Communication Based Train Control Research Articles

Electromagnetic compatibility study of trackside antenna array miniaturization in the subway tunnel

Abstract To improve the compatibility of the subway tunnel’s electromagnetic environment and reduce the radiation impact of trackside antennas on subway workers. This paper proposes a miniaturized dual-band trackside antenna array by using metamaterial units. Its operation bandwidth is 2.33 ∼ 2.56 GHz and 3.24 ∼ 3.45 GHz, which could simultaneously satisfy the signal cover demands of the communications-based train control (CBTC) and the civil 5G wireless communication system. The proposed miniaturized antenna array has a maximum gain of 14.4 dBi and a maximum channel capacity of 13.9 bps Hz−1 at a signal-to-noise ratio (SNR) of 20 dB, which can effectively improve the quality of wireless communication systems. The number of trackside antennas with a single operation frequency band is reduced, and the distance between the antennas is enlarged at the same time. Besides that, we analyze the radiation impact on the tunnel electromagnetic environment of the proposed trackside antenna array. In particular, the electromagnetic dose absorbed by the human model of a tunnel worker is quantized. The results show that the electric field strength in the tunnel reduces by 4.11% at least after antenna array miniaturization, and the specific absorption rate (SAR) absorbed by the worker model’s trunk, skull, brain, heart, and liver is reduced by a maximum of 19.02%, 33.16%, 28.27%, 41.75%, and 74.54%, respectively, further lowering the human electromagnetic exposure risk. Therefore, a miniaturized trackside antenna array could reduce the interference from other radiation sources in the tunnel while providing a new idea for electromagnetic protection for subway workers.

Developing CBTC system safety requirement hierarchy through STPA methodology

Safety-critical systems, such as the railway signal system, are subject to potentially high costs from failures, including loss of life and property damage. The use of new technology, including communication-based train control (CBTC) systems with software and computers, has changed the types of accidents that occur. Software-related issues and dysfunctional interactions between system components controlled by the software are increasingly the cause of incidents. Developing a “safe” safety-critical system requires accurate and complete safety requirements, which are the foundation of system development. Traditional hazard analysis techniques are insufficient for identifying the causes of accidents in modern railway signaling systems. Systems-Theoretic Process Analysis (STPA) is a powerful new hazard analysis method designed to address these limitations. Building upon this foundation, a hierarchical approach to safety requirement development has been further developed. This approach combines STPA analysis with a hierarchical modeling approach to establish traceability links from safety requirements to specific architectures, refine and allocate system-level safety requirements to relevant subsystems, and abstract safety requirements at higher hierarchical levels to enable easy changes to lower-level implementations. This paper employs the aforementioned methodology within the context of the CBTC system, thereby enhancing risk management and hazard analysis, enabling early insights, and facilitating the generation of safety requirements of CBTC System.

A transfer learning-based intrusion detection system for zero-day attack in communication-based train control system

A transfer learning-based intrusion detection system for zero-day attack in communication-based train control system

Federated learning-based edge computing for automatic train operation in communication-based train control systems

Federated learning-based edge computing for automatic train operation in communication-based train control systems

Wireless Train Detection Based on Null-Filled UHF RFID Reader Antenna for Communications-Based Train Control

Wireless Train Detection Based on Null-Filled UHF RFID Reader Antenna for Communications-Based Train Control

Graph neural network‐based attack prediction for communication‐based train control systems

