IoT-based virtual coupling systems enable reduced headways for high-speed trains, yet most Virtually Coupled Train Set (VCTS) studies still rely on a leader-follower paradigm where followers simply match the leader’s speed. Under position-dependent slopes/speed limits, asynchronous departures, and uncertain train dynamics, it remains difficult to jointly achieve traffic efficiency, cooperative tracking accuracy, and ride comfort. This paper proposes a hierarchical multi-objective Model Predictive Control (MPC) framework for follower trains in VCTS with amulti-stepdecision structure consisting of an offline tightening design and an online two-step regulation procedure.Offline, we solve an optimization-based tightening design that jointly determines tightened constraint sets and disturbance-affine feedback gains under Automatic Train Protection (ATP) supervision, improving traffic-efficiency potential by reducing conservatism that can otherwise trigger unnecessary braking actions and degrade throughput.Online, a two-step regulation is executed under uncertainty: first, a tracking-prioritized robust MPC enforces ATP-compliant operation; second, using the resultingoptimal and feasibletracking-cost information consistent with the offline-designed feedback gains, we construct a Lyapunov-based constraint that yields an explicit tracking-cost tolerance bound, within which a comfort-oriented MPC improves ride comfort. A comfort-aware adaptive mechanism then tunes the tolerance online for more consistent tracking-comfort behaviour. The proposed distributed framework is proved to be recursively feasible and input-to-state stable. Simulations on the Beijing-Tianjin high-speed railway line with CR400BF parameters demonstrate that, compared with representative weighted-sum and lexicographic baselines, the proposed method achieves a more reliable tracking-comfort trade-off with less conservative traffic-efficiency performance.

The synchronous arrival of trains in a virtually coupled train set (VCTS) is crucial to ensure efficient operations. The primary objective of VCTS is to decrease the inter-train spacing while stopping, aiming to minimize the time difference between arrivals. In this paper, we propose a distributed cooperative model predictive control-based (DCMPC) approach for synchronous arrival of VCTS. Firstly, the dynamics model of the unit train in VCTS is constructed, which considers external uncertainties. Then, a novel dynamic penalty matrix updating method is introduced to improve the accuracy and efficiency of unit train stopping. We also present the design of terminal constraints on the DCMPC control problem and the stability proof to ensure the feasibility of the controller. Finally, the effectiveness of the proposed method is verified through simulation experiments with concrete data from the Chengdu Metro Line NO.8 in China. Compared to conventional control approaches, the proposed DCMPC significantly reduces the arrival time difference while increasing the average stopping speed, resulting in a considerable 4% increase in the passing capacity of the line.

This study addresses the issue of communication link interruptions in train platoon control under the complex operating environment of high-speed railways. An adaptive trajectory tracking control approach based on a finite-time sliding mode is proposed under jointly connected switching topologies. The proposed framework ensures platoon consensus under jointly connected switching topologies, where the leader’s information need not reach all followers in each topology but only collectively over a finite interval, thus relaxing connectivity requirements and enhancing practicality. Within this framework, the sliding surface is designed to incorporate relative error signals determined by the communication topology, and an adaptive mechanism is employed to effectively handle unknown external disturbances without requiring prior knowledge of their bounds or derivatives. Simulation results demonstrate that, compared with MPC and non-adaptive approaches, the proposed strategy reduces position errors by approximately 80% and velocity errors by around 40%, significantly improving platoon tracking accuracy. Furthermore, simulations under both periodic and random topology switching indicate that periodic switching achieves performance closer to that of a fully connected topology, further enhancing trajectory tracking effectiveness.

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.

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.

This paper presents a distributed model predictive control (DMPC) approach for a virtually coupled train set (VCTS) with communication delays. Specifically, we stabilize the real-time states of VCTS and guarantee the satisfaction of safety constraints in automatic train protection systems with delayed states, which fills the gap in the existing literature. First, using the predicted trajectories at previous instants, the real-time states of VCTS are estimated from delayed states obtained through communication. Then, compatibility constraints are designed to regulate predicted control inputs, such that the error between the predicted and actual trajectories in the prediction horizon can be confined in a sequence of sets. Next, linear matrix inequalities are derived to tune cost functions, terminal sets and terminal controllers, which guarantee the stability and recursive feasibility of DMPC. Finally, experimental results demonstrate the performance of our approach in both cruising and speed-varying operations. The minimal spacing policies are evaluated with different communication delays and parameters in DMPC based on our mathematical designs.

The emerging virtual coupling technology aims to connect multiple train units as a virtually coupled train set (VCTS) with outstanding operation efficiency and flexibility. This paper studies the nonlinear formation control of VCTS, particularly considering the uncertainties from external disturbances and the practical modeling of formations. To solve this problem, we propose a distributed robust nonlinear model predictive control approach using tube techniques. Based on the analysis of contractiveness, nonlinear tubes are constructed to confine the uncertain trajectory influenced by uncertainties from the formation model, disturbances and distributed prediction. Specifically, to facilitate tube construction, compatibility constraints are deployed to restrict uncertainties from distributed prediction. Furthermore, the robust constraint satisfaction, recursive feasibility and stability of VCTS operations are established. Finally, based on the practical data in field tests, comparative experiments are conducted to demonstrate the advantages of our approach in control performance and computational efficiency.

The operation of urban rail transit is inevitably affected by disturbances in practice, causing the original train timetable and rolling stock circulation to be infeasible. This paper addresses the real-time train rescheduling problem through a novel two-step optimization framework. To realign train operations with the original plan, the first step manages traffic flow by optimizing dispatching measures, including retiming, cancellation, short-turning, and backup rolling stock utilization. To maintain the highest possible service quality during the transition period, the second step introduces stop-skipping and further fine-tunes the train timetable. Both steps are formulated as mixed-integer nonlinear programming models, and the second-step model is heuristically decomposed. For computational tractability, mathematical models are transformed using some linearization techniques and then solved according to the prescribed procedure. Numerical experiments based on a small-scale case and real-world data of the Beijing Yizhuang Metro Line show that the proposed two-step optimization framework can satisfy the real-time requirements and outperform the current rescheduling method employed in the automatic train supervision system. Furthermore, the framework is proven to be applicable to different disturbance durations and locations.

Virtually Coupled Train Set (VCTS) is an advanced train control technology enabling multiple trains to operate closely through wireless communication, enhancing capacity and operational flexibility. Traditional VCTS control algorithms struggle with complex dynamic models and local optimality, hindering real-time, long-term optimization. This paper proposes an Edge Intelligence (EI) enhanced Monte Carlo Tree Search (MCTS) framework for VCTS Optimal Control (M-VOC). MCTS is a heuristic search algorithm that identifies optimal operational solutions efficiently, focusing on long-term stability over local optimums. EI supports MCTS for real-time decision-making, and we introduce a model-based reinforcement learning algorithm to manage VCTS's complex dynamics. Our framework addresses VCTS control issues in real-time while optimizing long-term benefits. To meet computational and real-time demands, we propose a train-to-edge cooperative computing strategy using multi-intelligence reinforcement learning. Simulations demonstrate that our EI-enhanced MCTS strategy effectively provides cooperative control, ensuring virtually coupled trains operate safely, stably, and punctually with reduced intervals.

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.

Abstract The precise perception of the surrounding environment in traffic scenes is an important part of an intelligent transportation system. The event camera could provide complementary information to traditional frame‐based cameras, such as high dynamic range, and high time resolution, in the perception of traffic targets. To improve the precision and reliability of perception as well as facilitate lots of RGB camera‐based studies introduced to event cameras directly, a refined registration method for event‐based cameras and RGB cameras on the basis of pixel‐level region segmentation is proposed, to provide a fusion method at pixel level. A total of eight sequences and a dataset containing 260 typical traffic scenes are contained in the experiment dataset, both selected from DSEC, a traffic event‐based dataset. The registered event image shows a better spatial consistency with RGB images visually. Compared to the baseline, the evaluation indicators, such as the performance of the contrast, the proportion of overlapping pixels, and average registration accuracy have been improved. In the traffic object segmentation task, the average boundary displacement error of our method has decreased and the max decline value has reached 79.665%, compared to the boundary displacement error between ground truth and baseline. These results indicate prospective applications in the perception of intelligent transportation systems combined with event and RGB cameras. The traffic dataset with pixel‐level semantic annotations will be provided soon.

