High-speed Railway最新文献

During railway operations, trains normally run as scheduled, but the occurrence of unexpected events will disrupt traffic flow and cause train deviation from the original timetable. In order to assist dispatchers in rescheduling trains, this paper introduces an innovative Human-Computer Interaction framework. This framework enables train dispatchers to propose different timetable adjustment instructions to the original or adjusted timetable. These instructions will be processed, stored, analyzed, and digested by computer program, which finally lead to the modification and calculation of the embedded mathematical model, then a new adjusted timetable will be produced and provided to dispatchers for checking and modifying. This framework can iterate for unlimited times based on dispatchers’ intentions, until the final results satisfy them. A demonstration system named RTARS (Real-time Timetable Automatic Rescheduling System) is developed based on this framework and it has been applied in Beijing Railway Administration, which shows its effectiveness in reality.

Foamed Polyurethane Solidified Ballasted Track (FPSBT), an innovative railway track, is formed by solidifying ballast bed with foamed polyurethane. Compared with the traditional Discrete Ballasted Track (DBT), FPSBT does not require regular maintenance such as tamping and cleaning. However, limited studies exist on the mechanical properties of FPSBT. In this study, Laboratory experiments are conducted on polyurethane samples to investigate the effects of polyurethane density on the mechanical properties of FPSBT. Furthermore, the performance of DBT and FPSBT with different polyurethane densities are compared, and the recommended polyurethane density is obtained. FPSBT exhibited negligible accumulation of deformation under cyclic loads, indicating excellent performance of FPSBT owing to the anti-deformation properties of polyurethane. Further, a track load vehicle test is conducted. FPSBT exhibited better load-transmitting ability than DBT. Finally, the construction and application of FPSBT in China are introduced. This study is expected to contribute to realizing a more extensive application of FPSBT.

Railway real estate is the fundamental element of railway transportation production and operation. Effective management and rational utilization of railway real estate is essential for railway asset operation. Based on the investigation of the requirements of railway real estate management and operation, combined with Beidou positioning, GIS (Geographic Information System), multi-source data fusion and other cutting-edge technologies, this paper puts forward the multi-dimensional dynamic statistical method of real estate information, the identification method of railway land occupation and the comprehensive evaluation method of real estate development and utilization potential, and build the railway real estate supervision and operation platform, design the function of the platform, so as to provide intelligent solutions for the railway real estate operation.

Nowadays, high mobility scenarios have become increasingly common. The widespread adoption of High-speed Rail (HSR) in China exemplifies this trend, while more promising use cases, such as vehicle-to-everything, continue to emerge. However, the Internet access provided in high mobility environments still struggles to achieve seamless connectivity. The next generation of wireless cellular technology 5 G further poses more requirements on the end-to-end evolution to fully utilize its ultra-high band-width, while existing network diagnostic tools focus on above-IP layers or below-IP layers only. We then propose HiMoDiag, which enables flexible online analysis of the network performance in a cross-layer manner, i.e., from the top (application layer) to the bottom (physical layer). We believe HiMoDiag could greatly simplify the process of pinpointing the deficiencies of the Internet access delivery on HSR, lead to more timely optimization and ultimately help to improve the network performance.

The complex bridge-track interaction between kilometer-span bridges and continuous Welded Rail (CWR) brings great challenges to CWR designing. Taking a suspension bridge with laying CWR as a case, the mechanical properties of CWR on the bridge are analyzed to reveal the sensitive areas of the track, and the design method of CWR and track structures on the beam ends are proposed. The results show that the unidirectional Rail Expansion Joints(REJ) need to be installed on the beam end of the kilometer-span bridge to reduce rail longitudinal force. Due to the bridge characteristics, there is no CWR fixed area on the kilometer-span bridge, and rail longitudinal force on the main span caused by bending loads needs to be concerned. The deformation of track on the beam end is complex, which is the weak area on the kilometer bridge, the large relative displacement between the stock rail of REJ and the main beam can cause poor stability of ballast bed on beam end, small resistance fasteners need to be laid on the sides of stock rail on the main beam to increase the stability of ballast and fasteners on the beam end. To improve the driving safety and comfort of beam end, the Sleeper-Supporting Apparatus (SSA) should be specially designed to ensure the uniform transition of track on beam ends. Temperature and wind loads have a significant impact on track regularity on the kilometer span bridge, the dynamic response of trains and bridges under those loads needs to be attended to.

