The high reliability and safety of the critical on-board equipment of high-speed trains play a crucial role in the safety of train operation. So, online safety monitoring and maintenance strategy adjustment are key technologies to realize the safe operation of the advanced train control system. To reduce the difficulty of formal verification of Continuous-time Markov Chains (CTMC) model with uncertain parameters, online quantitative safety monitoring and maintenance strategy adjustment methods are realized by combining probabilistic model checking approach with machine learning algorithm. To begin with, for improving the credibility of the training data used for the machine learning algorithm, probabilistic model checking approach is used to verify the CTMC models transformed from dynamic fault tree (DFT) models. Then, for the defined continuous stochastic logic (CSL) property, the relationship between the maximum reachability probability and the failure/repair rates parameters are obtained by using least square support vector machine (LSSVM) in this paper, which can avoid the shortcoming that the traditional formal method can’t converge in the limited verification time. Finally, for improving the real-time performance of the proposed method, Quantitative Safety Boundary Computation Algorithm is designed to compute the quantitative safety boundaries (QSBs) for monitoring quantitative safety level (QSL) and adjusting the maintenance policy.

With the continuous growth in passenger of high-speed railway, the existing line passing capacity (LPC) is unable to meet the increasing transportation demands. The train tracking interval (TTI) serves as an important parameter for evaluating LPC. The traditional train control system (TCS) considers the transmission latency as a fixed constant measured in the worst environment, which restricts the effectiveness of TTI optimization. In this article, an optimization strategy of TTI for train autonomous operation is proposed based on stochastic network calculus (SNC), aiming at enhancing LPC. Different with the existing TCS, SNC can calculate the transmission latency as a dynamic value, thereby more accurately reflecting the complexity and variability of the train operating environment and speed. This strategy not only guarantees the safety of train operations but also achieves smaller and more appropriate transmission latency. In addition, this article employs a moving block system for train autonomous operation and further decreases the TTI which is realized by train-to-train communication. Simulation studies were conducted to explore the relationship between transmission latency and the dynamic operational speed and environmental changes. The results demonstrate that the proposed method can enhance LPC by 2% to 9%. Meantime, the developed control algorithm can prove the effectiveness and availability of the TTI adjustment method.

The system requirement specifications (SRSs) of the train control system (TCS) are the starting point and foundation of system design and development. Defects in the SRSs will bring great risk to the success of railway engineering projects. Therefore, formal modeling and verification methods are introduced to ensure the correctness of TCS. However, there is a huge gap between the formal computer executable model and the SRSs of TCS described in natural language. To solve this problem, a complete conversion process of ‘TCS requirement specification$\rightarrow $semi-formal models (UML/SysML)$\rightarrow $formal models (safety verification model and reliability evaluation model)’ should be created to ensure full coverage and consistency of semi-formal models and formal models to the SRSs of TCS. With the continuous development of wireless communication, artificial intelligence, and control technology, the future advanced TCS is developing towards a more intelligent and autonomous direction. Online safety monitoring and operational state-based maintenance approaches are critical technologies for developing the future advanced TCS. However, the traditional model-checking approach is time-consuming and susceptible to state space explosion problems. To reduce the difficulty of online safety monitoring and reliability evaluation, machine learning algorithms should be combined with the traditional model checking approaches to improve the verification efficiency during train operation. In this paper, we discussed various formal modeling and safety verification methods for the SRSs of TCS and pointed out the above development directions for the advanced TCS.

High-speed trains are susceptible to unexpected events such as strong winds and equipment failures, which can result in deviations from the scheduled timetable. As the density of traffic increases, these delays can quickly spread to other trains, eventually leading to conflicts in the timetable. To ensure the efficiency of high-speed railways, quickly resolving potential conflicts and generating appropriate rescheduling schemes are essential. The existing hierarchical structure of train control and online rescheduling tends to be inefficient in terms of information communication and can even lead to unfeasible rescheduled timetables and trajectories. To address these issues, an integrated structure of timetable rescheduling and train trajectory optimization is proposed by introducing the train minimum running time into the process of timetable rescheduling and using the adjusted running time as the objective of trajectory optimization. The integration model is formulated by considering the constraints of timetable rescheduling such as the maximum number of trains overtaking trains, platforms at stations, and the priority of the train, as well as the constraints of trajectory optimization. A deep reinforcement learning (DRL)-based approach is proposed to solve the problem. Numerical experiments are conducted on a segment of the Beijing-Shanghai high-speed railway line, using adapted data to demonstrate the effectiveness of the proposed method in rescheduling timetables and optimizing train trajectories. The results show that the integrated rescheduled timetable and the optimized train trajectory can be generated simultaneously and the computation time exhibits a linear increase with respect to the size of the problem.

