Tracking and collision avoidance of virtual coupling train control system

Tracking and collision avoidance of virtual coupling train control system

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.

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.

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.

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.

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) system is an advanced train signalling and control technology which is developed using the moving block signalling (MBS) framework. The CBTC system has been shown to be capable of improving the operational efficiency, line capacity and safety of the railway operation. The main objective in implementing the MBS framework in CBTC system is to minimize the train headways through the utilization of an inter-train continuous communication system that determine and control the position of each train more precisely. One important challenge in such an implementation is the fulfillment of the necessary requirement of having highly accurate train localization method to ensure the safety of the short headway operation. This paper describes the results from experimental examination and application of a synchronization control strategy for the CBTC system using an unscented Kalman filter (UKF)-based sensor fusion approach as the localization method. In the proposed approach, the train localization task is performed using an UKF-based sensor fusion method which fuses measurement data from speed sensors and radio frequency identification tags. A synchronization control approach to ensure the safety movement of the train convoy in curved railway tracks under the MBS scheme is then proposed. The results presented in this paper show that the proposed localization and synchronization control methods can significantly improve the localization accuracy and reduce the inter-train headways.

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.

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.

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.

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 development of virtual coupling technology provides solutions to the challenges faced by urban rail transit systems. Train tracking control is a crucial component in the operation of virtual coupling, which plays a pivotal role in ensuring the safe and efficient movement of trains within the train and along the rail network. In order to ensure the high efficiency and safety of train tracking control in virtual coupling, this paper proposes an optimization algorithm based on Soft Actor-Critic for train tracking control in virtual coupling. Firstly, we construct the train tracking model under the reinforcement learning architecture using the operation states of the train, Proportional Integral Derivative (PID) controller output, and train tracking spacing and speed difference as elements of reinforcement learning. The train tracking control reward function is designed. Then, the Soft Actor-Critic (SAC) algorithm is used to train the virtual coupling train tracking reinforcement learning model. Finally, we took the Deep Deterministic Policy Gradient as the comparison algorithm to verify the superiority of the algorithm proposed in this paper.

The development of an intelligent connected monorail transit system offers an effective solution to the mismatch between passenger flow and system capacity at various time intervals within urban rail networks. As the core of such a system lies the virtual coupling (VC) technology, which dynamically adjusts train configurations in response to real-time passenger demand, thereby improving resource utilization. However, during VC operations, severe communication delays between vehicles or the sudden emergence of obstacles ahead may still result in rear-end collisions among coupled vehicles, posing significant safety risks. To address these challenges, this paper focuses on the active collision avoidance control of intelligent connected monorail vehicles operating within the VC environment. At the modeling level, a control model is developed to facilitate VC between leading and following vehicles, and the dynamic characteristics of typical operational scenarios—including station approach coupling, tracking coupling, and departure decoupling—are thoroughly analyzed. Building upon this foundation, the train’s behaviors under collision avoidance during accelerated departures, decelerated arrivals, and unexpected obstacle encounters are further investigated. In terms of control strategy, a Model Predictive Control (MPC) algorithm is introduced to enable efficient coordination and proactive collision avoidance among trains. Ultimately, a simulation platform based on Chongqing Rail Transit Line 3 is established for validating the proposed model and algorithm under representative operating scenarios. The evaluation demonstrates gains in system flexibility and safety and technical foundation for the practical implementation of intelligent rail transit systems.