Autonóm intelligens járművek helyzete Európa

In 2014, a future route was planned between Rotterdam and Vienna, where – in the coming years - they want to realize the first cross-Europe corridor of the cooperative, intelligent transport system. Even in everyday life, we hear about the increased use of autonomous vehicles now. We are now able to come across driverless metros from Budapest to Paris. The marketable variation of the different automated vehicles (such as buses, truck, cars) are being developed and tested. We’ve been witnessing the success and failure of some of the early models. According to this, the Europe-wide mobility will – with the networking of the various transportation systems and spreading of the individual and public transportation that is capable of autonomous operation - probably change the verticum of the whole society, which will create one of the essential pillars of the intelligent city

Autonóm intelligens járművek helyzete Európa

In 2014, a future route was planned between Rotterdam and Vienna, where – in the coming years - they want to realize the first cross-Europe corridor of the cooperative, intelligent transport system. Even in everyday life, we hear about the increased use of autonomous vehicles now. We are now able to come across driverless metros from Budapest to Paris. The marketable variation of the different automated vehicles (such as buses, truck, cars) are being developed and tested. We’ve been witnessing the success and failure of some of the early models. According to this, the Europe-wide mobility will – with the networking of the various transportation systems and spreading of the individual and public transportation that is capable of autonomous operation - probably change the verticum of the whole society, which will create one of the essential pillars of the intelligent city

INCREASED EFFICIENCY IN MASSIVE TRANSPORT SYSTEMS BY PROGRAMMING SPEED PROFILES BETWEEN SEGMENTS

This study aimed to estimate the specific energy consumption in massive metro-type transport systems, with the aim of guiding the identification of projects aimed at increasing energy efficiency. To achieve the above purposes, this research focused on the estimation, through a software application, of the specific consumption of electrical energy. Results of simulations carried out with an application developed in MATLAB, to generate speed profiles in the operation of the train, allow to observe the sensitivity of the specific consumption of electrical energy to changes made in the cruising speed and in the acceleration and deceleration ramps. The results show that the specific consumptions depend to a large extent on the speed profiles and the operation of the metro, which opens an interesting field of application of optimization techniques aimed at the efficient use of energy.Este estudio tuvo como objetivo estimar el consumo específico de energía en sistemas masivos de transporte tipo metro, con el objetivo de orientar la identificación de proyectos tendientes a incrementar la eficiencia energética. Para lograr los propósitos anteriores, esta investigación se centró en la estimación, a través de una aplicación de software, del consumo específico de energía eléctrica. Resultados de simulaciones realizadas con una aplicación desarrollada en Matlab, para generar perfiles de velocidad en la operación del tren, permiten observar la sensibilidad del consumo específico de energía eléctrica ante cambios realizados en la velocidad de crucero y en las rampas de aceleración y desaceleración. Los resultados muestran que los consumos específicos dependen en gran medida de los perfiles de velocidad y del funcionamiento del metro, lo que abre un interesante campo de aplicación de técnicas de optimización orientadas al uso eficiente de la energía

Measuring the impact of reversible substations on energy efficiency in rail transport

open6Nowadays great interest is placed on the environmental issue. Greenhouse gas emissions are more than 50% higher than in 1990. European energy policy has been supporting efficient energy management in order to reduce the railway transport emissions by 50% within 2030. The railway stakeholders are encouraged to adopt technological solutions to foster energy efficiency. The electrodynamic braking combined with the adoption of reversible substations is one of the most promising solutions. In order to evaluate the impact of this innovative technology, a measurement campaign has been conducted on Metro de Madrid where a reversible substation was installed. In this paper, a preliminary analysis on the data acquired is presented. Traceable and accurate on-board train measurements of the energy flows and the losses are fundamental to quantify the impact of these new technologies and to carry out a survey on the efficiencies of the different vehicle components and on the strategies to reduce the energy consumption in the various operation modes. © IMEKO TC-4 2020.openCascetta F., Cipolletta G., Delle Femine A., Gallo D., Giordano D., Signorino D.Cascetta, F.; Cipolletta, G.; Delle Femine, A.; Gallo, D.; Giordano, D.; Signorino, D