Railway Point System (RPS) is an important infrastructure in railway industry and its faults may have significant impacts on the safety and efficiency of train operations. For the fault diagnosis of RPS, most existing methods assume that sufficient samples of each failure mode are available, which may be unrealistic, especially for those modes of low occurrence frequency but with high risk. To address this issue, this work proposes a novel fault diagnosis method that only requires the power signals generated under normal RPS operations in the training stage. Specifically, the failure modes of RPS are distinguished through constructing a reasoning diagram, whose nodes are either binary logic problems or those that can be decomposed into the problems of the binary logic. Then, an unsupervised method for the signal segmentation and a fault detection method are combined to make decisions for each binary logic problem. Based on the results of decisions, the diagnostic rules are established to identify the failure modes. Finally, the data collected from multiple real-world RPSs are used for validation and the results demonstrate that the proposed method outperforms the benchmark in identifying the faults of RPSs.

Image fusion refers to extracting meaningful information from images of different sources or modalities, and then fusing them to generate more informative images that are beneficial for subsequent applications. In recent years, the growing data and computing resources have promoted the development of deep learning, and image fusion technology has continued to spawn new deep learning fusion methods based on traditional fusion methods. However, high-speed railroads, as an important part of life, have their unique industry characteristics of image data, which leads to different image fusion techniques with different fusion effects in high-speed railway scenes. This research work first introduces the mainstream technology classification of image fusion, further describes the downstream tasks that image fusion techniques may combine within high-speed railway scenes, and introduces the evaluation metrics of image fusion, followed by a series of subjective and objective experiments to completely evaluate the performance level of each image fusion method in different traffic scenes, and finally provides some possible future image fusion in the field of rail transportation of research.

Further improving the railway innovation capacity and technological strength is the important goal of the 14th Five-Year Plan for railway scientific and technological innovation. It includes promoting the deep integration of cutting-edge technologies with the railway systems, strengthening the research and application of intelligent railway technologies, applying green computing technologies and advancing the collaborative sharing of transportation big data. The high-speed rail system tasks need to process huge amounts of data and heavy workload with the requirement of ultra-fast response. Therefore, it is of great necessity to promote computation efficiency by applying High Performance Computing (HPC) to high-speed rail systems. The HPC technique is a great solution for improving the performance, efficiency, and safety of high-speed rail systems. In this review, we introduce and analyze the application research of high performance computing technology in the field of high-speed railways. These HPC applications are cataloged into four broad categories, namely: fault diagnosis, network and communication, management system, and simulations. Moreover, challenges and issues to be addressed are discussed and further directions are suggested.

With the development of High-Speed Rail (HSR), countries and individual passengers alike have enjoyed far ranging benefits as a result – economic, social, environment and in added convenience. One of the important parts of HSR construction is the signaling system, where wireless communications play a key role in the transmission of train control data. Channel estimation has a significant impact on the quality of the wireless communication, however, whose performance is degraded due to the fast mobility of HSR. This paper focuses on the channel estimation technology in HSR. We first summarize the key challenges for HSR channel estimation, especially the Inter-Carrier Interference (ICI) faced by Orthogonal Frequency Division Multiplexing (OFDM) systems. Then we provide a comprehensive review of existing pilot-aided channel estimation schemes from three points: channel model, estimation algorithm, joint channel estimation and ICI mitigation schemes. Lastly, we present the challenges of channel estimation for the Orthogonal Time Frequency Space (OTFS) system and Reconfigurable Intelligent Surface (RIS), which are promising techniques for HSR systems in the future sixth Generation (6G) wireless communication.

The application of Global Navigation Satellite Systems (GNSSs) in the intelligent railway systems is rapidly developing all over the world. With the GNSS-based train positioning and moving state perception, the autonomy and flexibility of a novel train control system can be greatly enhanced over the existing solutions relying on the track-side facilities. Considering the safety critical features of the railway signaling applications, the GNSS stand-alone mode may not be sufficient to satisfy the practical requirements. In this paper, the key technologies for applying GNSS in novel train-centric railway signaling systems are investigated, including the multi-sensor data fusion, Virtual Balise (VB) capturing and messaging, train integrity monitoring and system performance evaluation. According to the practical characteristics of the novel train control system under the moving block mode, the details of the key technologies are introduced. Field demonstration results of a novel train control system using the presented technologies under the practical railway operation conditions are presented to illustrate the achievable performance feature of autonomous train state perception using BeiDou Navigation Satellite System (BDS) and related solutions. It reveals the great potentials of these key technologies in the next generation train control system and other GNSS-based railway implementations.