3,088 research outputs found

    Automatic Train Operation Speed Profile Optimization and Tracking with Multi-Objective in Urban Railway

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    Besides energy-efficiency, people also want train operation to be comfortable, punctual and parking precise. In this paper, a multi-objective model for automatic train operation in urban railway is proposed by unifying dimensions of different objectives firstly. This model is built by applying multi-objective decision with the penalty function, based on the analysis of train performance and its operation environment. Then a genetic algorithm is developed to solve this model and obtain the optimal recommended speed profiles. Thirdly, fuzzy controller is designed to achieve track recommended speed profiles. Finally, with the help of Matlab software, control effect is verified based on simulation. From the simulation results, it can be seen this strategy can meet the requirement of multi-objective, which are energy-saving, parking precisely, running punctually and comfort

    A novel method for determining fixed running time in operating electric train tracking optimal speed profile

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    Tracking the optimal speed profile in electric train operation has been proposed as a potential solution for reducing energy consumption in electric train operation, at no cost to improve infrastructure of existing Metro lines as well. However, the optimal speed profile needs to meet fixed running time. Therefore, this paper focuses on a new method for determining the fixed running time complied with the scheduled timetable when trains track the optimal speed profile. The novel method to ensure the fixed running time is the numerical-analytical one. Calculating accelerating time ta, coasting time tc, braking time tb via values of holding speed vh, braking speed vb of optimal speed profile with the constraint condition: the running time equal to the demand time. The other hands, vh and vb are determined by solving nonlinear equations with constraint conditions. Additionally, changing running time suit for each operation stage of metro lines or lines starting to conduct schedules by the numerical-analytical method is quite easy. Simulation results obtained for two scenarios with data collected from electrified trains of Cat Linh-Ha Dong metro line, Vietnam show that running time complied with scheduled timetables, energy saving by tracking optimal speed profile for the entire route is up to 8.7%, if the running time is one second longer than original time, energy saving is about 11.96%

    Cooperative control of high-speed trains for headway regulation: A self-triggered model predictive control based approach

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    The advanced train-to-train and train-to-ground communication technologies equipped in high-speed railways have the potential to allow trains to follow each with a steady headway and improve the safety and performance of the railway systems. A key enabler is a train control system that is able to respond to unforeseen disturbances in the system (e.g., incidents, train delays), and to adjust and coordinate the train headways and speeds. This paper proposes a multi-train cooperative control model based on the dynamic features during train longitude movement to adjust train following headway. In particular, our model simultaneously considers several practical constraints, e.g., train controller output constraints, safe train following distance, as well as communication delays and resources. Then, this control problem is solved through a rolling horizon approach by calculating the Riccati equation with Lagrangian multipliers. Due to the practical communication resource constraints and riding comfort requirement, we also improved the rolling horizon approach into a novel self-triggered model predictive control scheme to overcome these issues. Finally, two case studies are given through simulation experiments. The simulation results are analyzed which demonstrate the effectiveness of the proposed approach

    Importance and applications of robotic and autonomous systems (RAS) in railway maintenance sector: a review

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    Maintenance, which is critical for safe, reliable, quality, and cost-effective service, plays a dominant role in the railway industry. Therefore, this paper examines the importance and applications of Robotic and Autonomous Systems (RAS) in railway maintenance. More than 70 research publications, which are either in practice or under investigation describing RAS developments in the railway maintenance, are analysed. It has been found that the majority of RAS developed are for rolling-stock maintenance, followed by railway track maintenance. Further, it has been found that there is growing interest and demand for robotics and autonomous systems in the railway maintenance sector, which is largely due to the increased competition, rapid expansion and ever-increasing expense

    Data-driven approaches for modeling train control models: Comparison and case studies

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    In railway systems, the train dynamics are usually affected by the external environment (e.g., snow and wind) and wear-out of on-board equipment, leading to the performance degradation of automatic train control algorithms. In most existing studies, the train control models were derived from the mechanical analyzation of train motors and wheel-track frictions, which may require many times of field trials and high costs to validate the model parameters. To overcome this issue, we record the explicit train operation data in Beijing Metro within three years and develop three data-driven approaches, involving a linear regression-based model (LAM), a nonlinear regression-based model (NRM), and furthermore a deep neural network based (DNN) model, where the LAM and NRM can act as benchmarks for evaluating DNN. To improve the training efficiency of DNN model, we especially customize the input and output layers of DNN, batch normalization based layers and network parameter initialization techniques according to the unique characteristics of railway train models. From the model training and testing results with field data, we observe that DNN significantly enhances the predicting accuracy for the train control model by using our customized network structure compared with LAM and NRM models. These data-driven approaches are successfully applied to Beijing Metro for designing efficient train control algorithms

    Applications of Genetic Algorithm and Its Variants in Rail Vehicle Systems: A Bibliometric Analysis and Comprehensive Review

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    Railway systems are time-varying and complex systems with nonlinear behaviors that require effective optimization techniques to achieve optimal performance. Evolutionary algorithms methods have emerged as a popular optimization technique in recent years due to their ability to handle complex, multi-objective issues of such systems. In this context, genetic algorithm (GA) as one of the powerful optimization techniques has been extensively used in the railway sector, and applied to various problems such as scheduling, routing, forecasting, design, maintenance, and allocation. This paper presents a review of the applications of GAs and their variants in the railway domain together with bibliometric analysis. The paper covers highly cited and recent studies that have employed GAs in the railway sector and discuss the challenges and opportunities of using GAs in railway optimization problems. Meanwhile, the most popular hybrid GAs as the combination of GA and other evolutionary algorithms methods such as particle swarm optimization (PSO), ant colony optimization (ACO), neural network (NN), fuzzy-logic control, etc with their dedicated application in the railway domain are discussed too. More than 250 publications are listed and classified to provide a comprehensive analysis and road map for experts and researchers in the field helping them to identify research gaps and opportunities

    Optimization of the Suburban Railway Train Operation Plan Based on the Zonal Mode

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    Traditional all-stop train operation mode cannot meet the demand of long travel distance and centralized travel of commuters very well. To meet this special travel demand, a zonal train operation mode based on “many-to-many” train stops is proposed. The coefficient of passenger exchange is used to locate suburban areas by depicting travel characteristics of commuters. Operational separating points within the suburban area are used as decision variables to analyze the combined cost components of this model, including passenger travel costs and railway operating costs. An integer programming model with the lowest overall cost is established, and the genetic algorithm is employed to solve it. The results proved good relative benefits in operation costs and travel time. And the sensitivity analysis of both coefficient of passenger exchange and passenger intensity has shown that the zonal operation mode is suitable for suburban railways with centralized travelers. However, the research also shows that when the passenger volume rose to a very high level, the number of zones would be limited by the maximized capacity of railway lines, which may cause the decline of the relative operational efficiency

    Metro systems : Construction, operation and impacts

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