AbstractThe Advanced Persistent Threats (APTs) have emerged as one of the key security challenges to industrial control systems. APTs are complex multi‐step attacks, and they are naturally diverse and complex. Therefore, it is important to comprehend the behaviour of APT attackers and anticipate the upcoming attack actions. GNN‐AP is proposed, a framework utilising an alert log to predict potential attack targets. Firstly, GNN‐AP uses causality to eliminate confounding elements from the alert dataset and then uses an encoder‐decoder model to reconstruct an attack scenario graph. Based on the chronological characteristics of APT attacks, GNN‐AP identifies APT attack sequences from attack scenario graphs and integrates these attack sequences with communication‐based train control (CBTC) devices topology information to construct an Attack‐Target Graph. Based on the attack‐target graph, a graph neural network approach is used to identify the attack intent and transforms the attack prediction problem into a link prediction problem that predicts the connected edges of the attack and target nodes. The simulation results obtained using DARPA data show that the proposed method can improve the comparison methods by 4% of accuracy in terms of prediction. Furthermore, the method was applied to the CBTC system dataset with a prediction accuracy of 88%, demonstrating the efficacy of the proposed method for industrial control systems.

Blockchain-Empowered Edge Intelligence for TACS Obstacle Detection: System Design and Performance Optimization

With the significant advantages of system complexity and operating costs, Train Autonomous Circumambulate System (TACS) is gradually replacing the traditional Communication Based Train Control (CBTC) system as the next-generation train operation control system development direction. As train operation and control become more decentralized and autonomous, real-time and accurate obstacle detection, apart from route-level protection, is quite desirable in TACS. Most of the existing researches about obstacle detection focus on detection algorithm optimization based on the once-deployed lifelong use principle, while model re-optimization based on the actual operating environment under unexpected situations and model sharing among multi-users are largely ignored. In this paper, we design a novel obstacle detection system in TACS based on blockchain-empowered Edge Intelligence (EI). To make full use of the massive raw unannotated data collected online, we first propose an SSL-based TACS obstacle detection model. Considering the resource-hungry model training, we introduce EI into TACS and propose a MARL-based task offloading algorithm for secure and efficient computation offloading coordination. Furthermore, we propose a blockchain-based model sharing scheme to facilitate the multi-model parameter exchange and improve the obstacle detection accuracy. Extensive simulation results show that the designed obstacle detection system can effectively improve the TACS obstacle detection performance.

Optimizing Handoff Schemes in Communication-Based Train Control: Reinforcement Learning to Reduce the Age of Information

Optimizing Handoff Schemes in Communication-Based Train Control: Reinforcement Learning to Reduce the Age of Information

Joint Security and Resilience Control in IIoT-Based Virtual Control Train Sets Under Jamming Attacks

The virtually coupled train sets (VCTS) have been proposed to improve transportation efficiency, passenger satisfaction and operational capability for communication-based train control (CBTC) systems. Reliable and real-time trainto-train (T2T) wireless communications are quite desirable to ensure the stability of VCTS. Moreover, with the development of industrial Internet of Things (IIoT), an IIoT-based VCTS is proposed. Due to the physically exposed wireless environment, T2T wireless communications are vulnerable to jamming attacks (JAs), causing packets loss, time delay, and interference with the operation of VCTS systems. The distance between adjacent trains in the IIoT-based VCTS would be violate the minimum relative braking distance regulated by the traditional moving block (MB) principle if JAs were not mitigated. This is a severe safety events, which is intolerable for train control systems. In this study, we develop an event-triggered control (ETC) based resilience control method to mitigate the impact of JAs on the IIoT-based VCTS. A cooperative control approach, stable IIoT-based VCTS range and T2T communication cycle are jointly designed in the ETC-based model. The resilience control approach enables the communication cycle to be adjusted by sacrificing part of the system performance to ensure the safety and stability of VCTS systems when JAs occurs. Extensive simulation results demonstrate that the proposed ETC-based resilience control model can successfully defend against jamming attacks, and the transportation efficiency in urban rail transits can be significantly improved even under JAs.

