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This research introduces a hybrid deep learning approach to perform real-time forecasting of passenger traffic flow for the metro railway system (MRS). By integrating long short-term memory (LSTM) and the graph convolutional network (GCN), a hybrid deep learning neural network named the graph convolutional memory network (GCMN) was constructed and trained for accurate real-time prediction of passenger traffic flow for the MRS. Data collected of the traffic flow in Delhi’s metro rail network system in the period from October 2012 to May 2017 were utilized to demonstrate the effectiveness of the developed model. The results indicate that (1) the developed method provides accurate predictions of the traffic flow with an average coefficient of determination (R2) of 0.920, RMSE of 368.364, and MAE of 549.527, and (2) the GCMN model outperforms state-of-the-art methods, including LSTM and the light gradient boosting machine (LightGBM). This study contributes to the state of practice in proposing a novel framework that provides reliable estimations of passenger traffic flow. The developed model can also be used as a benchmark for planning and upgrading works of the MRS by metro owners and architects.

The railway signalling system is a key player in controlling train traffic, with the purpose of guaranteeing safety while respecting transport demand. For rail vehicles, visual-based driving is unfeasible, and thus, railway signalling systems have been developed as answers to this issue. Moreover, the increasing demand for railway traffic requires improved signalling system performances. Recent developments have led to the theoretical definition of the Moving Block signalling system as an approach to overcome the limitations of the Fixed Block system, currently adopted in many applications worldwide. This paper focuses on the development of a modular time-based simulation tool for Fixed Block and Moving Block railway signalling systems. The simulator incorporates all main elements for the assessment of signalling system’s performances, including the model of the Radio Block Centre, used for the communication among the trains, the On-Board Unit, which generates the reference speed profile, the vehicle longitudinal dynamics, and the speed control algorithm, emulating a human driver’s behaviour. Different operational conditions have been considered to show the capabilities of the simulator, which may represent a first step towards the reduction of on-site testing of railway signalling systems.

Railway point devices act as actuators that provide different routes to trains by driving switchblades from the current position to the opposite one. Point failure can significantly affect railway operations, with potentially disastrous consequences. Therefore, early detection of anomalies is critical for monitoring and managing the condition of rail infrastructure. We present a data mining solution that utilizes audio data to efficiently detect and diagnose faults in railway condition monitoring systems. The system enables extracting mel-frequency cepstrum coefficients (MFCCs) from audio data with reduced feature dimensions using attribute subset selection, and employs support vector machines (SVMs) for early detection and classification of anomalies. Experimental results show that the system enables cost-effective detection and diagnosis of faults using a cheap microphone, with accuracy exceeding 94.1% whether used alone or in combination with other known methods.

We estimate the effects of interstate highways on the growth of U.S. cities between 1983 and 2003. We find that a 10% increase in a city's initial stock of highways causes about a 1·5% increase in its employment over this 20 year period. To estimate a structural model of urban growth and transportation, we rely on an instrumental variables estimation that uses a 1947 plan of the interstate highway system, an 1898 map of railroads, and maps of the early explorations of the U.S. as instruments for 1983 highways.

Nowadays, new challenges arise relating to the compensation of power quality problems, where the introduction of innovative solutions based on power electronics is of paramount importance. The evolution from conventional electrical power grids to smart grids requires the use of a large number of power electronics converters, indispensable for the integration of key technologies, such as renewable energies, electric mobility and energy storage systems, which adds importance to power quality issues. Addressing these topics, this paper presents an extensive review on power electronics technologies applied to power quality improvement, highlighting, and explaining the main phenomena associated with the occurrence of power quality problems in smart grids, their cause and effects for different activity sectors, and the main power electronics topologies for each technological solution. More specifically, the paper presents a review and classification of the main power quality problems and the respective context with the standards, a review of power quality problems related to the power production from renewables, the contextualization with solid-state transformers, electric mobility and electrical railway systems, a review of power electronics solutions to compensate the main power quality problems, as well as power electronics solutions to guarantee high levels of power quality. Relevant experimental results and exemplificative developed power electronics prototypes are also presented throughout the paper.

