High-speed Railway Environment Research Articles

STNet: A Time-Frequency Analysis-Based Intrusion Detection Network for Distributed Optical Fiber Acoustic Sensing Systems.

Distributed optical fiber acoustic sensing (DAS) is promising for long-distance intrusion-anomaly detection tasks. However, realistic settings suffer from high-intensity interference noise, compromising the detection performance of DAS systems. To address this issue, we propose STNet, an intrusion detection network based on the Stockwell transform (S-transform), for DAS systems, considering the advantages of the S-transform in terms of noise resistance and ability to detect disturbances. Specifically, the signal detected by a DAS system is divided into space-time data matrices using a sliding window. Subsequently, the S-transform extracts the time-frequency features channel by channel. The extracted features are combined into a multi-channel time-frequency feature matrix and presented to STNet. Finally, a non-maximum suppression algorithm (NMS), suitable for locating intrusions, is used for the post-processing of the detection results. To evaluate the effectiveness of the proposed method, experiments were conducted using a realistic high-speed railway environment with high-intensity noise. The experimental results validated the satisfactory performance of the proposed method. Thus, the proposed method offers an effective solution for achieving high intrusion detection rates and low false alarm rates in complex environments.

3DGraphSeg: A Unified Graph Representation- Based Point Cloud Segmentation Framework for Full-Range High-Speed Railway Environments

Point cloud semantic segmentation (PCSS) is crucial for digital twins of high-speed railways. By now, the concerned subjects are confined within the interior infrastructures of railways. However, the surrounding environments are also important for the safe operation. Concerning this issue, a full-range high-speed railway scanning scheme based on UAV borne LiDAR is utilized. However, the massive data volume and data distribution imbalance pose great challenges for PCSS. To address these issues, a novel PCSS framework called 3DGraphSeg is proposed in this paper. To cope with the massive data volume, a structural representation algorithm named Local Embedding Super-Point Graph (LE-SPG) is proposed to represent the vast point cloud into a concise graph while retain the data's inherent topology structure by local spatial embedding. Then, the Gated Integration Graph Convolutional Network (GIGCN) is proposed to contextual segment the graph. In GIGCN, to prevent the gradients from vanishing or exploding, the hidden states of Gated Recurrent Units (GRUs) in every layer are integrated using a new layer named Gated Hidden States Integration (GHSI). GHSI strengthens the back propagation by giving the loss function direct access to each layer and absorbs the features of different layers comprehensively, which enables the network to produce a smoother decision boundary and prevents the overfitting problem. Besides, to enhance its robustness to data imbalance, we propose a loss function: Adaptive Weighted Cross Entropy (AWCE). Finally, five experiments are designed for verification. The proposed framework has excelled in different datasets and outperforms state-of-the-art approaches on the SemanticRail dataset.

Hierarchical Deep Reinforcement Learning for Self-Powered Monitoring and Communication Integrated System in High-Speed Railway Networks

To align with the vision of future intelligent high-speed railway (HSR) networks, integrating sensor monitoring and remote communication are challenging for ensuring the lightweight of train equipment, high-quality transmissions, and dynamic interaction between monitoring and communication. In this paper, we propose a self-powered multisensor monitoring and communication integrated system in HSR. A low-power backscatter communication working framework of the self-powered monitoring system is designed in the monitoring network model, and a finite Gaussian mixture model (GMM) clustering method is used to analyze the communication cell coverage area in the communication network model. Aiming to minimize the total task completion time, we formulate a data monitoring and remote communication problem with the energy transfer constraint, data collection constraint, and transmission data rate constraint. As for the non-convex minimum time optimization problem, we develop a novel option-based hierarchical deep reinforcement learning (OHDRL) method to deal with the complex continuous variation characteristics of the monitoring and communication integrated HSR system. The system learns to select options at a high level, and the action is executed according to the policy of the selected option at a low level. This approach enables us to handle stochastic HSR environments, closed-loop policies, and goals in a temporal abstraction way. Numerical results reveal that the proposed algorithm for the integrated monitoring and communication HSR achieves a significantly higher reward and more stable learning performance than other algorithms in the literature.