Arriving phase is an essential part of the virtually coupled train set (VCTS) operation in metros and significantly influences efficiency. This paper proposes a synchronous and precise control problem in VCTS arriving-phase operation and designs a hierarchical control approach to solve this problem. First, the control problem of VCTS arriving-phase operation is mathematically modeled, capturing the uncertain disturbances and measurement errors in train dynamics and considering input and safety constraints. Then, a two-layer control framework is designed, with the upper layer for coordinated trajectory planning and the lower layer for offset-free tracking of each train (unit) in VCTS. Specifically, in the upper layer, all units in VCTS are coordinated in an optimization problem to generate the reference trajectories that increase the synchronicity of arrival; in the lower layer, to track the reference trajectory, we propose an offset-free model predictive control (MPC) approach, which is based on an observer to handle uncertain disturbances and measurement errors. The stability of the offset-free MPC approach is proved, such that all units in VCTS can arrive at the station synchronously and precisely. Finally, experimental results demonstrate the performance of the proposed hierarchical approach.

Train breakdowns have significant negative impacts on metro systems and passengers. In this context, the implementation of a train-to-train rescue serves as a crucial mechanism to restore operations promptly. This paper proposes a train rescheduling approach in the case of a train breakdown, incorporating a train-to-train rescue on a metro line. The train-to-train rescue procedure is formulated into two sub-models according to the depot position and the operating direction of the faulty train. The train timetable rescheduling and the rolling stock rescheduling problems are simultaneously considered in this paper by leveraging advanced rescheduling strategies such as the flexible short-turning and adding backup trains. Subsequently, a two-stage approach is developed to solve the model. Additionally, some valid inequalities are proposed to improve the lower bound of the model. Simulations are carried out using a case study based on the real-world data from the Beijing metro Yizhuang line to verify the effectiveness of the train rescheduling model. The proposed algorithms achieve high-quality solutions within a reasonable timeframe, surpassing the efficiency of the CPLEX optimizer.

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.

With advancements in sensor technology, high dimensional signals such as functional curves and images are typically collected from multiple sensors to characterize the degradation of a system. Data fusion methods are employed to integrate multisensor signals generated from the system into a scalar health index (HI) to understand the degradation status of the system. This paper develops sparse group LASSO-principal component analysis (SGL-PCA), a method that constructs HIs for image and profile data. First, we remove the smooth background from each sensor signal. Then, we solve the degradation patterns and the degradation paths through a rank-one matrix approximation problem, with the consideration of the sparsity of the measurements related to the degradation process and the monotonicity of the degradation paths. Results from a simulation study and a case study illustrate that the HI constructed by the proposed method outperforms the benchmark methods in identifying the measurements subject to the degradation process and predicting the remaining useful life of the system. Note to Practitioners—In practice, sensors generating multiple high-dimensional curves and images are often installed in systems to characterize their degradation status. Compared with scalar sensor signals, the information that associates with the degradation process often appears in sparse regions from the sensor signals. Therefore, identifying the degradation information accurately is important in the health index (HI) construction for degradation modeling and prognostic analysis. This article proposes a method that simultaneously selects the degradation information and estimates the optimal weights for integrating multi-sensor signals in constructing the HI. The proposed method is applicable in the case where the systems degrade under a single failure mode, and multiple sensors are used to monitor the degradation processes. Practitioners can implement our method to predict the remaining useful lives of in-service systems through three steps: (1) estimate the backgrounds of each sensor and derive data-fusion model using a historical dataset; (2) construct the HIs of in-service systems; (3) predict the remaining useful lives of these systems based on the developed HIs.

The energy-efficient train control (EETC) problem is investigated in this article. And a soft actor-critic (SAC)-based method is proposed to optimize the train driving strategy. First, EETC problem is converted to the inverse problem, i.e., minimizing the trip time of the journey with constant energy consumption. Based on the conversion, the EETC problem is reformulated as a finite Markov decision process, which can be solved by deep reinforcement learning algorithms. Second, an optimization method based on the SAC method is designed to calculate the optimal driving strategy of the train with introducing the reservoir sampling method. Finally, some case studies are conducted to verify the effectiveness and performance of the proposed method. Simulation results demonstrate that a good energy-saving performance can be achieved. In single interval, the SAC-based method can reduce about 1.65% of the energy consumption compared with numerical method. And the energy consumption reduction can be extended to be 6.49% when the proposed approach is applied in multiple intervals.

Edge intelligence (EI) is becoming one of the research hotspots among researchers, which is believed to help empower intelligent transportation systems (ITS). ITS generates a large amount of data at the network edge by millions of devices and sensors. Data-driven artificial intelligence (AI) is at the core of ITS development. By pushing the AI frontier to the network edge, EI enables ITS AI applications to have lower latency, higher security, less pressure on the backbone network and better use edge big data. This paper surveys Edge Intelligence in Intelligent Transportation Systems. We first introduce the challenges ITS faces and explain the motivation of using EI in ITS. We then explore the framework of using EI in ITS, including the EI-based ITS architecture, the data gathering and communication methods, the data processing and service delivery, and the performance indexes. The enabling technologies, such as AI models, the Internet of Things, and Edge Computing technologies used in EI-based ITS, are reviewed intensively. We discuss the edge intelligence applications and research fields in ITS in depth. Typical application scenarios, such as autonomous driving, vehicular edge computing, intelligent vehicular transportation system, unmanned aerial vehicle (UAV) in ITS environment, and rail transportation control and management, are explored. The general platforms of EI, the EI training and inference in ITS, as well as the benchmark datasets, are introduced. Finally, we discuss some of the challenges and future directions of using EI in ITS.

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.

Railway turnout is the key infrastructure for trains to change their routes. In order to ensure the smoothness and safety of the train’s passing through turnouts, the insufficient displacement (ID) of switch rails after their conversions must be controlled within a permitted range. During design stage, it is quite important for the reduction of the ID to reasonably arrange the tractive points. In the existing literature, a feasible tractive layout is commonly suggested through the manual analysis using the finite element model of the switch rail. However, as the design space may explode for long rails with multiple tractive points, it is time-consuming for such labor-intensive methods to search a feasible tractive layout, and the result may be non-optimal. Therefore, it is necessary to develop an efficient method to arrange the tractive points optimally in order to minimize the ID. To this end, we propose a physics-informed optimization method for the design of the tractive layout. First, a tailored direct stiffness method is introduced to accurately estimate the ID given any tractive layout and frictions. On this basis, we establish an optimization model for the selection of the tractive locations with the objective of minimizing the expectation of the ID. To address the sparsity issue of the decision variable, an Encoding Rule with a hierarchical indexing method is proposed to improve the efficiency of genetic algorithm. Next, the number of tractive points is determined. Finally, several sets of experiments are conducted to demonstrate the effectiveness of the proposed method, which decreases the ID by 25.61% and 12.7% in terms of the maximal and mean values for the case with the switch of length 44.1m.

Due to the inventible disruptions caused by e.g., flood, hurricane and blizzard, metro managers in recent years have gradually shifted their attention from prevention of disruptions to ability to withstand and quick recovery from these disruptions, hence the need for enhancing the resilience of an urban rail system. In this paper, we propose a resilience-oriented train rescheduling framework, which helps the rail transit system recover to the normal state as soon as possible in case of disruptions, with the help of pre-allocated rolling stocks at the depots, side tracks and timetable rescheduling of grains. Specifically, we first construct an event-activity network for an urban rail line with multiple depots and side tracks, in which the arrival and departure of trains are modeled as a set of events. Several groups of decision variables and linear constraints are denoted to model the rescheduling of trains. Considering the use of short-turning train rescheduling strategy and pre-allocated rolling stocks, we then formulate the problem into a mixed-integer linear programming (MILP) model, where the objective is to maximize the resilience of the urban rail line against disruptions. Through the analysis of model properties, we develop a branch-and-cut algorithm by deriving a series of linear inequalities, which we prove are valid inequalities, to strength the tightness of the MILP model. Finally, numerical experiments based on real-world data of Beijing metro are conducted to verify the effectiveness of our approach.

