With the ever-growing demand for passenger transportation, a variety of emerging train control approaches are under researched to further improve the efficiency of railway transportation such as the virtual coupling train control technology, where the traditional absolute braking distance control mechanism is replaced by the relative braking distance control approach to shorten the headway between adjacent trains. To enhance the efficiency and safety of the railway transportation system, this paper investigates the multiple trains cooperative control problem under the relative braking distance constraint. First, a dynamic multiple trains motion model is constructed with the consideration of the safe headway distance between consecutive trains. Then, an optimal control model is proposed to compute the train control forces so as to enhance the accuracy of speed and position tracking. However, the relative braking headway constraint is associated with the coupling states among adjacent trains which is time-consuming to be solved directly. To satisfy the real-time requirement of train operation, a trust region method based two-layer framework is further designed to decouple the complicated coupling headway constraints, where the augmented Lagrangian method is adopted to transform the original problem into an unconstrained optimal control problem, and the trust region method is employed to solve the equivalent problem efficiently. Numerical experiments based on the real operational data are implemented to verify the effectiveness and efficiency of the proposed method.

Virtual coupling is considered a key technology for increasing line capacity and enhancing recovery capabilities in emergencies. Real-time generation and optimization of train speed trajectory are fundamental to ensuring safe and efficient train operation under virtual coupling. The autonomous train enables autonomous request line resources and makes decisions, allowing for more flexible coupling and reliable and efficient operation. This paper constructs a speed trajectory optimization model for the autonomous train under virtual coupling, which meticulously considers the effects of line resources, such as switches and routes, on virtual coupling. A Twin Delayed Deep Deterministic Policy Gradient (TD3) is utilized to train the agent to optimize the train speed trajectory in real-time. By integrating the Long Short-Term Memory (LSTM), the agent has a longer history memory and learns a better policy. Moreover, two protection mechanisms involving safe following and switch protection are designed to ensure absolute operation safety of the autonomous train and improve training efficiency. Three numerical experiments based on real data from the Beijing-Shanghai High-Speed Railway are conducted. The proposed method can generate a higher quality train speed trajectory within seconds, achieving an average reduction of over 10% in the objective function compared to the commonly used driving strategy and commercial solver. The protection mechanisms always ensure the safety of the trains, even in unknown operating scenarios. Furthermore, the effect of memory and its length have been analyzed by comparing the proposed method with other deep reinforcement learning methods.

The Industrial Internet of Things (IIoT), a core driver of Industrial 4.0, is considered as one of the most promising revolutionary technologies propelling the evolution of smart manufacturing towards Specialization, Reinforcement, Distinctiveness, and Innovation. The security and efficiency of smart manufacturing depend on the secure and efficient interaction of massive production data among entities. Yet, as a crucial measure of securing entity interactions, current authentication mechanisms overlook the single-point-of-failure issue and lightweight design. Moreover, interaction efficiency is rarely optimized and enhanced from the perspective of communication-supporting nodes. Paramountly, the assurance and optimization of entity interaction security and efficiency are strongly coupled, which is not considered in existing interaction frameworks. This paper designs a three-layer entity interaction framework based on mobile edge computing (MEC) and blockchain technology. Specifically, the double-layer blockchain and MEC-cluster assisted lightweight authentication (BCLA) mechanism is proposed under the three-layer framework to achieve lightweight entity authentication in a weakly centralized manner. To optimize the entity interaction efficiency from joint authentication and transmission, this paper further proposes an industrial edge server (IES) deployment optimization scheme and the proximity policy optimization based IES deployment (PAID) algorithm. The security features and efficiency of the three-layer framework are demonstrated by carrying out security analysis and performance evaluation, which is based on the Hyperledger Fabric platform.

With the rapid development of the Internet of Things (IoT), train control systems have emerged as a successful application scenario. The virtually coupled train set (VCTS), as a new paradigm for train control, relies on more efficient vehicle-to-vehicle and vehicle-to-ground communication to achieve closer train spacing. This enhanced communication allows trains to capture more complex and detailed state information. However, traditional train control algorithms, limited by their data processing capabilities, often cannot fully utilize this additional information, leading to conservative control strategies to ensure safety and stability. Generative Artificial Intelligence (GAI), particularly generative diffusion models, has recently shown great potential in optimizing IoT scenarios by handling more complex environments. This article proposes a GAI-based control algorithm framework that leverages diffusion models to optimize train trajectories. By integrating the extensive real-time data generated by IoT systems, the GAI-driven approach enhances decision-making processes, offering more precise and adaptive control strategies tailored to the demands of VCTS. This framework demonstrates the potential of combining IoT data with GAI to achieve higher control accuracy, ensuring safety and performance in dynamic and complex urban rail transit scenarios. Experimental results validate the effectiveness of the proposed method, highlighting its robustness and adaptability across various conditions.