RIS-Aided Hybrid Precoder Design With Anti-Blocking Scheme in High-Speed Railway

Reconfigurable intelligent surface (RIS)-aided high-speed railway (HSR) communication system is an efficient and promising way to slove the signal blocking by tuning passive signal reflection or refraction. However, the complexity HSR environment lead unstable communication links and low quality of service. Therefore, this paper studies a RIS-aided hybrid precoding for anti-blocking HSR network to enhance the sum rate of mobile relays when signal blocking. To describe the blocking scenes clearly, the blocking model is established and the closed form of the blocking probability is derived. Based on the blocking model, the non-convex blocking optimal problem is decoupled by the block coordinate descent method with the jointly optimization problem about hybrid precoder and reflect angle matrix of RIS. Then, the successive convex approximation and prox-linear method are utilized to solve the constant modulus constraint of RIS and hybrid precoding constraints. Simulation results show that the proposed RIS-aided anti-blocking hybrid precoding scheme can achieve higher sum rate than the traditional methods in blockage scenarios. Moreover, the proposed algorithm brings better performance of blocking probability and outage probability in high-speed scenarios.

Exploring the Public Health of Travel Behaviors in High-Speed Railway Environment during the COVID-19 Pandemic from the Perspective of Trip Chain: A Case Study of Beijing-Tianjin-Hebei Urban Agglomeration, China.

The outbreak and spreading of COVID-19 since early 2020 have dramatically impacted public health and the travel environment. However, most of the studies are devoted to travel behavior from the macro perspective. Meanwhile, few researchers pay attention to intercity travel behavior. Thus, this study explores the changes in the travel behavior of intercity high-speed railway travelers during the COVID-19 pandemic from the perspective of the individual. Using the smartphone data, this study first extracts the trip chains by proposing a novel method including three steps. The trip chain can describe the whole process of traveling, including individual characteristics, travel time, travel distance, travel mode, etc. Then, a Multinomial Logit model is applied to analyze the trip chains which verified the validity by using studentized residual error. The study finds that intercity travel behavior has changed in gender, age, travel mode choice, and travel purpose by comparing the trip chains between May 2019 and May 2021 in the Beijing-Tianjin-Hebei urban agglomeration. The method proposed in this study can be used to assess the impact of any long-term emergency on individual travel behavior. The findings proposed in this study are expected to guide public health management and travel environment improvement under the situation of normalized COVID-19 prevention and safety control.

Influencing factors and mechanism of high-speed railway passenger overall comfort: Insights from source functional brain network and subjective report.

Overall comfort is the priority for the high-speed railway (HSR) passengers, while its influencing factors and mechanism are not yet apparent. According to the source functional brain network and subjective report, this study revealed the potential influencing factors and mechanisms of passengers overall comfort in high-speed railway environments. Here, an ergonomics field test with 20 subjects was conducted where subjective reports and electroencephalography (EEG) were collected. The electric-source imaging and functional connectivity were used to build the source functional brain network from EEG and network indices were extracted. Statistics analysis results showed that static comfort played the most critical role in the overall comfort, followed by emotional valence, emotional arousal, aural pressure comfort, vibration comfort, and noise comfort. Thermal and visual comfort were insignificant due to the well-designed heating, ventilation, and air conditioning (HVAC) and lighting system of HSR. In addition, the source functional brain network of passengers who felt uncomfortable had the higher clustering coefficient, assortativity coefficient and global efficiency, which meant greater activation of brain compared with passengers who were in a state of comfort. According to the local attributes indices analysis, most key brain regions were located in the frontal and hippocampus, which revealed emotion and spatial perception contribute to the whole comfort degradation process. This work proposed novel insights into HSR passengers overall comfort according to subjective and objective methods. Our findings demonstrate emotional regulation and seat improvements are key factors for future improvement of HSR passengers overall comfort.