The design of modern industrial products is further improved through the hardware-in-the-loop (HIL) simulation. Realistic simulation is enabled by the closed loop between the hardware under test (HUT) and real-time simulation. Such a system involves a field programmable gate array (FPGA) and digital signal processor (DSP). An HIL model can bypass serious damage to the real object, reduce debugging cost, and, finally, reduce the comprehensive effort during the testing. This paper provides a historical overview of HIL simulations through different engineering challenges, i.e., within automotive, power electronics systems, and different industrial drives. Various platforms, such as National Instruments, dSPACE, Typhoon HIL, or MATLAB Simulink Real-Time toolboxes and Speedgoat hardware systems, offer a powerful tool for efficient and successful investigations in different fields. Therefore, HIL simulation practice must begin already during the university’s education process to prepare the students for professional engagements in the industry, which was also verified experimentally at the end of the paper.

This paper addresses the scheduled service network design problem for freight rail transportation. The proposed model integrates service selection and scheduling, car classification and blocking, train makeup, and routing of time-dependent customer shipments based on a cyclic three-layer space-time network representation of the associated operations and decisions and their relations and time dimensions. This paper also proposes a matheuristic solution methodology integrating slope scaling, a dynamic block-generation mechanism, long-term-memory-based perturbation strategies, and ellipsoidal search, a new intensification mechanism to thoroughly explore very large neighborhoods of elite solutions restricted using information from the history of the search. Experimental results show that the proposed solution method is efficient and robust, yielding high-quality solutions for realistically sized problem instances.

This work aims to cover the measurement, modeling, and analysis of waveform distortions in railway power systems. It is focused on waveform distortion as a phenomenon that includes harmonic distortion, interharmonic distortion, and supraharmonics. A comprehensive view of the interactions of waveform distortions in railway systems is needed, together with a grid perspective of power quality incorporating all aspects, sources, propagation, requirements, and effects. It is understood that the communities interested or involved in the subject of railway power systems would benefit from an integrated overview of the literature on the complex problem of waveform distortion. The literature review is divided into four categories: characterization and measurements, modeling, the application of artificial intelligence, and specific issues. For each category of work, the contributions are highlighted, and a discussion on opportunities, gaps, and critical observations is provided. The work successfully builds a framework for the subject with two main characteristics; the review is informative and propositional, providing a road map of opportunities for future works. Some aspects and recommendations can be highlighted. Suggestions for future works and research practices on waveform distortion in electrical transportation are offered.

Verification and Validation (V&V) activities aiming at certifying railway controllers are among the most critical and time-consuming in system development life cycle. As such, they would greatly benefit from novel approaches enabling both automation and traceability for assessment purposes. While several formal and Model-Based approaches have been proposed in the scientific literature, some of which are successfully employed in industrial settings, we are still far from an integrated and unified methodology which allows guiding design choices, minimizing the chances of failures/non-compliances, and considerably reducing the overall assessment effort. To address these issues, this paper describes a Model-Driven Engineering approach which is very promising to tackle the aforementioned challenges. In fact, the usage of appropriate Unified Modeling Language profiles featuring system analysis and test case specification capabilities, together with tool chains for model transformations and analysis, seems a viable way to allow end-users to concentrate on high-level holistic models and specification of non-functional requirements (i.e., dependability) and support the automation of the V&V process. We show, through a case study belonging to the railway signalling domain, how the approach is effective in supporting activities like system testing and availability evaluation. © 2014, Springer-Verlag Berlin Heidelberg.

This study aims to explore the optimal driving speed for ground vibration in suburban railway underground sections. We focused on the ground surface of suburban railway underground sections and developed a 3D finite element dynamic coupling model for the tunnel–soil system. Subsequently, considering factors such as train speed and passenger load, we analyzed the propagation characteristics of ground vibration responses in urban railway underground sections. The research results indicate a significant amplification phenomenon in the peak power spectrum of measurement points near the tunnels in underground sections. The high-frequency components of the power spectrum between measurement points are noticeably higher between the two tunnels. Furthermore, as the train speed increases, this amplification phenomenon becomes more pronounced, and the power spectrum of each measurement point mainly concentrates on several frequency bands, with the amplitude of the power spectrum near the prominent frequencies also increasing. However, when the train speed is between 100 and 120 km/h, the impact on the amplitude of the power spectrum at measurement points above the running tunnel is minimal. Additionally, the amplitude of the middle-to-high frequency components in the power spectrum increases with the increase in passenger numbers. The impact on the peak acceleration amplitude at each measurement point is minimal when the train speed is 80 km/h or below. However, once the train speed exceeds 80 km/h, the peak acceleration amplitude above the running tunnel rapidly increases, reaching its maximum value at 113 km/h, and then gradually decreasing.