V1.11.0), and other public databases. Results provide insights about the best combination of countries for acquiring raw materials to manufacture NiZn and for disposing of the waste of NiZn batteries that cannot be recycled. These results were automatically linked to some sustainability KPIs, such as global warming and capital costs, being replicable in case of data updates or changes in production or recycling locations, which were initially considered at Paris (France) and Krefeld (Germany), respectively. These results provided by an AI model were validated by using a sensitivity analysis and the Analytical Hierarchy Process (AHP) through an expert panel. The sensitivity analysis ensures the robustness of mathematical parameters and future variations in the market; on the other hand, the AHP validates the Artificial Intelligence (AI) results with interactions of human factors. Further developments should also consider the manufacturing and use phases in the optimization model.

Obtaining efficient speed profiles for metro trains is a multi-objective optimisation problem where energy consumption and travel time must be balanced. Automatic Train Operation (ATO) systems may handle a great number of possible speed profiles; hence optimisation algorithms are required to find efficient ones in a timely manner. This paper aims to assess the performance of a particular meta-heuristic optimisation algorithm, a variation of the traditional Ant Colony (ACO) modified to deal with multi-objective problems with continuous variables: MOACOr. This algorithm is used to obtain efficient speed profiles in up to 32 interstation sections in the metro network of Valencia (Spain), and the convergence and diversity of these solution sets is evaluated through metrics such as Inverse Generational Distance (GD) and Normalised Hypervolume (NH). The results are then compared to those obtained with a conventional genetic algorithm (NSGA-II), including a statistical analysis to identify significant differences. It has been found that MOACOr shows a better performance than NSGA-II in terms of convergence, regularity and diversity of the solution. These results indicate that MOACOr is a good alternative to the widely used genetic algorithm and could be a better tool for rail operation managers trying to improve energy efficiency.

Railways are an efficient transport mean with lower energy consumption and emissions in comparison to other transport means for freight and passengers, and yet there is a growing need to increase their efficiency. To achieve this, it is needed to accurately predict their energy consumption, a task which is traditionally carried out using deterministic models which rely on data measured through money- and time-consuming methods. Using four basic (and cheap to measure) features (train speed, acceleration, track slope and radius of curvature) from MetroValencia (Spain), we predicted the traction power using different machine learning models, obtaining that a random forest model outperforms other approaches in such task. The results show the possibility of using basic features to predict the traction power in a metropolitan railway line, and the chance of using this model as a tool to assess different strategies in order to increase the energy efficiency in these lines.

Minimizing energy consumption is a key issue from both an environmental and economic perspectives for railways systems; however, it is also important to reduce infrastructure construction costs. In the present work, an artificial neural network (ANN) was trained to estimate the energy consumption of a metropolitan railway line. This ANN was used to test hypothetical vertical alignments scenarios, proving that symmetric vertical sinusoid alignments (SVSA) can reduce energy consumption by up to 18.4% compared with a flat alignment. Finally, we analyzed the impact of SVSA application on infrastructure construction costs, considering different scenarios based on top–down excavation methods. When balancing reduction in energy consumption against infrastructure construction costs between SVSA and flat alignment, the extra construction costs due to SVSA have a return period of 25–300 years compared with a flat alignment, depending on the soil type and construction method used. Symmetric vertical sinusoid alignment layouts are thus suitable for scattered or soft soils, up to compacted intermediate geomaterials.

This paper presents the application of machine learning systems based on neural networks to model the energy consumption of electric metro trains, as a first step in a research project that aims to optimise the energy consumed for traction in the Metro Network of Valencia (Spain). An experimental dataset was gathered and used for training. Four input variables (train speed and acceleration, track slope and curvature) and one output variable (traction power) were considered. The fully trained neural network shows good agreement with the target data, with relative mean square error around 21%. Additional tests with independent datasets also give good results (relative mean square error = 16%). The neural network has been applied to five simple case studies to assess its performance – and has proven to correctly model basic consumption trends (e.g. the influence of the slope) – and to properly reproduce acceleration, holding and braking, although it tends to slightly underestimate the energy regenerated during braking. Overall, the neural network provides a consistent estimation of traction power and the global energy consumption of metro trains, and thus may be used as a modelling tool during further stages of research.