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.

Railway traffic management focuses on regulating train movements and delivering improved service quality to passengers; however, such efforts are subject to many uncertainties in terms of disruptions and passenger demand on a rail transit line. In contrast to most existing studies, which focus on the rescheduling of passenger timetables in a deterministic framework, this study proposes a two-stage stochastic optimization model for allocating backup rolling stocks (BRS) to storage lines to reschedule the timetable and serve passengers delayed by disruptions. The first stage is an assignment problem to determine the optimal plan for the allocation of BRS to storage lines to achieve a good trade-off between the investment cost for the BRS and the expected travel time of delayed passengers across different stochastic scenarios. The second stage is explicitly formulated as a network flow model to optimize the timetable of the delayed trains on the tracks and the BRS from the storage lines such that the passenger travel time is minimized under each stochastic scenario. To improve the efficiency of convergence, we develop an improved L-shaped method with several accelerating techniques. Among these, we show that the classical integer L-shaped cut can be tightened given the property of the second-stage problem, which can also be generalized to other two-stage integer stochastic programs. Real-world case studies based on historical data from the Beijing metro verify the effectiveness of the proposed approach in reducing the travel time for passengers. History: Accepted by Pascal Van Hentenryck, Area Editor for Computational Modeling: Methods & Analysis. Funding: This research was supported by the National Natural Science Foundation of China [Grants 71621001, 71825004, and 71901016]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplementary Information [ https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.1233 ] or is available from the IJOC GitHub software repository ( https://github.com/INFORMSJoC ) at [ http://dx.doi.org/10.5281/zenodo.6892548 ].

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.

Heterogeneous but complementary sources of data provide an unprecedented opportunity for developing accurate statistical models of systems. Although the existing methods have shown promising results, they are mostly applicable to situations where the system output is measured in its complete form. In reality, however, it may not be feasible to obtain the complete output measurement of a system, which results in observations that contain missing values. This article introduces a general framework that integrates tensor regression with tensor completion and proposes an efficient optimization framework that alternates between two steps for parameter estimation. Through multiple simulations and a case study, we evaluate the performance of the proposed method. The results indicate the superiority of the proposed method in comparison to a benchmark. Note to Practitioners—The proposed method aims to obtain an accurate estimation of the regression model when certain entries of the response are inaccessible. By considering both the information from multiple inputs and the structure of the response, our proposed method can achieve more accurate estimation of the output tensor. In order to apply the proposed method in practice, two assumptions should hold. First, the response tensor should be low-rank, meaning that fewer variation patterns should exist in the response than its dimensions. Second, the relationship between the input tensors and the response should be linear or approximately linear. The presented method in this article uses tensor decomposition techniques to exploit the correlation structures of the high-dimensional data and prevent overfitting. Another benefit of our integrated framework is that the rank of the response tensor converges automatically, which can be used directly in the parameter estimation.

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.

In big cities, the metro lines usually face great pressure caused by huge passengers demand, especially during peak hours. When disruptions occur, passengers accumulate quickly at stations. It is of great importance for dispatchers to take passenger flow control into consideration for the traffic management to ensure passengers’ safety and to maintain their satisfaction. This paper proposes an integrated disruption management model, which incorporates train rescheduling and passenger flow control. In this model, the train services can be short-turned, cancelled and rerouted, while the number of passengers entering a station is managed by controlling the station gates with consideration of the capacities of platforms and trains. Moreover, the number of passengers arriving at a station is calculated according to the origin-destination matrices. The objectives are to recover the train operation to the original timetable as soon as possible and to minimize the waiting time of passengers outside the stations. With the interaction between train services, passengers and station gates, an iterative metaheuristic approach is proposed to solve the integrated disruption management problem. Based on the data of a Beijing metro line, numerical experiments are conducted to test the proposed algorithm. The results demonstrate the importance of integrated disruption management and the effectiveness of our solution method.

Railway Automatic Switch (RAS) plays a crucial role in Turnout Switching System (TSS). The size of RAS’s Stationary Contacts (SCs) directly affects connectivity of the pivotal control and feedback circuit, which further influences the remaining useful life of TSS. However, it is impossible to avoid normal wear and tear or fractures of SC during daily operation, resulting in size change of SCs. Therefore, it is vital to monitor the size of SCs. However, due to lack of wear samples, it is hard to design automatic algorithms for this task, especially for developing currently popular deep learning. To this end, this paper proposes a computer vision method forrailway automatic switch stationary contacts wear detection under few-shot occasions.Our method includes two key modules: a Few Shot SC DETection (FSDet) module and a Contour-based Size MEAsurement (CSMea) module, which together form a system that achieves accurate SC detection and size monitoring. The FSDet module formulates a multi-template deep feature matching pipeline, which plays the role of detecting all SCs in an image under the few shot manner. Then, the CSMea module takes the above detected SC patches as input and detects wear regions utilizing contour features and key point features. Finally, size of SCs can be calculated in image level by computing average pixels distance in wear regions and rescaled into real world level using image calibration tools. Experimental results demonstrate that the proposed method can accurately and robustly detect and measure the size of different SC structures in few-shot occasions.

Focusing on improving the operation efficiency and riding comfort of metro rail lines in the peak hours, this article investigates the real-time train regulation and passenger load control problem with respect to frequent disturbances. To better illustrate the relationship between the train timetable and the on-board passengers, the variations of the departure time and the passenger load are elaborated in the form of a state-space model. Based on the Lyapunov stability theory, the problem of minimizing an upper bound on the quadratic performance function is transformed to a dynamic optimization problem with a set of linear matrix inequalities (LMIs), and a predictive control strategy is designed to guarantee the actual train schedule and number of in-vehicle passengers track the nominal timetable and the expected passenger load with a given disturbance attenuation level. With the objective to reduce the computational workloads and cut down the utilization of wireless transmitting resources, an event-triggered strategy is developed to implement the proposed stabilizing feedback controller only when the measurement error exceeds certain threshold, which has better adaptability to the application in large-scale metro networks. Some numerical examples based on the Beijing Yizhuang Metrol Line are provided for illustration of the effectiveness of the proposed scheme.

Currently qualitative as well as quantitative safety analysis of railway systems are usually conducted through Fault Tree Analysis (FTA) and Event Tree Analysis (ETA). FTA and ETA display the causalities and consequences of hazards or accidents by means of linear event sequences, which makes it difficult to incorporate non-linear relationships such as feedback. The repair of failure system components is not considered in traditional FTA and ETA. Moreover, quantitative safety evaluation by FTA and ETA requires that failure events should be statistically independent. Considering these issues, we propose an approach of conducting qualitative as well as quantitative safety evaluation of the CTCS-3 with stochastic Coloured Petri Nets (CPNs). The hierarchical CPN model of the CTCS-3 takes the scenarios, movement of trains, the failure and repair of system components into account. The occurrence times of hazardous events of a CTCS-3 system is quantitatively evaluated with the numerical data collected during the simulation of the system model. The evaluation results demonstrate the feasibility of the proposed approach.

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.

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.

Communication-based Train Control (CBTC) systems are the burgeoning directions for developing future train control systems. With the adoption of wireless communication and network techniques, train control systems are more vulnerable to cyber-attacks. Notably, the jamming attacks, aiming at the handoff process that is the weakest part of train ground communication systems, will cause long disruption of communication. It will have a severe impact on train control operation efficiency. Current research regarding industry control system security is hard to model the impact of the jamming attacks on the train control system quantitatively, and current countermeasure schemes against jamming attacks are not designed for the operating mechanism of train control systems. This paper first builds the train control security state transition probability model under jamming attacks. A cross-layer defense scheme is then proposed from the aspect of the physical layer, the cyber layer and the management layer. In the physical layer, this paper designs a model prediction control algorithm to track dynamic target signals, in the hopes of eventually tracking the dynamic target quickly and smoothly. In the cyber layer, a multi-stage and zero-sum stochastic game model is built for the channel selection for the attack and the defense, whereby the channel selection randomized policy will be obtained. In the management layer, a dynamic train travel speed profile generation algorithm is proposed to mitigate the jamming attacks’ impact on train control systems. Extensive simulation results are shown that jamming attack impact on CBTC can be mitigated effectively with our proposed cross-layer defense scheme.