Blind field tests to determine Rayleigh wave velocity on a high-speed railway environment: The reliability of seismic surface waves methods

To ensure track stability and good earthwork performance for high-speed railways the surface wave velocity in the earthworks should exceed the train’s speed. Specifications for high-speed rail are now stating minimum Rayleigh wave (VR) velocities to be checked during construction. This requires suitable reliable geophysical tests, however there are no defined standards or data collection and processing protocols. Additionally, the analysis of these data traditionally relies on the practice and experience of those undertaking the work which can introduce variability in results.This paper presents the results of a blind comparison trial investigating Multichannel Analysis of Surface Waves test (MASW) for use in high-speed rail earthworks compliance evaluation, concentrating on assessment of Rayleigh wave velocity (VR). Testing was undertaken by four companies, at the same site on natural ground and a stabilised trial embankment. Defined tests protocols and a test of their own design against a specification to assess VR were used. The anonymised VR results show reasonable agreement in the dispersive character of the soil if higher modes are carefully considered when picking a dispersion curve. The VR results were then investigated against depth using a rule of thumb. Such an approach avoids the inversion processing step (to get more traditional Vs against depth) which potentially introduces variability. This suggests that direct Rayleigh wave data could be used by earthworks engineers to give routine compliance assessment and then if required further investigation undertaken in areas of compliance concern within the overall earthwork.

QoS-Driven Resource Optimization for Intelligent Fog Radio Access Network: A Dynamic Power Allocation Perspective

The fog radio access network (Fog-RAN) has been considered a promising wireless access architecture to help shorten the communication delay and relieve the large data delivery burden over the backhaul links. However, limited by conventional inflexible communication design, Fog-RAN cannot be used in some complex communication scenarios. In this study, we focus on investigating a more intelligent Fog-RAN to assist the communication in a high-speed railway environment. Due to the train’s continuously moving, the communication should be designed intelligently to adapt to channel variation. Specifically, we dynamically optimize the power allocation in the remote radio heads (RRHs) to minimize the total network power cost considering multiple quality-of-service (QoS) requirements and channel variation. The impact of caching on the power allocation is considered. The dynamic power optimization is analyzed to obtain a closed-form solution in certain cases. The inherent tradeoff among the total network cost, delay and delivery content size is further discussed. To evaluate the performance of the proposed dynamic power allocation, we present an invariant power allocation counterpart as a performance comparison benchmark. The result of our simulation reveals that dynamic power allocation can significantly outperform the invariant power allocation scheme, especially with a random caching strategy or limited caching resources at the RRHs.

When mmWave High-Speed Railway Networks Meet Reconfigurable Intelligent Surface: A Deep Reinforcement Learning Method

Reconfigurable intelligent surface (RIS) is an efficient and promising way to enhance millimeter-wave (mmWave) high-speed railway (HSR) network resilience. However, due to the harsh nature of HSR environment and the unpredictable hardware damage of RIS, it is challenging to perceive accurate and complete channel state information (CSI). To tackle the challenge, this letter proposes a novel deep reinforcement learning framework to design the base station (BS) transmission beamforming and RIS phase shifts for the RIS aided mmWave HSR networks, which combines long short-term memory (LSTM) and deep deterministic policy gradient (DDPG), termed as LSTM-DDPG. The simulation results demonstrate that the proposed LSTM-DDPG scheme outperforms the benchmark schemes in terms of spectral efficiency with comparatively low execution time, which makes real-time decision-making truly viable in the dynamic HSR network.

Passenger overall comfort in high-speed railway environments based on EEG: Assessment and degradation mechanism

The overall comfort of train passenger is influenced by many environmental factors such as vibration, noise and pressure. However, the couple effect of these influencing factors causes the difficulty in evaluating the overall comfort. This study revealed the potential comfort degradation mechanisms in high-speed railway environments and proposed a machine learning evaluation model to assess passenger comfort. Here, the subjective overall comfort ratings and the electroencephalography (EEG) of twenty passengers were collected in the field tests. Compared with passengers who were in a state of comfort, the brain areas (BA6/13/20/24/31/40/47) of passengers who felt uncomfortable were all significantly activated in the beta band. Based on the neural signature above, three related human reactions when passengers feel uncomfortable were recognized, including perceiving the environment, inducing negative emotions, and finally producing body movement intention. To assess the overall comfort of train passengers, six kinds of features extracted from EEG signals were used to train an evaluation model based on the LightGBM algorithm. This work offers a neurological explanation for the mechanisms of degradation of overall comfort and provides a novel and effective method to assess it.

