Modelling Electric Trains Energy Consumption Using Neural Networks

[EN] Nowadays there is an evident concern regarding the efficiency and sustainability of the transport sector due to both the threat of climate change and the current financial crisis. This concern explains the growth of railways over the last years as they present an inherent efficiency compared to other transport means. However, in order to further expand their role, it is necessary to optimise their energy consumption so as to increase their competitiveness. Improving railways energy efficiency requires both reliable data and modelling tools that will allow the study of different variables and alternatives. With this need in mind, this paper presents the development of consumption models based on neural networks that calculate the energy consumption of electric trains. These networks have been trained based on an extensive set of consumption data measured in line 1 of the Valencia Metro Network. Once trained, the neural networks provide a reliable estimation of the vehicles consumption along a specific route when fed with input data such as train speed, acceleration or track longitudinal slope. These networks represent a useful modelling tool that may allow a deeper study of railway lines in terms of energy expenditure with the objective of reducing the costs and environmental impact associated to railways.The authors wish to thank Ferrocarrils de la Generalitat Valenciana (FGV) for their permission and help during the monitoring campaign. Project funded by the Spanish Ministry of Economy and Competitiveness (Grant Number TRA2011-26602).Martínez Fernández, P.; García-Román, C.; Insa Franco, R. (2016). Modelling Electric Trains Energy Consumption Using Neural Networks. Transportation Research Procedia. 18:59-65. https://doi.org/10.1016/j.trpro.2016.12.008S59651

Comparing energy consumption for rail transit routes through Symmetric Vertical Sinusoid Alignments (SVSA), and applying artificial neural networks. A case study of Metro Valencia (Spain)

[ES] Este 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%).[EN] 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.Pineda-Jaramillo, JD.; Salvador Zuriaga, P.; Insa Franco, R. (2017). Comparing energy consumption for rail transit routes through Symmetric Vertical Sinusoid Alignments (SVSA), and applying artificial neural networks. A case study of Metro Valencia (Spain). DYNA. 84(203):17-23. https://doi.org/10.15446/dyna.v84n203.65267S17238420

Modeling the energy consumption of trains by applying neural networks

[EN] 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 the calibration and validation of the neural network using part of the gathered consumption data, 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 the hypothetical operation scenarios.The realization of this paper was possible thanks to the collaboration agreement signed between the Universitat Politecnica de Valencia and Ferrocarrils de la Generalitat Valenciana, and funding obtained by the Spanish Ministry of Economy and Competitiveness, through the project "Strategies for the design and energy-efficient operation of railway and tramway infrastructure'' (Ref. TRA2011-26602).Pineda-Jaramillo, JD.; Insa Franco, R.; Martínez Fernández, P. (2018). Modeling the energy consumption of trains by applying neural networks. Proceedings of the Institution of Mechanical Engineers Part F Journal of Rail and Rapid Transit. 232(3):816-823. https://doi.org/10.1177/0954409717694522S816823232

A review of modelling and optimisation methods applied to railways energy consumption

[EN] Railways are a rather efficient transport mean, and yet there is increasing interest in reducing their energy consumption and making them more sustainable in the current context of climate change. Many studies try to model, analyse and optimise the energy consumed by railways, and there is a wide diversity of methods, techniques and approaches regarding how to formulate and solve this problem. This paper aims to provide insight into this topic by reviewing up to 52 papers related to railways energy consumption. Two main areas are analysed: modelling techniques used to simulate train(s) movement and energy consumption, and optimisation methods used to achieve more efficient train circulations in railway networks. The most used methods in each case are briefly described and the main trends found are analysed. Furthermore, a statistical study has been carried out to recognise relationships between methods and optimisation variables. It was found that deterministic models based on the Davis equation are by far (85% of the papers reviewed) the most common in terms of modelling. As for optimisation, meta-heuristic methods are the preferred choice (57.8%), particularly Genetic Algorithms.Martínez Fernández, P.; Villalba Sanchis, I.; Yepes, V.; Insa Franco, R. (2019). A review of modelling and optimisation methods applied to railways energy consumption. Journal of Cleaner Production. 222:153-162. https://doi.org/10.1016/j.jclepro.2019.03.037S15316222