Degradation modeling is a critical and challenging problem as it serves as the basis for system prognostics and evolution mechanism analysis. In practice, multiple sensors are used to monitor the status of a system. Thus, multisensor data fusion techniques have been proposed to capture comprehensive information for prognostic modeling and analysis, which aims at developing a composite health index (HI) through the fusion of multiple sensor signals. In the literature, most existing methods use a linear data-fusion model for integration of multisensor data to construct the HI, which is insufficient to model nonlinear relations between sensing signals and HI in a complicated system. This article proposes a novel data fusion method based on deep learning for HI construction for prognostic analysis. A pair of adversarial networks is proposed to enable the training procedure of neural networks. To guarantee the stability of the algorithm, we propose a root mean square propagation (i.e., RMSprop)-based sampling algorithm to estimate model parameters. A set of simulation studies and a case study on a set of degradation signals of aircraft engines are conducted. The results demonstrate that the proposed method has a significant improvement on remaining useful life prediction compared to existing data fusion methods.

Quality of service (QoS) guarantee is critical in urban rail transit. In this paper, the train-centric communication-based train control (CBTC) systems through train-to-train (T2T) wireless communication is introduced based on the modification of LTE vehicle-to-everything (LTE-V2X). To be specific, a novel train-centric CBTC systems is established based on T2T wireless communication where distributed sensing-based semi-persistent scheduling (DS-SPS) is served as the resource allocation scheme in the T2T scenario. The quantized age of information (AoI) is used as an integrated system QoS indicator of the CBTC wireless communication systems in urban rail transit. Machine learning techniques especially Q-learning is further utilized to improve system AoI performance. Simulation results show that the proposed LTE-T2T based wireless communication systems in train-centric CBTC with Q-learning can achieve improved system AoI and peak AoI performance compared with fixed SPS policy. Furthermore, the system performance of the designed LTE-T2T based wireless communication systems in train-centric CBTC with Q-learning is shown to be better than traditional LTE-M and WLAN based wireless communication systems.

Metro is a safety-critical urban infrastructure, and large cities depend heavily on their metro systems to alleviate traffic jam, while disruptive incidents or accidents have become more frequent, causing threats to passenger safety and transport service. Previously, metro companies were busy in solving the disruptions caused by equipment failure or external disturbances, and the failure process research has not received enough attention, so it is hard to illustrate the route of failure propagation. In this paper, a failure mapping model is proposed to depict the fault-to-incident propagation path. Besides, the research also introduces a method of locating the fault faster by checking the troubleshooting card of each fault propagation point. Then, a case study of Beijing Metro Line 5 is used to demonstrate the effectiveness and feasibility of the model. The systematic explanations of metro operational incidents and disruptions might help understand metro incidents and give possible solutions to improve the resilience of the metro system. This paper would be constructive for dealing with operational disruption.

This paper addresses an error-driven nonlinear feedback design technique to improve the dynamic performance of fuzzy adaptive dynamic surface control (DSC) for a class of uncertain multiple-input-multiple-output nonlinear systems with prescribed tracking performance. The highlight of the error-driven nonlinear feedback technique is that the feedback gain self-regulates versus different levels of output and virtual tracking errors, this reflects the classical control design criterions commendably: relatively high feedback gains can be implemented to guarantee disturbances and uncertainties attenuation and so on to improve the control performance when small tracking errors are measured, and relatively small feedback gains can be implemented to circumvent the problems of actuator and states saturations when large tracking errors are measured. The complexity problem of the traditional backstepping design is circumvented owe to the peculiarity of DSC method. Caused by the compound error functions of nonlinear feedback dynamics, a nonquadratic Lyapunov function is used to deduce the conditions of closed-loop stability. Fuzzy logic systems and error transformation-based method are used in the online learning of completely unknown dynamics and the prescribed performance tracking, respectively. Comparative results are presented to demonstrate the effectiveness and preponderance of the proposed control scheme with comparison to existing ones.

This paper addresses the robust automatic train regulation problem in high-frequency metro lines with fuzzy passenger arrival rate. Due to the uncertainty of passenger demand, the passenger arrival rate is assumed to be represented by fuzzy variables. A nonlinear state-space model is formulated to describe the characteristic of metro train operation. To satisfy the real-time requirement of train regulation, a fuzzy constrained predictive control approach is designed to optimize a cost function at each decision epoch subject to safety constraints on the control input. Based on the Lyapunov stability theory and model predictive control method, sufficient conditions for the existence of corresponding state feedback control law are given in a set of linear matrix inequalities. Moreover, for reducing delays caused by the uncertain disturbance, the robust train regulation strategy is designed to guarantee that the practical train timetable tracks the nominal one with respect to certain disturbance attenuation level. The effectiveness of the proposed approach is validated by a number of experiments under real running circumstances of Beijing Metro Yizhuang Line of China.

In subway systems, kinetic energy can be converted into electrical one by using regenerative braking systems. If regenerative energy (RE) is fully used, the energy demands from power grid can be dramatically reduced. Since energy storage systems usually have a high cost, they are not considered in this work. Thus, RE has to be immediately utilized by accelerating trains; otherwise, it is wasted into heat via resistors. Timetable optimization methods are often used to coordinate accelerating and braking trains at a station, such that RE can be optimally used by the former. To improve RE utilization (REU) in a subway line, we propose a timetable optimization problem and establish its mathematical model. Many realistic constraints with the decision variables, i.e., headway time and dwell time, are considered. Then we design an improved artificial bee colony (IABC) algorithm to solve the problem. Several numerical experiments are conducted based on the actual data from a subway line in Beijing, China. The correctness of the mathematical model and effectiveness of IABC are shown by comparing it with commercial software CPLEX and a genetic algorithm, respectively. The impact of the decision variables on REU is analyzed, which helps to improve the timetable currently used in this subway line. We also test the robustness of the optimized timetable when certain disturbance takes place.

High reliability and low latency are crucial for urban rail transits. In this paper, we introduce communication strategies for communication-based train control (CBTC) systems using long-term evolution for metro (LTE-M) to improve the reliability and latency. To be specific, the FlashLinQ-based Train-to-Train (T2T) communication schemes are introduced considering both the transmission delay and the packet drop. The quantified resilience is also introduced as a system metric to evaluate the preservation and recovery performance of CBTC systems. First, a novel urban rail transit wireless communication model is established using FlashLinQ-based T2T communications. Then, we introduce a novel cognitive control scheme based on LTE-M with T2T communication to enhance the quality of service and the resilience of multi-train CBTC systems. In the introduced scheme, Q-learning is used to generate optimal control strategies considering both wireless communication parameters adaption and train control parameters. Extensive simulations are conducted and the results show that the resilience of CBTC systems can be enhanced using the introduced scheme. Furthermore, using the introduced scheme, not only the gaps in optimal velocity versus distance curve are smaller, but also the unplanned traction and breaking are reduced as well.

Big data is becoming a research focus in intelligent transportation systems (ITS), which can be seen in many projects around the world. Intelligent transportation systems will produce a large amount of data. The produced big data will have profound impacts on the design and application of intelligent transportation systems, which makes ITS safer, more efficient, and profitable. Studying big data analytics in ITS is a flourishing field. This paper first reviews the history and characteristics of big data and intelligent transportation systems. The framework of conducting big data analytics in ITS is discussed next, where the data source and collection methods, data analytics methods and platforms, and big data analytics application categories are summarized. Several case studies of big data analytics applications in intelligent transportation systems, including road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plan, rail transportation management and control, and assets maintenance are introduced. Finally, this paper discusses some open challenges of using big data analytics in ITS.