A Security Defense Method Against Eavesdroppers in the Communication-Based Train Control System

A Security Defense Method Against Eavesdroppers in the Communication-Based Train Control System

Next-Hop Relay Selection for Ad Hoc Network-Assisted Train-to-Train Communications in the CBTC System

In the communication-based train control (CBTC) system, traditional modes such as LTE or WLAN in train-to-train (T2T) communication face the problem of a complex and costly deployment of base stations and ground core networks. Therefore, the multi-hop ad hoc network, which has the characteristics of being relatively flexible and cheap, is considered for CBTC. However, because of the high mobility of the train, it is likely to move out of the communication range of wayside nodes. Moreover, some wayside nodes are heavily congested, resulting in long packet queuing delays that cannot meet the transmission requirements. To solve these problems, in this paper, we investigate the next-hop relay selection problem in multi-hop ad hoc networks to minimize transmission time, enhance the network throughput, and ensure the channel quality. In addition, we propose a multiagent dueling deep Q learning (DQN) algorithm to optimize the delay and throughput of the entire link by selecting the next-hop relay node. The simulation results show that, compared with the existing routing algorithms, it has obvious improvement in the aspects of delay, throughput, and packet loss rate.

Research on Train-to-Train Communication Resource Allocation for Urban Rail Transport

As communication technology improves and urban rail communication system develops, the new CBTC (Communication-Based Train Control) system built upon train – train(T2T) communication has become the mainstream of future rail transit signaling systems. In the future train-centered CBTC system, Train-to-Ground (T2G Train-to-Ground) mode may coexists with T2T communication mode. To reduce the interference and ensure the reliable transmission of information and system security, a joint channel allocation and power control communication resource distrbution method is proposed, which firstly allocates channels to users by the Hungarian algorithm, and then make use of sparrow search algorithm to coordinates the user’s transmit power. Obtained by simulation results, this suggested strategy not only reduces interferences caused by resource sharing and satisfies the dependability of the system, but also significantly improves the throughput of the system.

Haberleşme Tabanlı Tren Kontrolünün (CBTC) Otomatik Tren Korumasına (ATP) İlişkin Dağıtılmış-Hiyerarşik Kontrol Yaklaşımı

Kentleşme ile birlikte metropollerde yaşayan insan sayısı artmaktadır, nüfus artışına paralel olarak şehir içi ulaşımda çok önemli bir yer tutan raylı ulaşım sistemlerine olan talep, kalabalık şehirlerde her geçen gün daha da artmaktadır.Artan talebe paralel olarak raylarda artacak trafik yoğunluğu, sinyalizasyon sistemlerini güvenlik, kapasite ve rayların verimine etkisi açısından oldukça önemli kılmaktadır. Günümüzde birçok raylı ulaşım sistemlerinde sinyalizasyon sistemi olarak Haberleşme Tabanlı Tren Kontrol (CBTC) tercih edilmektedir. CBTC, Otomatik Tren Koruması (ATP), Otomatik Tren Denetimi (ATS) ve Otomatik Tren İşletmesi (ATO) gibi bazı alt sistemlerden meydana gelmektedir. Alt sistemlerin kendine özgü tanımlanan bazı görevleri bulunmaktadır. Bu görevleri yerine getirirken, alt sistemlerden artan gereksinimlerden etkilenmeden, istenen emniyet fonksiyonlarını en üst düzeyde güvenle uygulaması beklenmektedir. Ancak, bu sistemlerin genel tasarımının, ihtiyaç duyulan yeni gereksinimlere göre değiştirilmesi gerekebilir. Bu bağlamda çeşitli standartların sağlarken, alt sistemlerin kontrol edilmesi ve modellenmeleri büyük önem taşımaktadır. Bu çalışmada, CBTC’nin alt sistemi ATP, ayrık olay sistemi yaklaşımıyla sonlu durum otomatları ile modellenmiştir ve alt sistemlere ait denetimsel gözetleyicilerin tasarımı yapılarak, kontrol edilebilirlik ve kilitlenmesiz kontrol koşullarını karşıladığı gösterilerek, elde edilen sonuçlar yekpare yaklaşım modeli ile karşılaştırılmıştır.