Slab Track Optimization Using Metamodels to Improve Rail Construction Sustainability

[EN] Railways are an efficient transportmode, but building and maintaining railway tracks has a significant environmental impact in terms of CO2 emissions and use of raw materials. This is particularly true for slab tracks, which require large quantities of concrete. They are also more expensive to build than conventional ballasted tracks, but require less maintenance and have other advantages that make them a good alternative, especially for high-speed lines. To contribute to more sustainable railways, this paper aims to optimize the design of one of the most common slab track typologies: RHEDA 2000. The main objective is to reduce the amount of concrete required to build the slab without compromising its performance and durability. To do so, a model based on finite-element method (FEM) of the track was used, paired with a kriging metamodel to allow analyzing multiple options of slab thickness and concrete strength in a timely manner. By means of kriging, optimal solutions were obtained and then validated through the FEM model to ensure that predefined mechanical and geometrical constraints were met. Starting from an initial setup with a 30-cm slab made of concrete with a characteristic strength of 40 MPa, an optimized solution was reached, consisting of a 24-cm slab made of concrete with a strength of 45 MPa, which yields a cost reduction of 17.5%. This process may be now applied to other slab typologies to obtain more sustainable designs.The authors wish to thank Joaquin J. Pons for his work, which was extremely helpful in the development of this paper. This project was supported by Grant PID2020-117056RB-I00 funded by MCIN/AEI/10.13039/501100011033 and by "ERDF Away of making Europe."Martínez Fernández, P.; Villalba Sanchis, I.; Insa Franco, R.; Yepes, V. (2022). Slab Track Optimization Using Metamodels to Improve Rail Construction Sustainability. Journal of Construction Engineering and Management. 148(7):1-10. https://doi.org/10.1061/(ASCE)CO.1943-7862.0002288110148

An analytical model for the prediction of thermal track buckling in dual gauge tracks

[EN] In rail transport, track gauge is one of the principal factors that condition the passage of trains. For technical and economic reasons, in some circumstances it is necessary to build and operate the so-called dual gauge track, in which a third rail is added to allow operation of trains in two separate gauges. Although the problem of lateral buckling of rail tracks under thermal loading has been well researched, the addition of the third rail increases the steel area subjected to thermal loads, and thus requires a more accurate analysis. The objective of this paper is to develop an analytical model to analyse the lateral buckling under thermal loads on dual gauge tracks. An in-depth analysis of the effects of the thermal track buckling response produced by each fundamental parameter is presented and discussed. It is found that the risk of buckling is more in dual gauge tracks when compared with the conventional tracks. Finally, this model establishes a mechanism that can be used to perform a more effective infrastructure management policy.Villalba Sanchis, I.; Insa Franco, R.; Salvador Zuriaga, P.; Martínez Fernández, P. (2018). An analytical model for the prediction of thermal track buckling in dual gauge tracks. Proceedings of the Institution of Mechanical Engineers. Part F, Journal of rail and rapid transit (Online). 1-10. doi:10.1177/0954409718764194S11

Impact of Symmetric Vertical Sinusoid Alignments on Infrastructure Construction Costs: Optimizing Energy Consumption in Metropolitan Railway Lines Using Artificial Neural Networks

[EN] 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 was realized thanks to the collaboration agreement signed between Ferrocarrils de la Generalitat Valenciana and Universitat Politecnica de Valencia, and funding obtained by the Spanish Ministry of Economy and Competitiveness through the project ''Strategies for the design and energy-efficient operation of railway and tramway infrastructure'' (Ref. TRA2011-26602).Pineda-Jaramillo, J.; Salvador Zuriaga, P.; Martínez Fernández, P.; Insa Franco, R. (2020). Impact of Symmetric Vertical Sinusoid Alignments on Infrastructure Construction Costs: Optimizing Energy Consumption in Metropolitan Railway Lines Using Artificial Neural Networks. Urban Rail Transit. 6(3):145-156. https://doi.org/10.1007/s40864-020-00130-714515663International Energy Agency (2018) Key world energy statistics. 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Analysis of the vibration alleviation of a new railway sub-ballast layer with waste tyre rubber