Virtual Coupling (VC) has emerged as a promising strategy for railway system control, offering significant enhancements to rail-line capacity and addressing uneven transport demand by dynamically minimizing the headway distance between trains when necessary. Consequently, the operating targets of VC rear trains, which can be influenced by operational environments and railway line conditions, must be promptly adjusted based on the spacing from the adjacent leading train. This adjustment ensures operational safety within the train movement authority. Different from traditional continuous prescribed performance control (PPC), this paper proposes a discontinuous reinforced prescribed performance control (RPPC) strategy, which takes into account both actual and converted errors and introduces innovative segmented dynamic control strategies designed for continuous operation time periods and discontinuous impulse points. To prevent unnecessary disruptive switching during transient moments, switching constraints (SCs), based on the speed and position of the rear train, are established. Additionally, leveraging Lyapunov theory, the stability of the closed-loop system for train operation is rigorously proven. Finally, a simulation scenario for a 3-train formation, along with a further data-based semi-physical simulation of a 2-train system, are presented for demonstrating the feasibility of the main results.

The communication network in a tunnel construction site facilitates real-time data exchange, and serves as a backbone for successfully executing construction projects. However, the long and closed spaces, irregular surfaces, and variable topology as tunnel excavation impose rigorous limitations on signal propagation, communication quality and coverage. To alleviate the realistic issues, we introduce a holistic three-phase cooperative relay scheme based on 5G New Radio (NR) vehicle-to-everything (V2X) architecture, which can extend the communication range and enhance network throughput. We theoretically derive the outage probability of the entire cooperative relaying process from source to destination, and quantify the impact of relaying on construction workflow with relay cost. To minimize the outage probability and relay cost, we formulate a cooperative relay strategies optimization problem and transform the solving procedure into a Markov decision process (MDP). We design a hybrid hierarchical proximal policy optimization (HH-PPO) reinforcement learning method to solve the MDP, which consists of two discrete actor networks, two continuous actor networks, and two critic networks. The hybrid structure enables HH-PPO to tackle the mixed action space, and the hierarchical structure enables adaptive and contextual actions generation by integrating the discrete network outputs into the continuous actor network. Simulation results validate the effectiveness of the HH-PPO algorithm with faster convergence speed, and show superior performance in terms of lower, stable outage probability and relay cost satisfaction compared with another benchmark.

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 virtual coupling technology is a promising train control system that connects vehicles through wireless communication instead of physical train couplers. For the virtually coupled train dynamic control system, the strategy for dynamic merge control is one of the most crucial problems to achieve the dynamic formation for train sets with different speed. This article proposes a dynamic merge controller based on the prescribed performance control method. The reference speed trajectories are calculated under different scenarios with respect to the variety of conditions between the leading and following trains. The designed controller can make both the tracking distance error and the speed error asymptotically converge to the zero state, and meanwhile satisfies the transient-state and steady-state performances in presence of unknown external disruptions and uncertain train parameters. Experimental results are provided to demonstrate the performance of designed strategy in enhancing the precise control the virtually coupled trains.