Correlation Analysis of External Environment Risk Factors for High-Speed Railway Derailment Based on Unstructured Data

In railway operation, unsafe events such as faults may occur, and a large number of unsafe event records are generated in the process of unsafe events’ recording and reporting. Unsafe events have been described in unstructured natural language, which often has inconsistent structure and complex sources, involving multiple railway specialties, with multisource, heterogeneous, and unstructured characteristics. In practical application, the efficiency of processing is extremely low, leading to potentially unsafe management utilization. Based on the data on unsafe events, this paper utilizes big data processing technology, conducts association rules mining and association degree analysis, extracts the word segmentation, and obtains the feature vector of unsafe fault event data. At the same time, the unsafe event data analysis model is constructed in combination with regular expression and pattern matching technology. This paper establishes the matching model of high-speed railway derailment-based external environment risk factors and applies it to the occurrence of unsafe events. This model could be utilized to analyze and excavate the link between external environment risk factors and the occurrence of unsafe events and carry out the automatic extraction of characteristic information such as risk possibility and consequence severity; hence, it has potential for identifying, with enhanced accuracy, high-risk factors that may lead to high-speed railway derailment. Based on this study, we could make full use of the unsafe event data, forecast the risk trend, and discover the law of high-speed railway derailment. This study introduces a viable approach to analyzing the unsafe event data, forecasting risk trend, and conceptualizing high-speed railway derailment. It could also enforce the accurate quantification of high-speed railway safety situation, refine the risk index and conduct in-depth analysis combined with the model, and effectively support the digitalization and intellectualization of high-speed railway operation safety.

Optimization of Time–Frequency Resource Management Based on Probabilistic Graphical Models in Railway Internet-of-Things Networking

As the high-speed railway (HSR) industry Internet-of-Things chain matures, HSR wireless communication technology has become an increasingly important research field. The efficient management of time–frequency resources for Internet-of-Things networking is the core issue of HSR wireless communication optimization. The lack of time–frequency resources in LTE-R is still severe. In this article, a new LTE-R time–frequency resource allocation optimization method based on the probabilistic graphical theory is proposed. Considering the regularity that high-speed trains always pass by the same geographical location in similar time periods, we can do some research on opportunistic spectrum accessibility in the existing LTE time–frequency resource algorithm. The probabilistic graphical theory is suitable for finding the appropriate communication access opportunity in an HSR environment. The simulation results show that our method can effectively improve the performance of various traditional LTE time–frequency resource allocation algorithms.

A Method to Supervise the Effect on Railway Radio Transmission of Pulsed Disturbances Based on Joint Statistical Characteristics

Radiocommunication systems between train and trackside (RSTT) carry essential information for train operations between on-board radio equipment and the related radio infrastructure located along the trackside, such as the train control, voice dispatching, command, operational information as well as the monitoring data. In a high-speed railway environment, the electromagnetic interference (EMI) has been a major threat to RSTT, and may result in critical security issues for railway transportation and even passengers. Given the complex scenario of the high-speed railway, it is significant to monitor the impact of disturbances in order to guarantee the quality of RSTT. On one hand, RSTT operate in a complex electromagnetic environment where transient disturbances coexist with permanent ones, and they both vary dramatically while the train is running. On the other hand, various radiocommunication technologies have been used for railway applications, featuring forward error-correction codes to resist EMI. Therefore, this paper puts forward a novel approach to evaluate the impact on radio transmission based on the joint statistical characteristics of time-varying EMI. This approach applies a dynamic effective signal-to-interference-plus-noise ratio mapping model to establish the relation between the block error performance of on-board radio and the joint statistical characteristics of disturbances with a mutual information-based metric. Simulations on radio transmission using Turbo coding and low-density-parity-check coding under various interferences indicate that this approach is effective to evaluate the degradation of transmission signal with forward error-correction coding due to EMI with different characteristics.