[EN] This paper focuses on the assessment of the vibration behaviour of granular sub-ballast materials mixed with rubber particles from scrap tyres. The main objective is to evaluate whether these mixes attenuate vibrations caused by passing trains, and if so, to what extent. Several laboratory and field tests were carried out to monitor the response of such materials to various excitation sources. The results show that under controlled laboratory conditions, the addition of rubber (up to 5% by weight) greatly increases the damping ratio and reduces vibration. Field tests also show that higher percentages of rubber yield a significant alleviation of vibration caused by repetitive and harmonic loads that are similar to those produced by passing trains. An addition of 5% by weight yields a reduction of 50% in the mean acceleration peak at one metre from the excitation source. The anisotropy of the mix is another key factor when evaluating the vibration behaviour of these mixes.The authors wish to thank GUEROLA for providing the soil samples from its quarry, EMRO for providing the rubber particles, and Ángel Morilla Rubio, Manolo Medel Perallo´n, and Esther Medel Colmenar for their help during field tests.Hidalgo Signes, C.; Martínez Fernández, P.; Medel Perallon, E.; Insa Franco, R. (2017). Analysis of the vibration alleviation of a new railway sub-ballast layer with waste tyre rubber. Materials and Structures. 50(2):1-13. doi:10.1617/s11527-016-0951-0S113502Yoon S, Prezzi M, Zia Siddiki N, Kim B (2005) Construction of a test embankment using a sand–tire shred mixture as fill material. Waste Manag 26:1033–1044Nakhaei A, Marandi SM, Sani Kermani S, Bagheripour MH (2012) Dynamic properties of granular soils mixed with granulated rubber. Soil Dyn Earthq Eng 43:124–132Buonanno A, Mele R (2012) The use of bituminous mix sub-ballast in the Italian state railways. 2nd eurasphalt and eurobitume congress, Barcelona, 20–22 September 2000Di Mino G, Di Liberto M, Maggiore C, Noto S (2012) A dynamic model of ballasted rail track with bituminous sub-ballast layer. Proced Soc Behav Sci 53:366–378Hidalgo C, Martínez P, Medel E, Insa R (2015) Characterisation of an unbound granular mixture with waste tyre rubber for subballast layers. Mater Struct 48:3847–3861. doi: 10.1617/s11527-014-0443-zThompson DJ (2009) Railway noise and vibration: mechanisms, modelling and means of control. Elsevier, OxfordAuersch L (2005) The excitation of ground vibration by rail traffic: theory of vehicle-track-soil interaction and measurements on high-speed lines. J Sound Vib 284(1):103–132Di Mino G, Di Liberto M (2007) Modelling and experimental survey on ground borne vibration induced by rail traffic. 4th international SIIV congress, 12–14 September, PalermoAlves P, Calçada R, Silva A (2012) Ballast mats for the reduction of railway traffic vibrations. Numerical study. Soil Dyn Earthq Eng 42:137–150Karlström A, Boström A (2007) Efficiency of trenches along railways for trains moving at sub- or supersonic speeds. Soil Dyn Earthq Eng 27:625–641Wolfe S, Humphrey D, Wetzel E (2004) Development of tire shred underlayment to reduce ground-borne vibration from LRT track. Geotechnical engineering for transportation projects. Proceedings of geotrans, vol 126. pp 750–759ASTM (2002) C215:2002. Standard test method for fundamental transverse, longitudinal, and torsional resonant frequencies of concrete specimensGuimond-Barrett A, Nauleau E, Le Kouby A, Pantet A, Reiffsteck P, Martineau F (2013) Free–free resonance testing of in situ deep mixed soils. Geotech Test J 36(2):1–9. doi: 10.1520/GTJ20120058Spanish Ministry of Public Works (2006) Pliego de Prescripciones Técnicas Generales de Materiales Ferroviarios PF-7: Subbalasto (general technical specifications for railway materials PF-7: subballast). Boletín Oficial del Estado 103:16891–16909ADIF (2008) Pliego de Prescripciones Técnicas Tipo para los Proyectos de Plataforma PGP-2008 (techical specifications for railway platform projects PGP-2008)Singh B, Vinot V (2011) Influence of waste tire chips on strength characteristics of soils. J Civ Eng Archit 5(9):819–827Martínez P, Villalba I, Botello F, Insa R (2013) Monitoring and analysis of vibration transmission for various track typologies. A case study. Transp Res Part D 24:98–109Wolfram Research Inc (2008) Mathematica [software], version 7.0, ChampaignISO (2003) Mechanical vibration and shock. Evaluation of human exposure to whole-body vibration. Part 2: vibration in buildings (1–80 Hz)Feng ZY, Sutter KG (2000) Dynamic properties of granulated rubber/sand mixtures. Geotech Test J 23(3):338–344AENOR (1994) UNE EN 29052-1:89. Acoustics—determination of the dynamic stiffness. Part 1: materials used under floating floors in dwelling