Railway scene status perception is the foundation for ensuring the efficient and safe operation of trains. Achieving comprehensive railway scene perception requires not only detecting objects but also understanding the relationships between them. However, current railway scene perception technologies struggle with the latter, while general object and relationship perception methods perform poorly when facing railway scene with significant structural features and small object characteristics. Thus, this paper proposes Rail-former, the first status perception framework specifically designed for railway scene. Rail-former operates based on two key components: the Railway Feature Enhancement Module (RFEM) and the Cross-Coupled Transformer (CCTF). RFEM first leverages a cluster-based normalization evaluation method to condense railway scene structural features in spatial domain. It then applies high-dimensional information compensation to enhance small-object feature representation. The CCTF implements a cross-coupled structure between object decoder and triplet decoder based on a novel interactive attention mechanism, rather than conventional single-decoder or parallel dual-decoder structures, enabling guided enhancement of both object features and relationship features in railway scene. Additionally, we introduce a multimodal matching loss function that incorporates masks, categories, and bounding boxes to mitigate overfitting to a single modality during training. To the best of our knowledge, Rail-former is the first work in the railway domain that leverages panoptic parsing results to reveal relationships. Extensive experiments conducted on both our self-constructed railway dataset and a public dataset demonstrate the superior performance of our approach, achieving accuracy rates of 49.9% and 37.4%, surpassing the best competing methods by 7.8% and 1.1%, respectively.

With the extensive adoption of information technology, tunnel construction is experiencing a rapid digital transformation. Integrating powerful direct communication among construction equipment (CE) facilitates real-time data exchange, promoting collaborative operations among CE. Concurrent execution of multiple construction procedures leads to a significant rise in the amount of CE and communication links, resulting in resource competition. However, this competition is aimed at enhancing collaboration. To address this inherently contradictory issue, we propose a hierarchical resource management framework and align communication quality of service (QoS) to construction efficiency using construction procedure coherence degree (CPCD) based on age of information (AoI). By formulating resource management as a stochastic optimization problem, a suitable online two-level deep reinforcement learning algorithm referred to as diffusion based soft actor critic (DSAC)-QMIX is designed to derive the radio resource allocation strategies. DSAC is responsible for orchestrating spectrum inter-fleets at the high-level, and QMIX makes the resource management and power control decision for each CE at the low-level. Simulation results validate the effectiveness of the DSAC-QMIX algorithm with comparable transmission rate, and show superior performance in terms of CPCD satisfaction compared with other benchmarks.

As the demand for coal and other ore resources increases, the hauling capacity of heavy-haul railways is severely challenged. Virtual coupling technology has gained attention for its ability to improve operational efficiency in bottleneck sections and reduce the time it takes for trains operating on the line to resume normal operation during emergencies. In this article, virtual coupling-based timetable rescheduling method is proposed to reduce the delays under disruptions and improve the line capacity. A mixed-integer linear program (MILP) model that allows trains to be coupled either at departure or by sharing the same arrival and departure line is formulated to reduce the delay time and its propagation range. The strategies of retiming, rearranging tracks, and virtual coupling are adopted to collaboratively optimize the deviation in train schedules and track utilization under disruptions, aiming to enhance the occupancy capacity of arrival and departure lines while simultaneously reducing train delays. A heuristic algorithm utilizing simulated annealing (SA)-particle swarm optimization (PSO) algorithm is developed to generate optimal train coupling and stopping schemes. Numerical experiments are conducted to verify the effectiveness of the proposed model and heuristic algorithm on a real heavy-haul railway configuration. The results demonstrate that our method effectively reduces train delays and minimizes the impact of track utilization on adjacent stations, as well as the repercussions of train delays on subsequent stations.

On a passenger-freight collinear railway, the freight train operation level is comparatively low, frequently resulting in significant deviations from the original timetable and crew plan in the presence of various interferences. This article focuses on the problem of coordinated rescheduling of train timetable and crew scheme in the presence of disruptions on a double-track passenger-freight collinear railway. We develop a mixed-integer linear program (MILP) model considering the distinct priorities of passenger and freight trains, as well as crew operations, thereby surpassing the current practice of independently adjusting train timetable and crew plan to achieve a collaborative solution. The objective is to minimize delays for passenger trains and deviations in crew schedule, while maximizing the delivery rate of freight trains at railway Bureau boundary stations prior to the settlement time. Furthermore, for large-scale delays, we design a solution algorithm based on the rolling horizon approach to enhance computational efficiency. To validate the effectiveness of the proposed model, simulation experiments are conducted using actual running data from the Beijing–Shanghai railway. The experimental results illustrate that our coordinated model enhances the feasibility of adjustment outcomes during emergencies, in contrast to the model that neglects crew connections. Additionally, our proposed algorithm guarantees a solving error of under 5% and reduces solving time by over 60% compared with the results obtained by CPLEX. Moreover, three additional comparison experiments are conducted to further demonstrate the impact of crew activities on train operation adjustments, which also indicate that our approach can provide dispatchers with more feasible train operation adjustment schemes in terms of crew utilization.