A Cross-Layer Defense Method for Blockchain Empowered CBTC Systems Against Data Tampering Attacks

Due to the high integration of wireless communication and networking technologies, the communication-based train control (CBTC) systems are exposed to additional cyber-attack surfaces, allowing sophisticated attackers to combine cyber attack vectors with physical attack means to achieve malicious goals. Notably, the decentralized authentication features are missing in existing communication protocols which make the CBTC be easily compromised by data tampering attacks, and lead to serious operational accidents. With outstanding advantages in decentralized authentication, blockchain provides new effective solutions for decentralized identity authentication in CBTC. Consequently, it is critical to study the complex physical consequences of cyber breaches from a cross-layer defense perspective. In this paper, we propose a novel cross-layer defense method for cyber security in blockchain empowered CBTC against data tampering attacks. In the physical layer, the joint Kalman filter and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\chi ^{2} $ </tex-math></inline-formula> detector is proposed for the train state estimation and detection. In the cyber layer, an asymmetric encryption-based secure communication protocol with identity authentication and the blockchain-based distributed key management system with the adaptive consensus mechanism are designed for data communication security. Considering the unavailable direct observation of the CBTC cyber security states, a partially observable Markov (POMDP) decision model is constructed to derive the optimal adaptive consensus strategies for balancing cyber security and efficiency. Extensive simulation results show that the proposed blockchain empowered CBTC cross-layer defense method can effectively improve the cyber security protection capability and minimize the impact of data tampering attacks on the train operation.

Cybersecurity Considerations for Communication Based Train Control

Cybersecurity Considerations for Communication Based Train Control

A Comprehensive Resilient Control Strategy for CBTC Systems Through Train-to-Train Communications Under Malicious Attacks

In recent years, intelligent transportation systems and automatic train control technologies have been developed rapidly. The communication-based train control (CBTC) system through train-to-train communications is very important in the intelligent transportation system and it uses wireless communication links to transmit the train control commands. However, the CBTC system is sensitive to information security attacks. Therefore, a comprehensive resilient control strategy is proposed for the CBTC system under Denial of Service (DoS) attacks. As a cyber physical system, the CBTC system includes two layers. In the communication layer, a strongly robust train-to-train communication topology is proposed which adopts a bidirectional communication strategy and a three times communication protocol to achieve the goal of resisting multi-channel DoS attacks. In the physical layer, an observer-based distributed intrusion detection method is firstly proposed to detect the attack. Further, a resilient control method based on a distributed state observer of the leader train is proposed to avoid triggering the train safety protection mechanism frequently. Finally, simulation results prove that the proposed comprehensive resilient control algorithm is effective in the CBTC system.

Communication-Based Train Control with Dynamic Headway Based on Trajectory Prediction

Rail transit plays a significant role in the operation of an efficient and effective urban public transportation system. Safety and capacity are some of the most crucial objectives in railway operations. The communication-based train control (CBTC) system is a continuous and automatic train control system that realizes constant and high-capacity train ground two-way communication. In this study, a dynamic headway model of the ‘softwall’ moving-block approach is proposed for CBTC to increase the track capacity and improve dispatching efficiency based on the train trajectory prediction. For this precise trajectory prediction task, we introduce a hybrid trajectory prediction model to combine Long Short-term memory (LSTM) and Kalman Filter (KF) to extract the train’s local data features and learn the long-term dependencies, respectively. Then we present a dynamic headway model to maximize the train headway and reduce the track distance. The leading trains’ information is used to construct the iterative learning control strategy, and the predicted trajectory is input into the algorithm of the headway model. We use a simulation model of the rail network in Chengdu to demonstrate the effectiveness of our proposed approach. The results show the Mean Absolute Error (MAE) of the predicted trajectory retreated to 93.97 cm and reductions in operation headway of at least 64.33% under the dynamic headway model versus the traditional moving-block model.