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. IEEE Transactions on Automation Science and Engineering, 9(3), 496-504. doi:10.1109/tase.2012.2201148Domínguez, M., Fernández-Cardador, A., Cucala, A. P., Gonsalves, T., & Fernández, A. (2014). Multi objective particle swarm optimization algorithm for the design of efficient ATO speed profiles in metro lines. Engineering Applications of Artificial Intelligence, 29, 43-53. doi:10.1016/j.engappai.2013.12.015Domínguez, M., Fernández, A., Cucala, A. P., & Lukaszewicz, P. (2011). Optimal design of metro automatic train operation speed profiles for reducing energy consumption. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 225(5), 463-474. doi:10.1177/09544097jrrt420Sicre, C., Cucala, P., Fernández, A., Jiménez, J. A., Ribera, I., & Serrano, A. (2010). A method to optimise train energy consumption combining manual energy efficient driving and scheduling. Computers in Railways XII. doi:10.2495/cr100511Sicre, C., Cucala, A. 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). Application of neural networks in evaluation of railway track quality condition. Journal of Mechanical Science and Technology, 26(1), 113-122. doi:10.1007/s12206-011-1016-5Açıkbaş, S., & Söylemez, M. T. (2008). Coasting point optimisation for mass rail transit lines using artificial neural networks and genetic algorithms. IET Electric Power Applications, 2(3), 172-182. doi:10.1049/iet-epa:20070381Pineda-Jaramillo, J. D., Insa, R., & Martínez, P. (2017). 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. doi:10.1177/0954409717694522Tetko, I. V., Livingstone, D. J., & Luik, A. I. (1995). Neural network studies. 1. Comparison of overfitting and overtraining. Journal of Chemical Information and Computer Sciences, 35(5), 826-833. doi:10.1021/ci00027a006Molines, J., Herrera, M. P., & Medina, J. R. (2018). Estimations of wave forces on crown walls based on wave overtopping rates. Coastal Engineering, 132, 50-62. doi:10.1016/j.coastaleng.2017.11.004Molines, J., & Medina, J. R. (2015). Calibration of overtopping roughness factors for concrete armor units in non-breaking conditions using the CLASH database. Coastal Engineering, 96, 62-70. doi:10.1016/j.coastaleng.2014.11.00

Predicting the traction power of metropolitan railway lines using different machine learning models

This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Rail Transportation on 2021, available online: http://www.tandfonline.com/10.1080/23248378.2020.1829513[EN] 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.This work was supported by the Ministerio de Economia y Competitividad [TRA2011-26602].Pineda-Jaramillo, J.; Martínez Fernández, P.; Villalba Sanchis, I.; Salvador Zuriaga, P.; Insa Franco, R. (2021). Predicting the traction power of metropolitan railway lines using different machine learning models. International Journal of Rail Transportation. 9(5):461-478. https://doi.org/10.1080/23248378.2020.1829513S4614789

An integrated energy-efficient operation methodology for metro systems based on a real case of Shanghai Metro Line One

Metro systems are one of the most important transportation systems in people's lives. Due to the huge amount of energy it consumes every day, highly-efficient operation of a metro system will lead to significant energy savings. In this paper, a new integrated Energy-efficient Operation Methodology (EOM) for metro systems is proposed and validated. Compared with other energy saving methods, EOM does not incur additional cost. In addition, it provides solutions to the frequent disturbance problems in the metro systems. EOM can be divided into two parts: Timetable Optimization (TO) and Compensational Driving Strategy Algorithm (CDSA). First, to get a basic energy-saving effect, a genetic algorithm is used to modify the dwell time of each stop to obtain the most optimal energy-efficient timetable. Then, in order to save additional energy when disturbances happen, a novel CDSA algorithm is formulated and proposed based on the foregoing method. To validate the correctness and effectiveness of the energy-savings possible with EOM, a real case of Shanghai Metro Line One (SMLO) is studied, where EOM was applied. The result shows that a significant amount of energy can be saved by using EOM

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