Urban Rail Transit Systems (URTS) have increasingly become the backbone of modern public transportation, attributed to their unparalleled convenience, high efficiency, and commitment to sustainable green energy. As we witness a global resurgence of urban rail transit, it becomes evident that most existing URTS still operate on a level of suboptimal intelligence, with their operation and maintenance methods lagging behind other advanced urban transit systems. URTS generate considerable data, offering substantial opportunities for service quality enhancements. Machine Learning (ML), with its demonstrated proficiency in extracting valuable insights from vast data, hold significant promise in the quest to empower URTS. This survey presents a comprehensive exploration of the potential application of ML in URTS. Initially, we delve into the existing challenges of URTS, thereby elucidating the compelling motivation behind the integration of ML into these systems. We then propose a taxonomy of ML paradigms and techniques, discussing in-depth their potential applications in URTS, encompassing perception, prediction, and optimization tasks. Subsequently, we scrutinize a plethora of ML-empowered URTS application scenarios, including but not limited to obstacle perception, infrastructure perception, communication and cybersecurity perception, passenger flow prediction, train delay prediction, fault prediction, remaining useful life (RUL) prediction, train operation and control optimization, train dispatch optimization, and train ground communication optimization. Finally, we present an insightful discussion on the challenges and future directions for URTS, aiming to harness the full potential of ML techniques to deliver superior service and performance.

The increasingly intense cyber-attacks have always been a crucial issue to the communication-based train control (CBTC) system due to exposed wireless channels. Both cyber-attack intrusion detection and defense policy calculation demand substantial computing resources. Combined with high capacity and reliability 5G technologies, edge intelligence (EI) is believed to help empower CBTC systems in terms of security and efficiency. This paper proposes an EI-enabled structure for CBTCs to defend against cyber-attacks, where the EI server provides real-time intelligent computing services for trains to derive real-time defense policies. We formulate the cyber-attack and defense process in EI-enabled CBTCs as a two-level game model, where system security and edge computing resource allocation are jointly optimized. In the lower-level game, we model interactions between the cyber attacker and system defender as a discrete repeated security game (DRSG), which is also a non-zero sum and incomplete information game. The fictitious play (FP) is introduced to derive a Nash equilibrium (NE) based optimal defense scheme. In the upper-level game, considering that the EI server cannot simultaneously update the optimal defense scheme for all trains due to the limited computation resources, we construct a multi-stage computation resource allocation game (MCRAG). We derive the optimal computation resource allocation scheme by the neural fictitious self-play (NFSP), where a deep Q-learning network (DQN) and a supervised learning network are jointly built to learn the strategy. Extensive simulation results show that our proposed EI-enabled CBTC system and the two-level game model can effectively defend against various attacks.

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.

With the acceleration of urbanization, the dynamic passenger flow has an ever-growing impact on the actual train operation. In this paper, we propose a learning based intelligent train regulation method with dynamic passenger flow prediction. To capture the characteristics of the dynamic metro passenger flow, a convolutional neural network is established to predict the real-time passenger flow from two dimensions including space and time. As the prediction accuracy is restricted by the insufficiency of the practical passenger flow data, a deep convolutional generative adversarial network is constructed to generate data that have the same distribution as the original passenger flow dataset. Then, by considering the effects of the dynamic passenger flow on the train operation and the train capacity constraints, the dynamic train regulation is formulated as a multi-stage optimal control problem with the objective function of minimizing the train traction energy consumption and the total traveling time of passengers. To efficiently obtain the optimal regulation strategy at each decision step, a deep Q-network algorithm is proposed to solve the formulated problem such the dimensionality curse caused by the excessive state space is avoided. The numerical experiments demonstrate the high efficiency and effectiveness of our proposed algorithm and model.

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.