Maximizing regenerative energy utilization (REU) through timetable optimization has become a hot topic recently. Considering the constraints of operation time for a subway system and travel time for each train, we propose a new timetable optimization problem to maximize REU. We formulate its mathematical model, and then an artificial bee colony (ABC)-based algorithm is designed to solve it. Case studies are conducted based on the actual data obtained from a real subway line. Experiments results prove the correctness of the mathematical model and effectiveness of the proposed ABC-based algorithm. The results are also used to improve the currently used timetable by reallocating its headway and dwell time properly. Impacts of decision variables on REU are discussed, which is useful for the timetable designers. In addition, the ABC-based algorithm is compared with GA and outperforms the latter.

Nowadays, in urban rail transit systems, train wayside communication system uses Wireless Local Area Network (WLAN) as wireless technologies to achieve safety‐related information exchange between trains and wayside equipment. However, according to the high speed mobility of trains and the limitations of frequency band, WLAN is unable to meet the demands of future intracity and intercity rail transit. And although the Time Division‐Long Term Evolution (TD‐LTE) technology has high performance compared with WLAN, only 20 MHz bandwidth can be used at most. Moreover, in high‐speed scenario over 300 km/h, TD‐LTE can hardly meet the future requirement as well. The equipment based on Enhanced Ultra High Throughput (EUHT) technology can achieve a better performance in high‐speed scenario compared with WLAN and TD‐LTE. Furthermore, it allows using the frequency resource flexibly based on 5.8 GHz, such as 20 MHz, 40 MHz, and 80 MHz. In this paper, we set up an EUHT wireless communication system for urban rail transit in high‐speed scenario integrated all the traffics of it. An outdoor testing environment in Beijing‐Tianjin High‐speed Railway is set up to measure the performance of integrated EUHT wireless communication system based on urban rail transit. The communication delay, handoff latency, and throughput of this system are analyzed. Extensive testing results show that the Quality of Service (QoS) of the designed integrated EUHT wireless communication system satisfies the requirements of urban rail transit system in high‐speed scenario. Moreover, compared with testing results of TD‐LTE which we got before, the maximum handoff latency of safety‐critical traffics can be decreased from 225 ms to 150 ms. The performance of throughput‐critical traffics can achieve 2‐way 2 Mbps CCTV and 1‐way 8 Mbps PIS which are much better than 2‐way 1 Mbps CCTV and 1‐way 2 Mbps PIS in TD‐LTE.

Communication-based train control (CBTC) systems use wireless local area networks for information transmission between trains and wayside equipment. Since inevitable packet delay and drop are introduced in train-wayside communications, information uncertainties in trains' states will lead to unplanned traction/braking demands, as well as waste in electrical energy. Moreover, with the introduction of regenerative braking technology, power grids in CBTC systems are evolving to smart grids, and cost-aware power management should be employed to reduce the total financial cost of consumed electrical energy. In this paper, a cognitive control method for CBTC systems with smart grids is presented to enhance both train operation performance and cost efficiency. We formulate a cognitive control system model for CBTC systems. The information gap in cognitive control is calculated to analyze how the train-wayside communications affect the operation of trains. The Q-learning algorithm is used in the proposed cognitive control method, and a joint objective function composed of the information gap and the total financial cost is a.pplied to generate optimal policy. The medium-access control layer retry-limit adaption and traction strategy selection are adopted as cognitive actions. Extensive simulation results show that the cost efficiency and train operation performance of CBTC systems are substantially improved using our proposed cognitive control method.

A vast amount of text data is recorded in the forms of repair verbatim in railway maintenance sectors. Efficient text mining of such maintenance data plays an important role in detecting anomalies and improving fault diagnosis efficiency. However, unstructured verbatim, high-dimensional data, and imbalanced fault class distribution pose challenges for feature selections and fault diagnosis. We propose a bilevel feature extraction-based text mining that integrates features extracted at both syntax and semantic levels with the aim to improve the fault classification performance. We first perform an improved X2statistics-based feature selection at the syntax level to overcome the learning difficulty caused by an imbalanced data set. Then, we perform a prior latent Dirichlet allocation-based feature selection at the semantic level to reduce the data set into a low-dimensional topic space. Finally, we fuse fault features derived from both syntax and semantic levels via serial fusion. The proposed method uses fault features at different levels and enhances the precision of fault diagnosis for all fault classes, particularly minority ones. Its performance has been validated by using a railway maintenance data set collected from 2008 to 2014 by a railway corporation. It outperforms traditional approaches.

Most stations and tracks in metro networks are irreplaceable due to daily operations. If any of them were disrupted, it would impact not only the individual metro line but also the whole metro network. Therefore, metro managers need to have a good understanding of alternative routes between each pair of stations in the metro network. In the event of incidents, metro managers can make use of this information to reroute passengers to minimize the impact of disruptions. This paper aims to develop a route diversity index to address two questions: “how many reasonable routes are there for passengers between any two stations in normal operations or in the event of a disruption?” and “which stations are most vulnerable (i.e., the largest impact to the overall metro network when they are disrupted)?” To implement this measure in practice, definitions of routes and route diversity and a solution algorithm based on characteristics of metro networks are described to calculate the route diversity index. To show proof of the concept, a simple network example and a real-world network based on the Beijing Metro network in China are presented to demonstrate the feasibility of the route diversity index and its application to a real-world metro network.

In existing urban rail transit systems, the train-ground communication system for different subsystems is deployed independently. Investing and constructing the communication infrastructures repeatedly not only wastes substantial social resources, but it also is difficult to maintain all these infrastructures. In this paper, we propose an integrated train-ground communication system based on wireless network virtualization for urban rail transit systems. In order to improve the communication-based train control (CBTC) subsystem performance during handoff, we propose a novel handoff scheme to support handoff between virtual networks. The application-layer quality-of-service (QoS) parameters of the CBTC, passenger information system, and closed circuit television subsystems are used as the performance measures in the handoff design. We then formulate the QoS optimization problem in the proposed integrated train-ground communication system as an approximate dynamic programming (ADP) problem. The extensive simulation results show that the proposed integrated train-ground communication system QoS can be improved substantially with our ADP-based optimization model.

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.

This paper studies the energy-efficient operation problem of two trains operating along the same line consecutively in urban trail transit (URT). In URT, a moving block signaling (MBS) system is utilized in which the following train's tracking target point is moving forward continuously with the leading train's running. In this case, the following train may be influenced by the leading train's exceptional situations, leading to energy wasting and arrival time delay. In order to reduce energy consumption and arrival delay for the following train, a multiple-optimization-model-based energy-efficient operation method is proposed and verified in this paper. The novelty of this paper lies on the establishment of a novel multiple-model-based switching optimization framework to reduce energy consumption while guaranteeing the punctuality during train tracking operation. More specifically, the operation status of the leading train is combined into the operation optimization for the following train, based on which eight energy-efficient tracking scenarios are discovered and classified into four optimization problems. Then, a general form of running phase sequence is designed to describe the energy-efficient train tracking operation strategy, and a new energy consumption function is proposed. Based on these, the four optimization problems are built into small-scale nonlinear programming models, which are computationally inexpensive and easy for online implementation. During the tracking process, the following train can obtain the energy-efficient strategies through switching among these four optimization models. In this framework, the following train is able to arrive at the next station by the fastest arrival time with lower energy consumption even when the arrival time is delayed by the leading train. Simulation results show the feasibility and effectiveness of the new method.

This paper formulates a two-objective model to optimize the timetables of urban rail transit systems based on energy-saving strategies and service quality levels. With time-dependent passenger demands, the calculation process of passenger travel time simulates boarding and alighting activities with some constraints to guarantee traffic capacity and meet passenger requirements, particularly in the oversaturated conditions. Traction and auxiliary energy consumption are considered in the operational energy consumption calculation. The regenerative energy, which is generated from braking trains and simultaneously used by traction trains, is also taken into account in the calculation with transmission loss. Through adjusting the headway, this model makes a tradeoff between passenger travel time and operational energy consumption with guaranteed traffic capability. Furthermore, a genetic algorithm with the binary encoding method is designed to obtain high-quality timetables. Based on the operational data of the Beijing Yizhuang subway line, we implement some numerical experiments to demonstrate the effectiveness of the proposed approaches.