Satellite‐Terrestrial Channel Characterization in High‐Speed Railway Environment at 22.6 GHz

AbstractThe integration of satellite and terrestrial communication systems plays a vital role in the fifth‐generation mobile communication system (5G) for the ubiquitous coverage, reliable service, and flexible networking. Moreover, the millimeter wave (mmWave) communication with large bandwidth is a key enabler for 5G intelligent rail transportation. In this paper, the satellite‐terrestrial channel at 22.6 GHz is characterized for a typical high‐speed railway (HSR) environment. The three‐dimensional model of the railway scenario is reconstructed and imported into the Cloud Ray‐Tracing (CloudRT) simulation platform. Based on extensive ray‐tracing simulations, the channel for the terrestrial HSR system and the satellite‐terrestrial system with two weather conditions are characterized, and the interference between them are evaluated. The results of this paper can help for the design and evaluation for the satellite‐terrestrial communication system enabling future intelligent rail transportation.

A study of modeling experiments of the vibration behavior of elevated railway box girder

Based on the model similarity theory, this article deduces the model similarity relationship of the elevated railway box girder at the elastic stage and designs a 1/10 box girder scale model by adopting a 32 m simply-supported box girder bridge from the Beijing–Shanghai Railway as the prototype. It then verifies the validity of the model design and the dynamic similarity between the 1/10 model and the prototype through constraint mode and free mode experiments on the 1/10 scale model, together with transient finite element calculation. The dynamic calculation model is utilized here for the analysis of the errors occurring in the production of the model, and the effect of the model structure simplification on the box girder mode frequency and vibration response. Finally, the article studies the vibration transmissibility characteristics between the plates and along the longitudinal direction by means of model testing. It also discusses the effect of different bridge support stiffness on the box girder vibration. The results presented in this paper can provide a method for forecasting and evaluating the existing or plan-to-build high speed railway environment vibration.

Path loss models for train‐to‐train communications in typical high speed railway environments

Within the next few decades, railways will undergo a major modernisation in order to increase their efficiency and safety. The modernisation process will enable new concepts such as fully autonomous trains and dynamic coupling of consists. Today's centralised railway management systems will not be able to safely handle these train concepts and, thus, will be replaced or complemented by new systems. These systems require a tight train monitoring which is based on the reliable exchange of information covering among others the accurate position and velocity of trains. The authors are sure that modern train concepts demand both reliable train-to-train (T2T) communication and improved capabilities for train-to-ground communication. However, T2T communication is hardly investigated and so far no technology has been selected. With this publication, the authors like to provide more insight into T2T signal propagation. This is the basis for the future development of a modern T2T communication system. The authors focus on wide band propagation for T2T scenarios and describe an extensive channel sounding measurement campaign involving two high speed trains. Results on signal propagation measurements in high speed T2T scenarios are presented for different environments. Hence, path loss models for rural, sub-urban and tunnel environments are introduced.

A time-domain estimation method of rapidly time-varying channels for OFDM-based LTE-R systems

This paper addresses the performance of fast doubly selective fading channel estimation combined with Inter-Carrier Interference (ICI) cancellation for Long Term Evolution (LTE) communication platform in the High Speed Railway (HSR) environment. We consider the Channel Impulse Response (CIR) coefficients with a critical Doppler frequency shift and multi-path fading that were taken from the WINNER II channel model and the D2a propagation scenario, where the conditions of HSR are analyzed. As multi-path fading increases and the channel varies in the order of the symbol period, we first propose a novel approach for designing a pilot symbol structure in the time domain. Then, we describe the deployment of the proposed pilot symbol structure to estimate the channel in the time domain. Channel information corresponding to the data positions is obtained by linear interpolation. In each OFDM symbol, the slope and the initial value for establishing an interpolation function are estimated to adapt to the time variation of the channel. An accurate estimate of channel state information is used for the purpose of ICI cancellation. The simulation results show that the channel estimated by our proposed method can follow the real channel well, even in a very high Doppler frequency. The estimation method in terms of Mean Squared Error (MSE) significantly outperforms the state-of-the-art methods. The combination of our channel estimator with several interference cancelers provides a considerably better system performance than that achieved when frequency channel estimation is used.