With the great advances in computer science and communication technology, more and more control systems are implemented as distributed networked control systems (DNCSs). Due to the nature of the time delay of communication networks, it is of importance to investigate how communication delays affect the systems from different perspectives. Most of the literature by far focus on analyzing the impacts of time delays on the system stability or safety control with mathematical models (i.e., differential equations), which are of interest in the early phases of the system development (e.g., the conceptual phase). However, in the later phases of the system development (e.g., architecture design or system implementation), qualitative as well as quantitative safety analysis based on system models that describe the concrete structures, interactions between components, and state transitions of the underlying systems is desirable. Additionally, the availability of a control system is of interest from the perspective of operation. This article studies the impacts of communication delays on the safety and availability of DNCSs by a colored-Petri-net-based approach. To exemplify the proposed approach, a simplified communication-based train control system is presented.

Train delay prediction can improve the quality of train dispatching, which helps the dispatcher to estimate the running state of the train more accurately and make reasonable dispatching decision. The delay of one train is affected by many factors, such as passenger flow, fault, extreme weather, dispatching strategy. The departure time of one train is generally determined by dispatchers, which is limited by their strategy and knowledge. The existing train delay prediction methods cannot comprehensively consider the temporal and spatial dependence between the multiple trains and routes. In this paper, we don’t try to predict the specific delay time of one train, but predict the collective cumulative effect of train delay over a certain period, which is represented by the total number of arrival delays in one station. We propose a deep learning framework, train spatio-temporal graph convolutional network (TSTGCN), to predict the collective cumulative effect of train delay in one station for train dispatching and emergency plans. The proposed model is mainly composed of the recent, daily and weekly components. Each component contains two parts: spatio-temporal attention mechanism and spatio-temporal convolution, which can effectively capture spatio-temporal characteristics. The weighted fusion of the three components produces the final prediction result. The experiments on the train operation data from China Railway Passenger Ticket System demonstrate that TSTGCN clearly outperforms the existing advanced baselines in train delay prediction.

Virtually coupled train sets (VCTS) have been proposed to increase the transportation capacity and the flexibility of railway organization. Due to the lack of reliable wireless communications and accurate perceptual information, the promotion of VCTS was challenged. With the development of industrial Internet of Things (IIoT), an IIoT-based VCTS is built in the article based on the popular communication-based train control architecture. Considering the dynamic and complex operation environment, it is difficult to achieve the efficient cooperative control of VCTS. The reason is that the traditional method is frequently trapped into a local optimization. To resolve the problem, we apply reinforcement learning (RL) to obtain an optimal policy for the IIoT-based VCTS, where the traditional artificial potential field (APF) is taken to develop the reward function. RL can thus search the global optimal policy, whereas APF can help RL to reduce the computation complexity. This can substantially increase the efficiency of the proposed approach. Simulation results confirmed that the proposed RL-based cooperative control approach would bring excellent performance in the IIoT-based VCTS.

While communication-based train control (CBTC) systems play a crucial role in the efficient and reliable operation of urban rail transits, its high penetration level of communication networks opens doors to Man-in-the-Middle (MitM) attacks. Current researches regarding MitM attacks do not consider the characteristics of CBTC systems. Particularly, the limited computing capability of the on-board computers prevents the direct implementation of most existing intrusion detection and defense algorithms against the MitM attack. In order to tackle this dilemma, in this article, we first introduce edge intelligence (EI) into CBTC systems to enhance the computing capability of the system. A cross-layer defense scheme, which includes the detection and defense stages, are proposed next. For the cross-layer detection stage, we propose a Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) based detection method to combine the detection probability calculated from the train control parameter sequence and operation log files. For the cross-layer defense stage, we construct a Bayesian game based defense model to derive the optimal defense policy against MitM attacks. To further improve the accuracy of the defense scheme as well as optimize the communication resource allocation scheme, we propose an optimal communication resource allocation scheme based on the Asynchronous Advantage Actor-Critic (A3C) algorithm at last. Extensive simulation results show that the proposed scheme achieves excellent performance in defending against MitM attacks.

This paper investigates the robust distributed cruise control problem of multiple high-speed trains under external disturbances. First, by modeling each train as a cascade of point masses connected by spring-like couplers, the longitudinal interaction between adjacent cars are represented by the connected topological graph. Then, under the framework of the communication-based train control technology, the interaction of desirable speed information among trains and the wayside control center is described by the directed topological graph. Next, a distributed cruise controller is designed by taking advantages of the graphic theory such that the multiple trains track different target speeds, and both the distance of neighboring cars and the headway of successive trains are kept in appropriate ranges. Finally, to eliminate the influence of external disturbances, we adopt the disturbance observer to approximate the perturbations, and present a sufficient condition for the existence of the distributed control strategy and the observer gain parameter in form of the linear matrix inequality (LMI). Numerical experiments illustrate that the composite control law is effective in inhibiting the external disturbances, and guaranteeing the safety, efficiency and comfort of high-speed trains' movement.