Designing a train control system over wireless train-wayside communications is a challenging task due to the packet delay and loss caused by unreliable wireless communications. In this paper, we study the performance optimization issues in communication-based train control (CBTC) systems with both packet delay and loss introduced by random transmission errors and frequent handoffs in train-wayside communications. A networked control system (NCS) model is formulated for multitrain CBTC systems with packet delay and loss. Then, we propose a novel train control scheme to enhance the quality of service (QoS) of CBTC systems. In the proposed scheme, medium-access control layer retry limit adaption is used to minimize the energy consumption, and guidance trajectory update is used to mitigate the trip time tracking errors. Extensive simulation results show that the QoS of CBTC systems can be substantially improved in the proposed scheme. The energy consumption and trip time error can be reduced in both static and time-varying wireless environments.

Due to rising energy prices and environmental concerns, the energy efficiency of urban rail transit has attracted much attention from both researchers and practitioners in recent years. Timetable optimization and energy-efficient driving, as two mainly used train operation methods in relation to the tractive energy saving, make major contributions in reducing the energy consumption that has been studied for a long time. Generally speaking, timetable optimization synchronizes the accelerating and braking actions of trains to maximize the utilization of regenerative energy, and energy-efficient driving optimizes the speed profile at each section to minimize the tractive energy consumption. In this paper, we present a fully comprehensive survey on energy-efficient train operation for urban rail transit. First, a general energy consumption distribution of urban rail trains is described. Second, the current literature on timetable optimization and energy-efficient driving is reviewed. Finally, according to the review work, it is concluded that the integrated optimization method jointly optimizing the timetable and speed profile has become a new tendency and ought to be paid more attention in future research.

The majority of existing studies in subway train operations focus on timetable optimization and vehicle tracking methods, which may be infeasible with disturbances in actual operations. To deal with uncertain passenger demands and realize real-time train operations (RTOs) satisfying multiobjectives, including overspeed protection, punctuality, riding comfort, and energy consumption, this paper proposes two RTO algorithms via expert knowledge and an online learning approach. The first RTO algorithm is developed by a knowledge-based system to ensure the multiple objectives with a constant timetable. Then, by considering uncertain passenger demand at each station and random running time errors, we convert the train operation problem into a Markov decision process with nondeterministic state transition probabilities in which the aim is to minimize the reward for both the total time delay and energy consumption in a subway line. After designing policy, reward, and transition probability, we develop an integrated train operation (ITO) algorithm based on Q-learning to realize RTOs with online adjusting the timetable. Finally, we present some numerical examples to test the proposed algorithms with real detected data in the Yizhuang Line of Beijing Subway. The results indicate that, taking the multiple objectives into account, the RTO algorithm outperforms both manual driving and automatic train operations. In addition, the ITO algorithm is capable of dealing with uncertain disturbances, keeping the total time delay within 2 s and reducing the energy consumption.

In this paper, with recent advances in cooperative and cognitive wireless networks, we propose a CBTC system to enable train-train direct communications. In addition, the proposed system is optimized with the cognitive control method. Unlike the exiting works on cooperative wireless networks, in this paper, train control performance in CBTC systems is explicitly used as the performance measure in the design. Reinforcement learning is applied to obtain the optimal handover decision and adaption policy of communication parameters. Simulation result shows that the performance of train control can be improved significantly in our proposed CBTC system.

Increasing attention is being paid to energy efficiency in subway systems to reduce operational cost and carbon emissions. Optimization of the driving strategy and efficient utilization of regenerative energy are two effective methods to reduce the energy consumption for electric subway systems. Based on a common scenario that an accelerating train can reuse the regenerative energy from a braking train on the opposite track, this paper proposes a cooperative train control model to minimize the practical energy consumption, i.e., the difference between traction energy and the reused regenerative energy. First, we design a numerical algorithm to calculate the optimal driving strategy with the given trip time, in which the variable traction force, braking force, speed limits, and gradients are considered. Then, a cooperative train control model is formulated to adjust the departure time of the accelerating train for reducing the practical energy consumption during the trip by efficiently using the regenerative energy of the braking train. Furthermore, a bisection method is presented to solve the optimal departure time for an accelerating train. Finally, the optimal driving strategy is obtained for the accelerating train with the optimal departure time. Case studies based on the Yizhuang Line, Beijing Subway, China, are presented to illustrate the effectiveness of the proposed approach on energy saving.

The real-time train scheduling problem for urban rail transit systems is considered with the aim of minimizing the total travel time of passengers and the energy consumption of the operation of trains. Based on the passenger demand in the urban rail transit system, the optimal departure times, running times, and dwell times are obtained by solving the scheduling problem. A new iterative convex programming (ICP) approach is proposed to solve the train scheduling problem. The performance of the ICP approach is compared with other alternative approaches, i.e., nonlinear programming approaches, a mixed-integer nonlinear programming (MINLP) approach, and a mixed-integer linear programming (MILP) approach. In addition, this paper formulates the real-time train scheduling problem with stop-skipping and shows how to solve it using an MINLP approach and an MILP approach. The ICP approach is shown, via a case study, to provide a better tradeoff between performance and computational complexity for the real-time train scheduling problem. Furthermore, for the train scheduling problem with stop-skipping, the MINLP approach turns out to have a good tradeoff between the control performance and the computational efficiency.

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) Systems use bidirectional train-wayside wireless communications to ensure the safe operation of rail vehicles. The random delay and packet drop introduced by wireless communication will lead to inaccurate train status and unnecessary control commands, which will affect the performance of CBTC systems. In this paper, we study the impact of communication delay and packet drop on CBTC systems. We formulate a networked control system (NCS)-based model for CBTC systems with random delay and packet drop, and propose a control scheme based on packet retry limit adaption and guidance trajectory update to improve the train control performances. Simulation result shows that our proposed scheme can significantly improve the energy efficiency and punctuality in CBTC systems compared to existing schemes.

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) 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.

Energy-efficient train operation represents an important issue for daily operational urban rail transit. Most energy-efficient train operation strategies are normally planned according to a timetable, which is designed by offline traffic information. In this paper, a new energy-efficient train operation model based on real-time traffic information is proposed from the geometric and topographic points of view through a nonlinear programming method, leading to an energy-efficient driving strategy with real-time interstation running time monitored by the automatic train supervision system. The novelty of this work lies not only in the establishment of a new model for energy-efficient train operation but also in the utilization of combining analytical and numerical methods for deriving energy-efficient train operation strategies. More specifically, the energy-efficient operation model is built based on trajectory analysis when the energy-efficient optimal controls are applied, from which an energy-efficient reference trajectory is obtained under the running time and distance constraints, in which the nonlinear programming method is utilized. In contrast to most existing methods, the proposed model turns out to be a small-scale problem, and the difficulties of solving partial differential equations or the process of predetermining and reiteratively calculating some key factors as traditionally involved are avoided. Thus, it is more feasible to implement the strategy and easier to make real-time adjustment if needed. The comparative analysis and the simulation verification with the actual operating data confirm the effectiveness of the proposed method. With the proposed method, some delayed trains are able to maintain punctuality at the next station and sometimes even reducing energy consumption.

Energy efficiency is paid more and more attention in railway systems for reducing the cost of operation companies and emissions to the environment. In subway systems, the optimizations on timetable and driving strategy are two important and closely dependent parts of energy-efficient operations. The former regulates the fleet size and the trip time at interstations, and the latter determines the control sequences of traction and braking force during the trip. Most conventional research optimized the timetable and the driving strategy separately such that global optimality cannot be achieved. In this paper, we analyze the hierarchy of energy-efficient train operation and then propose an integrated algorithm to generate the globally optimal operation schedule, which can get better energy-saving performance. Within the criteria of meeting the passenger demand, the integrated energy-efficient algorithm can simultaneously obtain the optimal timetable and driving strategy for trains, which realizes the combination of the high-level transportation management and the low-level train operation control. The simulation results based on the Beijing Yizhuang Subway Line illustrate that the integrated algorithm can achieve a 24.0% energy reduction for one day, on average. In addition, the computation time is within 2 s, which is short enough to be applied for real-time control system.