For the operational efficiency improvement of trains in railway transportation, Autonomous Train Control System (ATCS) is getting widespread research attention. The station is a main scenario for the autonomous train operation, and its passing capacity is one of the critical factors affecting the further release of line capacity potential for railway lines. Based on this, the paper proposes an autonomous route management system to enhance the efficiency of route management in stations. As a safety critical system, the functional safety and performance analysis of the system must be evaluated before it is implemented in practical application. Hence, Colored Petri Nets (CPNs) are used to formalize and evaluate the system. Afterward, based on the proposed CPNs models, functional safety is carried out by dynamic attribute analysis and computational tree logic. Moreover, considering the impact of communication delays and processing delays on the route resource allocation, a performance analysis of the autonomous route process for trains at the station is implemented through practical parameterizations. The results indicated that the proposed autonomous route management strategy can reduce the train operating time and train arrival interval in stations by 15.63 s and 24.9 s, respectively.

This paper addresses the cooperative control problem of trains with specific consideration of human interventions in unusual situations where hard handovers on control objectives and safety constraints may occur. To overcome the effects of such discontinuous factors caused by interventions on the smooth operation of trains and avoid drastic changes in the control input, cooperative control policies with soft-switching are constructed based on novel potential energy functions and weighted functions such that, besides achieving consensus among trains for desired velocities and positions, maintaining prescribed tracking distance, collision avoidance is also guaranteed during the state transition process. Furthermore, adaptive approximation and saturation compensation mechanisms are adopted to cope with parameter uncertainties and input saturation. A rigorous proof is provided to demonstrate the correctness of the proposed results theoretically, and numerical experiments are conducted using real operation data to illustrate the theoretical conclusions.

The advanced 5G and mobile edge computing promote the development of the intelligent high-speed railway and enable computing-intensive and latency-sensitive tasks. Mobile edge computing can effectively release the pressure of tasks on on-board computing resources by migrating communication, computing, and storage to edge servers. However, the fluctuation of data transmission and task processing duration exists in practical projects. The current work assumes that the traditional communication model is ideal and that the latency can be derived as a fixed value, which is the most conservative bound. Obviously, there will be some system performance loss because the transmission latency is usually much shorter than this conservative bound. Therefore, an offloading strategy is proposed to maximize the task completion rate, considering the fluctuation of transmission latency and processing time in this paper. First, the stochastic network calculus is adopted to evaluate the fluctuation and probability of the transmission latency. Additionally, the task processing duration is regarded as an exponential distribution that is affected by the computing resource. Then, the task offloading model is generated based on the potential game to maximize the task completion rate. Moreover, the Nash Equilibrium and best response are derived. A reinforcement learning algorithm combined with a game is proposed to reach the Nash Equilibrium and obtain the task offloading strategy. Finally, extensive theoretical analysis and simulations are illustrated to prove the effectiveness of the model.

Malware detection is a vital task for cybersecurity. For malware dynamic behavior, threats come from a small number of Application Programming Interfaces (APIs) embedded in the API sequences, which are easily ignored or obfuscated in the detection process. Prior works proposed graph-based learning methods to solve this problem using API-level behavior relations. However, the malware detection is still challenging, due to the ignore of the temporal correlation between malicious behaviors. In this study, we model the software behaviors with multiscaled API graph sequences to represent API-level behaviors as well as graph-level temporal behavior correlations. We then propose a novel Dynamic Evolving Graph Convolutional Network (DEGCN) model to capture dynamic evolving pattern of both local API-level and global graph-level software behaviors. In particular, we first extract the API-level (node) representations to capture the directed graph representations for each time slot. We then propose a Graph-encoding-based Gate Recurrent Unit (GGRU) network to capture the graph-level evolving features and their evolving status. The graph features of different time slots and different graph scales are concatenated to detect whether the software is benign or malicious. Our evaluation with two public benchmarks reports that DEGCN achieves the best performance compared with state-of-the-art algorithms.