The virtual coupling approach has been proposed to improve transportation efficiency, passenger satisfaction and operational excellence for communication based train control (CBTC) systems, and a train formation is built and named virtual coupled train sets (VCTS). In order to ensure the stability of the VCTS, train-to-train (T2T) wireless communications are critical to provide the continuous information exchange channel for adjacent trains. Due to unreliability of wireless communications, jamming attacks are inevitable, which could bring packets loss and time delay and disturb the control performance of VCTS. As the distance between adjacent trains is much less than the traditional headway under the moving block (MB) mode in CBTC systems, the jamming attacks could lead to safety events, which are unacceptable. Therefore, in the paper, an event trigger control (ETC) method is developed to mitigate effects of jamming attacks on VCTS by designing a stability range for VCTS formation and reducing the communications cycle of CBTC systems. This ETC-based cooperative control approach can ensure the stable headway between adjacent trains and improve the transportation efficiency. Simulation results demonstrate availability of the proposed scheme.

New wave of urbanization, ever more stringent emission standards, and high pressure on improving efficiency of private and public transport have made the development of more sustainable transportation systems one of the fundamental societal challenges of the next decade. Connected vehicles have been envisioned to provide enabling key technologies to enhance transportation efficiency, reduce incidents, improve safety, and mitigate the impacts of traffic congestion. The seamless integration and convergence of vehicular communication networks, information and transportation systems, and mobile devices and networks will face a number of technical, economic, and regulatory challenges. It is of paramount importance to (i) design vehicular communication systems that enable road users and other actors to exchange information in real time with high reliability, (ii) enable pervasive sensing to monitor the status of vehicles and the surroundings, (iii) develop data analytics tools for processing large amounts of data generated by the connected vehicles, and (iv) develop middleware platforms for data management and sharing.

In this paper, we propose a network virtualization based integrated train ground communication system for urban rail transit systems. In order to improve the CBTC subsystem performance during handoff, we propose a novel handoff scheme to support handoff between virtual networks. The application layer QoS parameter of the CBTC, PIS and CCTV subsystems is used as the performance measure in the handoff design. The proposed integrated train ground communication system QoS optimization problem is formulated as a Approximate Dynamic Programming (ADP) problem. Extensive simulation results show that the proposed integrated train ground communication system QoS can be improved substantially with our ADP based optimization model.

Communication-based train control (CBTC) is an automated train control system using bidirectional train-ground wireless communications to ensure the safe operation of rail vehicles. Due to unreliable wireless communications and train mobility, the train control performance can be significantly affected by wireless networks. Although some works have been done to study CBTC systems from both train-ground communication and train control perspectives, these two important areas have traditionally been separately addressed. In this paper, with recent advances in cognitive dynamic systems, we take a cognitive control approach to CBTC systems considering both train-ground communication and train control. In our approach, the notion of information gap is adopted to quantitatively describe the effects of train-ground communication on train control. Moreover, unlike the existing works that use network capacity as the design measure, in this paper the linear quadratic cost for the train control performance in CBTC systems is considered in the performance measure. Reinforcement learning is applied to obtain the optimal policy based on the performance measure, which includes linear quadratic cost and information gap. In addition, the wireless channel is modeled as finite-state Markov chains with multiple state transition probability matrices, which can demonstrate the characteristics of both large-scale and small-scale fading. The channel state transition probability matrices are derived from real field measurement results. Simulation results show that the proposed cognitive control approach can significantly improve the train control performance in CBTC systems.

Communication-Based Train Control (CBTC) system is an automated train control system using bidirectional train-ground communications to ensure the safe operation of rail vehicles. Due to unreliable wireless communications and frequent handoff, existing CBTC systems can severely affect train control performance. In this paper, we use recent advances in Coordinated Multi-Point transmission and reception (CoMP) to enhance the train control performance of CBTC systems. In addition, unlike the exiting works on CoMP, linear quadratic cost for the train control performance in CBTC systems is considered as the performance measure. Moreover, we propose an optimal guidance trajectory calculation scheme in the train control procedure that takes full consideration of the tracking error caused by handoff latency. Simulation results show that the train control performance can be improved substantially in our proposed CBTC system with CoMP.