The train scheduling problem for urban rail transit systems is considered with the aim of minimizing the total travel time of passengers and the energy consumption of the trains. We adopt a model-based approach, where the model includes the operation of trains at the terminus and at the stations. In order to adapt the train schedule to the origin-destination-dependent passenger demand in the urban rail transit system, a stop-skipping strategy is adopted to reduce the passenger travel time and the energy consumption. An efficient bilevel optimization approach is proposed to solve this train scheduling problem, which actually is a mixed-integer nonlinear programming problem. The performance of the new efficient bilevel approach is compared with the existing bilevel approach. In addition, we also compare the stop-skipping strategy with the all-stop strategy. The comparison is performed through a case study inspired by real data from the Beijing Yizhuang line. The simulation results show that the efficient bilevel approach and the existing bilevel approach have a similar performance but the computation time of the efficient bilevel approach is around one magnitude smaller than that of the bilevel approach.

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.

The train timetable optimization problem in subway systems is to determine arrival and departure times for trains at stations so that the resources can be effectively utilized and the trains can be efficiently operated. Because the energy saving and the service quality are paid more attention, this paper proposes a timetable optimization model to increase the utilization of regenerative energy and, simultaneously, to shorten the passenger waiting time. First, we formulate a two-objective integer programming model with headway time and dwell time control. Second, we design a genetic algorithm with binary encoding to find the optimal solution. Finally, we conduct numerical examples based on the operation data from the Beijing Yizhuang subway line of China. The results illustrate that the proposed model can save energy by 8.86% and reduce passenger waiting time by 3.22% in comparison with the current timetable.

A communication-based train control (CBTC) system is an automated train control system that uses bidirectional train-ground communications to ensure the safe operation of rail vehicles. CBTC systems have stringent requirements for communication availability and latency. Due to unreliable wireless communications and frequent handoffs, existing CBTC systems can severely affect train control performance, train operation efficiency, and the utility of railways. In this paper, we use recent advances in coordinated multipoint transmission and reception (CoMP) to enhance the train control performance of CBTC systems. With CoMP, a train can communicate with a cluster of base stations (BSs) simultaneously, which is different from the current CBTC systems, where a train can only communicate with a single BS at any given time. In addition, unlike the existing works on CoMP, in this paper, the linear quadratic cost for the train control performance in CBTC systems is considered the performance measure. We jointly consider the BS cluster selection and handoff decision issues in CBTC systems. Moreover, in order to mitigate the impacts of communication latency on train control performance, 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 train control performance can be substantially improved in our proposed CBTC system with CoMP.

Communication-based train control (CBTC) systems use wireless local area networks (WLANs) to transmit train status and control commands. Random transmission delays and packet drops are inevitable in train-ground communication, which could result in unnecessary traction, brake or even emergency brake of trains, loss of line capacity and passenger satisfaction. In this paper, we study the random packet drops in CBTC systems, analyze their impact on the performances of CBTC systems, and propose a control scheme to improve CBTC performances. We model the system to control a group of trains as a networked control system with packet drops in transmissions. Extensive simulation results are presented. We show that our proposed scheme can provide less energy consumption, better riding comfortability and total cost reduction.

Random transmission delays and packet drops are inevitable in communication-based train control (CBTC) systems, which could result in unnecessary traction, brake or even emergency brake of trains, losses of line capacity and passenger satisfaction. In this paper, we study the transmission delays in CBTC systems, analyze their impacts on the performances of CBTC systems, and propose a novel scheme to improve the performances of trains' control system in CBTC by mitigating the impacts. Unlike the existing works that only consider a single train, we consider a group of trains in CBTC systems to improve the CBTC performances. Extensive simulation results are presented. We show that the current adopted control scheme experiences obvious performance losses under transmission delays. By contrast, our proposed scheme is tolerant of transmission delays, and can significantly improve the CBTC performances.

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 vehicular ad hoc networks (VANETs), security always comes with a price in terms of QoS performance degradation. In this paper, we take an integrated approach of optimizing both security and QoS parameters, and study the tradeoffs between them in VANETs. Specifically, we use recent advances in cooperative communication to enhance the QoS performance of VANETs. In addition, we present a prevention-based security technique that provides both hop-by-hop and end-to-end authentication and integrity protection. We derive the closed-form effective secure throughput considering both security and QoS provisioning in VANETs with cooperative communications. The system is formulated as a partially observable Markov decision process (POMDP). Simulation results are presented to show that our proposed scheme can substantially improve the effective secure throughput of VANETs with cooperative communications.

Communication-Based Train Control (CBTC) system is an automated train control system using bidirectional train-ground communications. Most existing CBTC train-ground communication systems work in the infrastructure mode without train-train communications. 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 cooperative relaying to enable train-train communications, and consequently enhance the train control performance of CBTC systems. Linear quadratic cost for the train control performance in CBTC systems is considered as the performance measure. We jointly consider cooperative relaying and handoff decision issues in CBTC systems. Moreover, in order to mitigate the impacts of handoff latency on the train control performance, we propose an optimal guidance trajectory calculation scheme that takes full consideration of the tracking error caused by handoff latency. Simulation result shows that the train control performance can be improved substantially in our proposed CBTC system.

The Onboard system is a typical safety-critical system, in which any fault can lead to huge human injury or wealth losing. Function testing method which is mainly focus on the conformance relation between the specification and the SUT has been widely used in testing the Onboard system in the past few years. However, most of the test cases are manually generated which can't be reused and leads to repeat works when the specification is changed. To improve the testing efficiency and quality, Model-based testing method is introduced. We use a tool chain to generate test case automatically based on Timed Automata theory and apply in function testing of the Onboard system. EBD-TR timed automata network model is established using tool Uppaal. And based on the EBD-TR model, two kinds of coverage criteria (all-location coverage, and all-edge coverage) are used in tool of CoVer to generate test case automatically. Different test suits of the Onboard system are acquired and a complete model transition function test suit is derived which is proven very useful for testing the Onboard system.

For urban metro systems with platform screen doors, train automatic stop control (TASC) has recently attracted significant attention from both industry and academia. Existing solutions to TASC are challenged by uncertain stopping errors and the fast decrease in service life of braking systems. In this paper, we try to solve the TASC problem using a new machine learning technique and propose a novel online learning control strategy with the help of the precise location data of balises installed at stations. By modeling and analysis, we find that the learning-based TASC is a challenging problem, having characteristics of small sample sizes and online learning. We then propose three algorithms for TASC by referring to heuristics, gradient descent, and reinforcement learning (RL), which are called heuristic online learning algorithm (HOA), gradient-descent-based online learning algorithm (GOA), and RL-based online learning algorithm (RLA), respectively. We also perform an extensive comparison study on a real-world data set collected in the Beijing subway. Our experimental results show that our approaches control all stopping errors in the range of ±0.30 m under various disturbances. In addition, our approaches can greatly increase the service life of braking systems by only changing the deceleration rate a few times, which is similar to experienced drivers. Among the three algorithms, RLA achieves the best results, and GOA is a little better than HOA. As online learning algorithms can dynamically reduce stopping errors by using the precise location data from balises, it is a promising technique in solving real-world problems.

Given rising energy prices and environmental concerns, train energy-efficient operation techniques are paid more attention as one of the effective methods to reduce operation costs and energy consumption. Generally speaking, the energy-efficient operation technique includes two levels, which optimize the timetable and the speed profiles among successive stations, respectively. To achieve better performance, this paper proposes to optimize the integrated timetable, which includes both the timetable and the speed profiles. First, we provide an analytical formulation to calculate the optimal speed profile with fixed trip time for each section. Second, we design a numerical algorithm to distribute the total trip time among different sections and prove the optimality of the distribution algorithm. Furthermore, we extend the algorithm to generate the integrated timetable. Finally, we present some numerical examples based on the operation data from the Beijing Yizhuang subway line. The simulation results show that energy reduction for the entire route is 14.5%. The computation time for finding the optimal solution is 0.15 s, which implies that the algorithm is fast enough to be used in the automatic train operation (ATO) system for real-time control.

