Railway track maintenance is becoming a real challenge for Railway Engineers due to the need of meeting increasingly high quality requirements by means of cost-effective procedures. Frequently, this can be only achieved by implementing some technological developments from other fields into the railway sector, such as Digital Signal Processing. Indeed, the present work delves into data acquisition and processing techniques in order to enhance track surveying processes. For this purpose, run tests on the Metropolitan Rail Network of Valencia (Spain) were carried out, and axlebox accelerations were gathered and analysed in different ways. The results determined the optimal sampling and filtering frequencies as well as the location of accelerometers along the train. Furthermore, by means of spectral analysis and time-frequency representations, diverse track defects, track singularities and vibration modes can be clearly identified. It is shown how, with a Hamming time window of 0.5 s and an overlapping of 95%, a wide set of track defects can be detected, without the need of complementary analyses. These values yield the best results as they are a good compromise between time and frequency resolution and allow for appropriate pattern recognition of the corresponding track singularities and resonant frequencies.

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This paper presents the training of a neural network using consumption data measured in the underground network of Valencia (Spain), with the objective of estimating the energy consumption of the systems. After calibration and validation of the neural network using part of the consumption data gathered, the results obtained show that the neural network is capable of predicting power consumption with high accuracy. Once fully trained, the network can be used to study the energy consumption of a metro system and for testing hypothetical operation scenarios. Keywords Gradient; energy consumption; artificial neural networks; Metro; railway; track layout. 1. INTRODUCTION The transport sector contributes greatly to global energy consumption. According to the International Energy Agency [1], overall energy consumption in 2013 was 2 563.52 Million Tonnes of Oil Equivalent (Mtoe), with the transport sector being responsible of up to 27.6%. Railways are generally much more efficient than road transport in terms of energy consumption for both freight and passengers [2], [3], [4]. Despite this, it is still necessary to reduce their energy consumption in order to improve their competitiveness and contribute to a more sustainable world. For this reason, many strategies are implemented to reduce energy consumption in railways. There are strategies proposed concerning line design, rolling stock and operation [5]. Traditionally, energy consumption of an electric train is monitored at the substations. This provides information about the total energy consumed in an instant, or during a given period of time. However, substations do not give information on how the energy is consumed by each element and subsystem of the railway system, and thus it is not possible to know in detail the impact of any action taken to reduce energy consumption. The current energy consumption in railways depends on many factors such as gradients, maximum speeds, loads, patterns of stops, electrical efficiency of train and power supply system, running resistance, driving style, etc. Researchers have estimated the energy consumption and explored improvements in rail transport through track layout optimisation by means of Geographic Information Systems (GIS) [6], [7]. Other authors have used genetic algorithms to optimise different aspects such as track alignments and operator and user costs for rail operation [6], [7], [8] or crew scheduling [9], [10]. There are methods that aim to optimise travel time and coasting points by using models based on artificial neural networks and genetic algorithms [11]. But these methods do not include gradient or real time measured energy consumption as data.

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This paper presents the training of an artificial neural network using consumption data measured in the metropolitan network of Valencia, Spain, to estimate the energy consumption of a metro system. After calibration and validation of the neural network, the results obtained show that it can be used to predict energy consumption with high accuracy. Once fully trained, the neural network is used for testing hypothetical operational scenarios aimed to reduce the energy consumption of a metro system. These operational scenarios include different vertical alignments that prove that Symmetric Vertical Sinusoid Alignments (SVSA) can reduce energy consumption by 18.41% in contrast to a flat (0% gradient) alignment.Keywords: Symmetric Vertical Sinusoid Alignments (SVSA); gradient; energy consumption; artificial neural networks; metro system.Comparando el consumo energético para rutas de tránsito ferroviario mediante Alineamientos Verticales Sinusoidales Simétricos (SVSA), y aplicando redes neuronales artificiales. Un estudio de caso de MetroValencia (España) ResumenEste artículo presenta el entrenamiento de una red neuronal artificial usando el consumo energético medido en la red metropolitana de Valencia, España, para estimar el consumo energético de un sistema metro. Después de la calibración y validación de la red neuronal, los resultados obtenidos muestran que esta puede ser utilizada para predecir el consumo energético con una gran precisión. Una vez entrenada, la red neuronal es utilizada para probar diferentes escenarios de operación hipotéticos con el objetivo de reducir el consumo energético de un sistema metro. Estos escenarios de operación incluyen diferentes trazados verticales que prueban que los Alineamientos Verticales Sinusoidales Simétricos (SVSA, por sus siglas en inglés) pueden reducir el consumo energético en un 18.41 % en contraste con un alineamiento plano (pendiente del 0%).