Communication-based train control (CBTC) is being rapidly adopted in urban rail transit systems, as it can significantly enhance railway network efficiency, safety, and capacity. Since CBTC systems are mostly deployed in underground tunnels and trains move at high speeds, building a train-ground wireless communication system for CBTC is a challenging task. Modeling the tunnel channels is very important in designing the wireless networks and evaluating the performance of CBTC systems. Most existing works on channel modeling do not consider the unique characteristics of CBTC systems, such as high mobility speed, deterministic moving direction, and accurate train-location information. In this paper, we develop a finite-state Markov channel (FSMC) model for tunnel channels in CBTC systems. The proposed FSMC model is based on real field CBTC channel measurements obtained from a business-operating subway line. Unlike most existing channel models, which are not related to specific locations, the proposed FSMC channel model takes train locations into account to have a more accurate channel model. The distance between the transmitter and the receiver 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) is gradually adopted in urban rail transit systems, as it can significantly enhance railway network efficiency, safety and capacity. Since CBTC systems are mostly deployed in underground tunnels and trains move in high speed, building a train-ground wireless communication system for CBTC is a challenging task. Modeling the tunnel channels is very important to design and evaluate the performance of CBTC systems. Most of existing works on channel modeling do not consider the unique characteristics in CBTC systems, such as high mobility speed, deterministic moving direction, and accurate train location information. In this paper, we develop a finite state Markov channel (FSMC) model for tunnel channels in CBTC systems. The proposed FSMC model is based on real field CBTC channel measurements obtained from a business operating subway line. Unlike most existing channel models, which are not related to specific locations, the proposed FSMC channel model takes train locations into account to have a more accurate channel model. The distance between the transmitter and the receiver 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.

In subway systems, the energy put into accelerating trains can be reconverted into electric energy by using the motors as generators during the braking phase. In general, except for a small part that is used for onboard purposes, most of the recovery energy is transmitted backward along the conversion chain and fed back into the overhead contact line. To improve the utilization of recovery energy, this paper proposes a cooperative scheduling approach to optimize the timetable so that the recovery energy that is generated by the braking train can directly be used by the accelerating train. The recovery that is generated by the braking train is less than the required energy for the accelerating train; therefore, only the synchronization between successive trains is considered. First, we propose the cooperative scheduling rules and define the overlapping time between the accelerating and braking trains for a peak-hours scenario and an off-peak-hours scenario, respectively. Second, we formulate an integer programming model to maximize the overlapping time with the headway time and dwell time control. Furthermore, we design a genetic algorithm with binary encoding to solve the optimal timetable. Last, we present six numerical examples based on the operation data from the Beijing Yizhuang subway line in China. The results illustrate that the proposed model can significantly improve the overlapping time by 22.06% at peak hours and 15.19% at off-peak hours.

Communication-Based Train Control (CBTC) system is an automated train control system using bidirectional train-ground communications to ensure the safe operation of rail vehicles. Handoff design has significant impacts on the train control performance in CBTC systems based on multi-input and multi-output (MIMO)-enabled WLANs. Most of previous works use traditional design criteria, such as network capacity and communication latency, in handoff designs. However, these designs do not necessarily benefit the train control performance. In this paper, we take an integrated design approach to jointly optimize handoff decisions and physical layer parameters to improve the train control performance in CBTC systems. We use linear quadratic cost for the train controller as the performance measure. The handoff decision and physical layer parameters adaptation problem is formulated as a stochastic control process. Simulation result shows that the proposed approach can significantly improve the control performance in CBTC systems.

Leaky Coaxial Cables (LCXs) are widely used in tunnels and underpasses for smoother electric field coverage in wireless communication systems. But there are very few studies on LCXs working in 2.4GHz, which are the frequency bands used in 802.11b/g. The paper shows characteristics of 2.4GHz LCX through simulations and field tests. The results show that the communication through 2.4GHz frequency band LCX can provide stronger radio signals and thus better throughput compared with free space in tunnels. What's more, the handover interruption time between APs can be reduced because the Ping-Pong handover is avoided through LCX.