Neural networks for modelling the energy consumption of metro trains

[EN] 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.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Project funded by the Spanish Ministry of Economy and Competitiveness (Grant number TRA2011-26602).Martínez Fernández, P.; Salvador Zuriaga, P.; Villalba Sanchis, I.; Insa Franco, R. (2020). Neural networks for modelling the energy consumption of metro trains. Proceedings of the Institution of Mechanical Engineers. Part F, Journal of rail and rapid transit (Online). 234(7):722-733. https://doi.org/10.1177/0954409719861595S7227332347Douglas, H., Roberts, C., Hillmansen, S., & Schmid, F. (2015). An assessment of available measures to reduce traction energy use in railway networks. Energy Conversion and Management, 106, 1149-1165. doi:10.1016/j.enconman.2015.10.053Su, S., Tang, T., & Wang, Y. (2016). Evaluation of Strategies to Reducing Traction Energy Consumption of Metro Systems Using an Optimal Train Control Simulation Model. Energies, 9(2), 105. doi:10.3390/en9020105Domínguez, M., Fernández, A., Cucala, A. P., & Blanquer, J. (2010). Efficient design of Automatic Train Operation speed profiles with on board energy storage devices. Computers in Railways XII. doi:10.2495/cr100471Dominguez, M., Fernandez-Cardador, A., Cucala, A. P., & Pecharroman, R. R. (2012). Energy Savings in Metropolitan Railway Substations Through Regenerative Energy Recovery and Optimal Design of ATO Speed Profiles. 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P., & Fernández-Cardador, A. (2014). Real time regulation of efficient driving of high speed trains based on a genetic algorithm and a fuzzy model of manual driving. Engineering Applications of Artificial Intelligence, 29, 79-92. doi:10.1016/j.engappai.2013.07.015Van Gent, M. R. A., van den Boogaard, H. F. P., Pozueta, B., & Medina, J. R. (2007). Neural network modelling of wave overtopping at coastal structures. Coastal Engineering, 54(8), 586-593. doi:10.1016/j.coastaleng.2006.12.001Hasançebi, O., & Dumlupınar, T. (2013). Linear and nonlinear model updating of reinforced concrete T-beam bridges using artificial neural networks. Computers & Structures, 119, 1-11. doi:10.1016/j.compstruc.2012.12.017Shahin, M. A., & Indraratna, B. (2006). Modeling the mechanical behavior of railway ballast using artificial neural networks. Canadian Geotechnical Journal, 43(11), 1144-1152. doi:10.1139/t06-077Sadeghi, J., & Askarinejad, H. (2012). 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