Through Running and Integration of Federal Railroad Administration and Federal Transit Administration Regulated Passenger Trains: A Path Toward Mixing Intercity, Commuter, Metro, and Light Rail on the Same Tracks

U.S. rail transit (subways, metros, and light rail) and Federal Railroad Administration (FRA) regulated heavy rail (commuter, intercity and regional rail) operate completely separately in revenue service. This necessitates transfers between the modes at terminals. While not unique to the U.S.A., its version of this practice is extreme and prevents the development of robust seamless rail networks. Especially in the post-Covid environment, this leaves commuter rail in search of a mission and rail transit isolated from suburbs. This paper discusses the statutory regulatory scheme that divides the two modes in the U.S.A. It will analyze the justification for the segregation and its history. Such issues include potential collisions, weight, crashworthiness, electrification, signaling, loading gauge, platform height, and operating practices. This paper concludes that the regulatory barrier preventing an FRA-regulated train from going onto a non-FRA railroad are surmountable. Running through trains between the FRA-regulated system and the rail transit network would enhance regional networks. The “Karlsruhe model” in Germany and the through running of regional trains onto the Tokyo subway network are two prime examples. Recent technological advances—such as dual mode battery multiple units, robust signaling systems such as Communications Based Train Control and Positive Train Control, and advanced car body designs able to deal with different loading gauges—make through running more practical. With little or no new right-of-way, it is possible to create far more useful rail networks. Potential shared networks at the conceptual level are discussed for Los Angeles, Seattle, Washington, D.C., Dallas, and Sacramento.

Experiment and theoretical analysis of electromagnetic interference on communication systems in multi-rails environment

Purpose Electromagnetic interference (EMI) on communication systems of unban rail transit can hardly be clarified because of complicated factors around railways. This paper aims to target this issue and extend experimental and theoretical analysis. Design/methodology/approach This paper take the Nanjing Dashengguan Bridge as an example, because it carries the most tracks in the world and bears three kinds of trains running through, providing a perfect complex environment. First, it investigates the three communication systems, terrestrial trunked radio, communications-based train control (CBTC) and passenger information system (PIS) that Nanjing Metro uses, and select appropriate devices accordingly. Second, it establishes a system level platform and conduct three tests to analyze their respective operating principles and performance difference under common electromagnetic environments. Third, it adopts theoretical formula to verify test results. Findings The experiment results and theoretical analysis mutually corroborate each other and present practical recommendations: an 8 m or more distance between two tracks will ensure no obvious EMI created by a passing train on communication systems; two certain communication systems should not share the same frequency band; interference level is more related to field strength than weathers and building materials; and CBTC DSSS waveguide mode as well as PIS LTE mode are preferred. Originality/value This research also provides a practical method of investigating EMI for other complex situations.

Joint Security and Train Control Design in Blockchain-Empowered CBTC System

The communication-based train control (CBTC) system ensures the high efficiency and orderliness of trains and is widely used in urban rail transit networks. The adoption of wireless communication and network techniques makes the CBTC systems more vulnerable to cyber attacks. Identity authentication is an effective approach to improve system security. The existing identity authentication mechanisms in CBTC adopt a centralized key management system sensitive to single-point failures. To improve system security, in this article, we deploy a blockchain in CBTC systems. The client that runs the blockchain program not only acts as blockchain nodes to provide distributed key management for the CBTC system but they also work as a relay node to authenticate the communication between train control nodes in CBTC systems. Based on the blockchain-empowered distributed security scheme, the block producer selection and onboard blockchain client handoff decision problem are studied. With the objective to minimize the impact of the key updating process on CBTC system performance and keep the system security under a reasonable level, we formulate the block producer selection and onboard blockchain client handoff decision problem using the deep reinforcement learning approach. Extensive simulation results illustrate that the proposed blockchain-empowered security scheme can significantly improve the CBTC system security, and CBTC systems need to sacrifice part performance to ensure system security.