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.

Data communication technology is one of the key subsystem in communication-based train control (CBTC), which is an automated train control system for railways that ensures safe operation of rail vehicles using data communications. In CBTC systems, less service availability could cause train derailment, collision or even catastrophic loss of lives or assets. Therefore, the availability of data communication should be carefully considered in designing CBTC systems. In this paper, we propose two WLAN-based data communication systems with redundancy in CBTC systems. The availability is analyzed using continuous time Markov chain (CTMC) model. We also model the WLAN-based data communication system behavior with Deterministic and Stochastic Petri Net (DSPN). The DSPN solution is used to show the soundness of our proposed CTMC model. Numerical examples illustrate that the proposed systems with redundancy can significantly improve the availability of communication availability in CBTC systems.

There is growing interest in plug-in electric vehicles (EVs). Charging EVs from smart microgrids fueled by renewable energy resources is becoming a popular green approach. Although some works have been done about renewable energy sources and EVs in smart microgrids, the stochastic characteristics and the dynamic interplay between these two important green solutions should be carefully considered. In this paper, we study the charging policies in smart microgrids with EVs and renewable energy sources. Based on the renewable energy sources states, battery states, and the number of charging EVs, an optimal charging policy is obtained to maximize the energy utilization with service availability constraints. We formulate the optimal charging problem as a stochastic decision process. Simulation results are presented to show that the proposed scheme can improve the service availability for EVs in microgrids fueled by renewable energy sources.

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.

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.

Communication-based train control (CBTC) is an automated control system for railways using data communications. CBTC systems have stringent communication latency requirements. For rail transit systems, wireless local area network (WLAN)-based CBTC is a popular approach due to the wide availability of commercial-off-the-shelf WLAN equipment. However, WLANs were not originally designed for high-speed environments with frequent handoffs, which may result in communication interrupt and long latency. In this paper, we propose a handoff scheme in CBTC systems based on WLANs with multiple-input-multiple-output (MIMO) technologies to improve the handoff latency performance. In particular, we consider channel estimation errors and the tradeoff between MIMO multiplexing gain and diversity gain in making handoff decisions. The handoff problem is formulated as a partially observable Markov decision process (POMDP), and the optimal handoff policy can be derived to minimize the handoff latency. Simulations results based on real field channel measurements are presented to show the effectiveness of the proposed scheme.

Communication-Based Train Control (CBTC) is an automated control system for railways using data communications. CBTC systems have stringent communication latency requirements. However, in rail transit systems, frequent train handoffs can cause severe communication latency. In this paper, we propose a handoff scheme in CBTC systems based on WLANs with multiple-input and multiple-output (MIMO) technologies to improve the handoff latency performance. Particularly, we consider channel estimation errors and the tradeoff between MIMO multiplexing gain and diversity gain in making handoff decisions. The handoff problem is formulated as a partially observable Markov decision process (POMDP), and the optimal handoff policy can be derived to minimize the handoff latency. Simulations results based on real field channel measurements are presented to show the effectiveness of the proposed scheme.

When verifying the safety of ETCS, testing and formal methods have limitations to some degree. Runtime verification is effective to detect deviation between the current and the expected system behaviors. To improve the accuracy of runtime monitoring, 4-valued LTL (Linear Time Logic) semantics and formula rewriting based algorithm are proposed. Furthermore, approximation technique is presented for 4-valued LTL formulae to make the verification procedure high efficient. Finally, the method is applied to the European Train Control System (ETCS) by monitoring several scenario traces. The experimental results show that the 4-valued LTL semantics are able to generate the most accurate verification outcomes. It can also be found that the approximation technique improves the verification efficiency apparently in some cases.

The RBC (Radio Block Center) handover is an important part of European Train Control System level 2 which is a typical safety-critical hybrid system. In this paper, we build a formal model of RBC handover procedure using Differential Dynamic Logic, which is a first-order dynamic logic for specifying and verifying hybrid systems, and identify some constraints that are necessary for ensuring safety of train control, including collision avoidance as well as derailment avoidance. Moreover, we formally verify the safety-related properties of our model with deductive verification tool KeYmaera. The experimental results show the validity and feasibility of the method. Meanwhile, the safety constraints and safety-related properties verified in the paper can be helpful to the practical application of train control.

The importance of the specification of train control system is increasingly recognized and gained more attention in signalling field in China as the specification is the basis to ensure that the signalling system supplied by manufacturer meet the requirements of the railway administration, for example, the requirement for the interoperability of the system. The specifications which are described in natural language are probably deficient and it is inadequate that the specifications are checked only based on the experience of experts. In this paper, a modelling method was applied on the description and the verification of the System Requirement Specification, SRS, of train control system. The Specification and Description Language, SDL, was used to describe the functional behavior of the onboard equipment which is defined in the SRS. First, we defined the principles of modelling which are summarized for the purpose of the interoperability validation. Then, we applied a top-down hierarchical approach to model the functional behavior. The model started from the system level to describe the interface and refined in the block level to show the interaction of system scenarios and detailed the state transition and the working processes of the system in the process level. Debugged the SDL model, we validated the model in Telelogic Tau tool to find the problems of the SRS. The results showed that the ambiguous terms and the incompatible descriptions of the SRS can be found. It can be helpful for the modification of the SRS and the quality of train control system further.

This paper introduces a framework of parallel control and management for high-speed railway systems (HRSs). First, based on multiagent modeling, an artificial HRS that is consistent with realistic operations of the actual HRS is constructed. Then, different kinds of computational experiments are performed on the artificial HRS, followed by analysis and synthesis with a case. Finally, through an interactive and parallel operation between the actual and artificial HRSs, a set of practical control and management strategies can be achieved for the actual HRS. With the primary objective of ensuring reliability and safety of HRSs, this study could enhance the quality of services and the integrated transportability with other existing modes of transportation systems to provide appropriate recommendations and strategies for forming an overall effective comprehensive transportation system.

The problem of the position and velocity tracking control of high-speed trains becomes interesting yet challenging when simultaneously considering inevitable factors such as the resistive friction and aerodynamic drag forces, the interactive impacts among the vehicles, and the nonlinear traction/braking notches inherent in train systems. In this paper, a multiple point mass with a single-coordinate dynamic model that reflects resistive and transient impacts is derived, and based on this, computationally inexpensive robust adaptive control designs with optimal task distribution for speed and position tracking are proposed under traction/braking nonlinearities and saturation limitations. It is shown that the proposed method is not only robust to external disturbances, aerodynamic resistance, mechanical resistance, and transient impacts but adaptive to unknown system parameters as well. The effectiveness of the proposed approach is also confirmed through numerical simulations.

Runtime verification is a promising method that tries to bridge the gap between formal methods and traditional testing. In this paper, we present an improved runtime verification method via multi-valued formula rewriting. A 3-valued executable semantics for finite trace LTL is formally defined, and an algorithm based on this new semantics is proposed and implemented in Maude, which is a high performance rewriting system. To improve the efficiency of our algorithm, we introduce a novel approximation technique, which reduces rewriting steps by sacrificing some abilities of detecting the satisfactions of LTL properties. Moreover, this technique provides a quick procedure for distinguishing non-monitor able properties from those can be monitored. Finally, experiments are conducted to show the strength and weakness of the presented method.

Communication based train control (CBTC) system was based on mobile communication and overcome fixed blocks in order to increase track utilization and train safety. As an intelligent autonomous decentralized systems (IADS), data communication system (DCS) between trains and zone center (ZC) are crucial factors for the safe and efficient operation of CBTC system. The real-time behavior under various transmission conditions needs to be modeled and evaluated. The paper presents a DCS model and evaluates the influence of communication collision and message length on the transfer delay. Performance evaluation of the coloured Petri net model shows that the communication collision caused by two trains in one cell does not result in the unacceptable train operation situations. But the message length may have much influence on the transfer delay