An empirical analysis of the radio propagation characteristics in high-speed railway environment

For a wireless mobile network, a profile of radio propagation characteristics is the key to study any wireless techniques, especially in High-Speed Railway (HSR) environment. Unfortunately, such a profile is not available so far, which leads manifold challenges to wireless study for HSR scenarios. In this paper, we focus on this topic, and try to obtain this profile in various kinds of HSR scenarios based on previous field tests in China. Our study reveals that the Line-Of-Sight (LOS) propagation path plays a predominant role in the HSR scenarios, which can suppress the shadow fading. Finally, we find out that each kind of small-scale fading effects has a unique profile on different wireless mobile systems for HSR scenarios. As a result, this study presents a theoretical guidance for studying any wireless techniques in HSR environment, e.g., cell handover for HSR.

Determination of Cell Coverage Area and its Applications in High-Speed Railway Environments

In this paper, a determination method of the cell coverage area for the narrow-strip-shaped cells in high-speed railways (HSRs) is proposed, which is of great importance to HSR dedicated communication network planning. A closed-form expression for the cell coverage area is first derived on the basis of the edge outage probability and the propagation channel parameters, and it is found that the linear coverage in HSRs has a higher percentage of coverage area than the traditional circular coverage of cellular systems. For the convenience of link budget computation, we also present a family of curves related with the percentage of linear coverage. Then, the implementation of the proposed determination of cell coverage area is presented, and it is found that the linear cell of HSRs has a larger maximum communication distance than circular coverage. Furthermore, the impact of frequency on the HSR link budget is investigated, and the results show that a lower frequency band reduces the number of the required base stations. The results can be used in designing and optimizing the wireless coverage and the communication network planning of HSR communications.

철도교통용 고속 트랜스폰더시스템 고속선 실차 성능평가

고속열차의 정확한 위치를 검지하기 위해서는 차상에 설치된 트랜스폰더 리더가 지상에 설치된 트랜스폰더 태그로부터 텔레그램을 오류없이 정확하게 수신하여야 한다. 고속철도 환경에서 텔레그램 수신 성능은 선로 변에 설치된 전기/신호 설비 및 열차에 설치된 전장품으로부터 발생하는 전자기 간섭의 영향을 받는다. 본 연구에서는 열차 위치검지시스템에 절대위치를 제공하기 위해 개발된 철도교통용 고속 트랜스폰더시스템 시작품의 철도환경 적합성을 검증하기 위하여, 호남고속선에 트랜스폰더 태그를 설치하고 HEMU-430X가 268km/h~334km/h로 운행할 때 트랜스 폰더 리더가 각 태그로부터 텔레그램을 오류없이 몇 회 수신하는지 측정하는 방식으로 실차 테스트를 수행하였다. 이를 바탕으로 트랜스폰더 리더와 태그 사이에 형성된 컨택 존의 길이를 추정하고, 400km/h의 고속주행환경에서 태그로부터 오류없이 수신가능한 텔레그램 개수를 예측하였다. It is necessary to receive telegrams transmitted by transponder tags installed along the track in order to detect the exact position of a high-speed train. In a high-speed railway environment, telegrams can be corrupted by the electromagnetic interference that comes from onboard electric train power equipment or wayside devices. In this study, we verified the railway environment compatibility of a high-speed transponder system developed as a train position detection system. We installed transponder tags on the Honam high-speed line and measured the number of error-free telegrams received from the transponder tag while the HEMU-430X was running at 268km/h~334km/h. Based on the measurement, we estimated the length of the contact zone formed between the transponder reader and tag. Field test results allow us to estimate how many error-free telegrams can be received when HEMU-430X is at speeds up to 400km/h.