The high-speed train control system is essential to the safety and efficiency of train operation. With the rapid increase of high-speed railway (HSR) operating mileage and development of information technology, the disposal flow and methods in emergency response are still based on dispatchers and drivers’ experience within the “layered” architecture of current system. There is a certain gap between current processing methods and effective resolution, which may even cause the spread of delay along with the railway networks. Therefore, we propose an integration system of operation control and online rescheduling to improve the recovery ability of HSR carrying capacity. We first describe the framework, information flow, and disposal process of the current system and analyze the shortcomings in handling emergencies. Then, the basic concept, system structure, and framework of the integration system are introduced. Finally, taking temporary speed restriction caused by strong wind as an example, we also analyze the principle of why and how the integration system can promote the recovery ability of HSR carrying capacity.

Railway accidents inevitably happen around the world although the latest automatic train protection (ATP) or similar train control systems have been widely used. To increase the safety of train control systems, we propose a new train protection solution called movement authority plus (MA+). MA+ is designed to be overlaid onto a current train control system, e.g., European Train Control System Level 2 (ETCS-2), to form a parallel control system. Its system structure and working principles are presented. Redundant train-distance measurement strategies are adopted to ensure its availability. Safety performance of the overall system is evaluated and it is compared with ETCS-2. Benefit from the real-time information exchange between on-board MA+ units and that between them and switch announcement units, MA+ works based on the moving block principle. We calculate the minimum train headway distance required by MA+ and compare it with that required by ETCS-2. The results prove the feasibility of the proposed MA+ solution, i.e., MA+ is able to protect train safety when ETCS-2 fails, and would not interrupt its normal work.

While different location technologies have been used to collect data for trains, the development and validation of new systems remain a challenge. In this paper, a formal approach is presented for the development and verification of a new metro train-to-train distance measurement system. This system is based on the spread-spectrum technology to accomplish distance measurement. Different from existing systems in the air and maritime transport, this system does not require any other localization unit, except for a communication unit. To assist the development of the system, colored Petri nets (CPNs) are used to formalize and evaluate its structure. Based on the CPN model, the system structure is validated. In addition, a procedure is proposed to generate a code architecture from the formal model. The system performance is assessed in terms of the range and accuracy of its detection. Therefore, both mathematical simulation and actual measurements, and model-based estimation validations are implemented. The results indicate that the system is able to carry out distance measurement in metro lines, and the formal approaches are reusable for further development and verification of other systems.

Communication-based train control (CBTC) systems are widely applied in the world. The primary functions are carried out by wireless communications, which implement the communication between train and ground. However, this kind of solution involves too many subsystems and interfaces, which makes the group equipment complex and expensive to maintain. What is more, once the ground control center or the communication channel is out of service, the train must operate in degraded modes, and more human factors will be involved. As a result, the efficiency and safety of the train operation will be affected. In order to significantly increase efficiency and safety, train-to-train (T2T) communication is proposed as a new solution for railway signaling. Hence, the availability and performance analysis of the direct T2T data communication system is essential. In this paper, the system structure is described, and different parameters that can affect the performance of the communication system are discussed. In order to evaluate the system availability and performance, the stochastic Petri nets (SPNs) are applied to formalize the communication system. The numerical analysis is carried out with different parameters, which consider both bit error and transmission rates. The results show that the method applied in this paper can be used to evaluate the performance and availability of the T2T data communication system.

The communication system that is presently applied in the European Train Control System can only support data exchange between vehicles and ground, but the direct vehicle‐to‐vehicle communication is not available. The details of interlocking information and other vehicles’ movements are invisible to drivers who are the last defense to prevent unsafe scenarios. As connected vehicles have been envisioned to enhance transportation efficiency and improve safety, the direct vehicle‐to‐vehicle communication network is involved in this paper to increase the safety of railway transport. In this paper, a new train movement authority (MA+) is proposed. Apart from a wireless communication unit, this system does not require any other infrastructure. With the assistance of vehicle‐centric communication technology, MA+ can detect the condition of switches and trains within a certain scope. In this paper, the system structure of MA+ is proposed. Additionally, different implementation scenarios are also discussed. The detection range is estimated and validated based on mathematical calculation and experimental equations. An application demo of MA+ is presented on the Driver Machine Interface of the onboard equipment. The results indicate that MA+ can be a flexible and scalable system for furthering the improvement of railway safety.