An Unscented Kalman Filter-based Synchronization Control Approach for Communication-Based Train Control Systems

Communication-based train control (CBTC) systems are automated train control systems based on continuous and bidirectional train-ground communications. CBTC is the direction of future train control systems. Complex channel condition and frequent handoff can severely affect urban rail CBTC train-ground communication performance, and consequently affect CBTC operation efficiency. Current research regarding CBTC does not consider the impact of train-ground communications on train control performance. This paper studies the joint optimization of communication and control in CBTC systems. With the objective to minimize the optimal operation profile tracking error and energy consumption, linear quadratic cost is defined as the control performance measure. In order to ensure train operation safety, the optimization model puts constraints on train control actions related to safety. Moreover, based on the stochastic channel characteristics and the real-time train position information, handoff decision and train control policy are jointly optimized using deep reinforcement learning. Extensive simulation results based on real-field channel measurements illustrate that the proposed optimization method can significantly improve the train control performance in CBTC systems, and CBTC systems need to sacrifice part of performance to ensure system safety.

Leaky waveguide has been adopted in communication based train control (CBTC) systems, as it can significantly enhance railway network efficiency, safety and capacity. Since CBTC systems have high requirements for the train ground communications, modeling the leaky waveguide channels is very important to design the wireless networks and evaluate the performance of CBTC systems. In the letter, we develop a finite-state Markov channel (FSMC) model for leaky waveguide channels in CBTC systems based on real field channel measurements obtained from a business operating subway line. The proposed FSMC channel model takes train locations into account to have a more accurate channel model. The overall leaky waveguide is divided into intervals, and an FSMC model is applied in each interval. The accuracy of the proposed FSMC model is illustrated by the simulation results generated from the model and the real field measurement results.

Communication-based train control (CBTC) systems use wireless local area networks (WLANs) to transmit train status and control commands. Since WLANs are not originally designed for applications with high mobility, random transmission delays and packet drops are inevitable, which could result in unnecessary traction, brakes or even emergency brakes of trains, loss of line capacity, and passenger satisfaction. In this paper, we study the packet drops introduced by random transmission errors and handovers in CBTC systems, analyze the impact of random packet drops on the stability and performances of CBTC systems, and propose two novel schemes to improve the performances of CBTC systems. Unlike the existing works that only consider a single train and study the communication issues and train control issues separately, we model the system to control a group of trains as a networked control system (NCS) with packet drops in transmissions. Extensive field test and simulation results are presented. We show that our proposed schemes can provide less energy consumption, better riding comfortability, and higher line capacity compared with the existing scheme.

To meet the growing railway-transportation demand, researchers have designed a new train control system, communication-based train control (CBTC) system, to maximize the ability of train lines by reducing the headway of each train. However, wireless communications expose the CBTC system to new security threats. Due to the cyber-physical nature of the CBTC system, a jamming attack can damage the physical part of the train system by disrupting the communications. To address this issue, this chapter develops a secure framework to mitigate the impact of the jamming attack based on a security criterion. At the cyber layer, we apply a multi-channel model to enhance the reliability of the communications and develop a zero-sum stochastic game to capture the interactions between the transmitter and jammer. We present analytical results and use dynamic programming to find the equilibrium of the stochastic game. We investigate a special case study to obtain insight results, showing a strong inter-dependency between the physical and cyber layers. Finally, the experimental results are provided to evaluate the performance of the proposed secure mechanism.

Communication-based train control (CBTC) systems are automated train control systems based on continuous and bidirectional train-ground communication. As one of the critical subsystems of the safety-demanding CBTC systems, the Data Communication System (DCS) is physically exposed to the environment. DCS faults may lead to delayed trains, stranded passengers, suspension of service, or even catastrophic losses of lives or assets. Therefore, it is quite desirable to identify the DCS faults and accordingly take effective maintenance measures. Most fault classification works are based on traditional Machine Learning (ML) techniques, such as Decision Tree (DT) and Bayesian Network. A substantial amount of fault information can be lost when we manually extract features from the system logs. This paper proposes a Deep Hybrid Learning (DHL) method to identify the DCS faults. A pre-trained Chinese Bidi-rectional Encoder Representation from Transformers (BERT) Deep Learning (DL) model is used to extract fault features from raw text logs automatically, and several traditional ML models are used to classify the system faults by the extracted features. Our study highlights the advantages of the DHL model in DCS fault diagnosis tasks. The intense simulation results illustrate that the proposed DHL model achieves substantial accuracy compared with traditional ML algorithms.

In communication-based train control (CBTC) systems, as a train travels along a track, it changes its association from one access point (AP) to another, which is referred to as a handoff. Control parameters for handoff algorithms are often set based on desired system requirements, such as outage probability, without considering the effect of the underlying propagation characteristics of the channel on the system performance. Therefore, for typical CBTC systems, conservative safety margins are introduced in order to ensure safe operations leading to potentially sub-optimal system performance. In this paper, we propose a systematic procedure for selecting a control parameter, namely the threshold value, which determines when handoffs are triggered for received signal strength (RSS) based handoff algorithms. The proposed scheme leads to the selection of threshold values that increase the safety margin in addition to reducing the number of handoffs incurred, thereby enhancing the overall CBTC system performance. These enhancements are made possible with the integration of physics-based propagation models and network design that harnesses the propagation characteristics of the underlying wireless channels.

Although there has been substantial research in system analytics for risk assessment in traditional methods, little work has been done for safety risk prediction in communication-based train control (CBTC) system, especially intelligently predicting risk caused by the uncertainty in the system operation. Risk prediction and assessment of hazards in train control systems are vital for the safety and efficiency of urban rail transit. In this paper, we propose an intelligent hazard-risk prediction model based on a deep recurrent neural network for a new communication-mode CBTC system. First, a train-to-train communication-based train control (T2T-CBTC) system is proposed to improve the drawback of CBTC in information-exchanging mode. Then we design a risk prediction feature selection and generation method and estimate a critical function feature in the T2T-CBTC system by statistical model checking. Finally, we construct our intelligent hazard-risk prediction model based on a deep recurrent neural network using a long-short-term memory (LSTM) network. The model had excellent risk prediction classification results and performance in our experiment, even for unbalanced data set. This model consistently outperforms the deep belief network trained in Accuracy, Precision, Recall and F1-score for the hazard-risk prediction problem. Specifically, the mean accuracy is 97.2% and mean F1-score is 93.9% in overall performance of model. The improvements of our model against DBN model are 8.2% for Precision, 7% for Recall and 8% for F1-score.

Communication-based train control (CBTC) systems are automated train control systems using information technologies to ensure safe train control. With the adoption of wireless communication and computer network technologies, CBTC systems are facing increasingly serious security attacks. The Man-in-the-Middle (MitM) attack is one kind of attacks with strong concealment, with which attackers can gain control of the system and inflict serious train collision. This paper proposes a Bayesian game based defense scheme for CBTC systems under Man-in-the-middle attacks. We develop an attack detection method and active defense strategy to reduce the harm brought by MitM and improve the efficiency of the system under MitM attacks. Extensive simulation results show that the proposed defense scheme achieves great performance in defending against MitM attacks.

Communication-based train control (CBTC) systems use wireless local area networks (WLANs) for train-ground transmission. Transmission delays and packet drops may cause unnecessary tractions and brakes of trains, degradation of riding comfortability, rising of energy consumption and loss of the line's capacity. In this paper, We model the system to control a group of trains in CBTC as a networked control system (NCS) with transmission delays and packet drops. We study the delays and packet drops in CBTC, analyze their effects on train's control, and propose a method to improve the performance of CBTC. Extensive simulation results are presented. We show that our proposed method has a strong tolerance for transmission delays and packet drops, and outperforms the currently adopted scheme in different channel conditions.

Electromagnetic interference (EMI) is an important issue that needs to be considered for the safe operation of communication-based train control (CBTC) systems in railway environments. In this paper, two EMI scenarios are considered: the EMI of a wireless local area network (WLAN) at subway stations to CBTC systems and the interference of the CBTC system in tunnels to WLAN devices inside the train. To effectively solve those EMI problems, we introduce two hybrid wave propagation modeling techniques, where ray-tracing and finite-difference time-domain (FDTD) methods are hybridized for the first scenario and vector parabolic equation and FDTD methods are hybridized for the second scenario. The two hybrid models combine advantages of each individual method and achieve a better tradeoff between accuracy and efficiency. Based on our analysis, EMI-aware planning such as the optimization of WLAN antenna placement at subway stations can be pursued. The benefits that this approach can produce are demonstrated.

With the rapid development of wireless communications, passengers are able to surf the internet on the subway trains through mobile communication systems, even wireless local area networks (WLAN). That leads to communications-based train control (CBTC) systems interfered by WLAN equipments in the carriage due to working on the same frequency (2.4GHz frequency band). In this paper, we evaluate the interference from WLAN equipments for the spatial deployment for CBTC systems. To investigate the impact of the carriage to radio propagation, the test on the shielding effectiveness of the carriage is carried out and also verifies the simulation results based on ray-tracing method. Then, multiple spatial deployment scenarios for access points (APs) and mobile radios (MRs) of CBTC systems are investigated on anti-interference to WLAN equipments, respectively. From the analysis, the suggestions of the deployment for APs and MRs are provided.

Communication-Based Train Control (CBTC) system is a critical signal system to ensure rail transit’s safe operation. Compared with the train-ground CBTC system, the train control system based on train-to-train (T2T) communication has the advantages of fast response speed, simple structure, and low operating cost. As the core part of the train control system based on T2T communication, the reliability of the data communication system (DCS) is of great significance to ensure the train’s safe and efficient operation. According to the T2T communication system requirements, this paper adopts Long-Term Evolution-Unlicensed (LTE-U) technology to design the DCS structure and establishes the reliability model of the communication system based on Deterministic and Stochastic Petri Nets (DSPN). Based on testing the real line’s communication performance parameters, the DSPN model is simulated and solved by π -tool, and the reliability index of the system is obtained. The research results show that the LTE-U-based T2T communication system designed in this paper meets the train control system’s needs for communication transmission. This paper’s reliability evaluation method can complete the reliability modeling of train control DCS based on T2T communication. The research in this paper will provide a strong practical and theoretical basis for the design and optimization of train control DCS based on T2T communication.

The paper summarises the evolution of rail signalling system from track-circuit signalling to advanced Communication-Based Train Control (CBTC) systems, highlighting the differences, benefits and challenges with regard to improving performance while ensuring safety. It describes the evolution of such systems over the last three decades; from wayside signals to cab signals, to profile-based systems, to communications-based train control systems for an effective means of overcoming the fundamental limitations of the conventional track-circuit based system. CBTC allows trains to operate with much closer headways while ensuring safe train separation and better train control flexibility. Not only do CBTC systems determine the position of the train with a higher degree of accuracy independently of the track circuits, but also offer bidirectional train/wayside communication to enhance train operations and supervision. Operational and performance benefits realised with the newer technology systems are discussed, together with the challenges of implementing such systems. Standardisation for Communication Based Signalling System initiatives is also described and industry trends presented.

A common concern with Communications Based Train Control (CBTC) systems is that the wireless subsystem (usually referred to as the data communications subsystem or DCS) is susceptible to radio frequency (RF) interference from the plethora of wireless devices (especially wireless local area networks or WLANs) that generally share the same 2.4 GHz spectrum as that used by the DCS. This concern is investigated in greater detail given the operational histories of two CBTC-equipped public transit lines: NYCT’s “L” or Canarsie Line in New York City and the Las Vegas monorail. Measurements of the RF environment in both locations showed a substantial level of activity in the 2.4 GHz band from WLANs and other devices but with no obvious adverse impact on the operational performance of either line. This paper presents RF measurements at 4 locations in New York City and 1 location in Las Vegas, discusses the relative severity of the RF noise and interference environment and explores why both CBTC systems are able to perform in the presence of in-band RF noise and interference without any obvious adverse impact.

In this paper, We improve the transmission mechanism of the WLAN (wireless local area network), so that it will be suited to the wireless CBTC (communication based train control). Considering the characteristic of the wireless CBTC, the author brings forward a new frequency allocation proposal, which is combined all of sub-channels into an operating channel. The advantage of this improvement is that the bit error rate will be greatly reduced. Besides a new spread spectrum sequence is introduced, which improves the anti-jamming of the CBTC system. These improved methods can be introduced in the future work to further improve the reliability of the system.