Advanced Persistent Threats (APTs) represent sophisticated cyberattacks that evade detection through stealthy, multistage operations, posing severe risks to critical infrastructure and organizational security. Due to their ability to effectively capture contextual information of attack behaviors, provenance graphs have emerged as a promising approach for APT detection. However, traditional binary edges in provenance graphs fail to represent the collaborative nature of APT attacks, where multiple entities coordinate in single operations, and local graph structures cannot capture the long-range dependencies across attack stages. To address these challenges, we propose HyperDetector, a novel hypergraph-based method for APT detection. First, we introduce hypergraph representation for provenance data, where hyperedges naturally connect multiple entities involved in system events, preserving the higher-order relational structures that characterize APT behaviors. Second, we employ block self-attention mechanisms that enable global reasoning across distant hypergraph regions, effectively linking dispersed attack indicators throughout the system. Through the synergistic integration of these approaches, HyperDetector achieves comprehensive understanding of both localized multi-entity collaborative behaviors and system-wide attack propagation patterns. Extensive evaluations across multiple prominent datasets demonstrate that HyperDetector outperforms state-of-the-art methods, showcasing its effectiveness for robust and holistic APT detection. Additionally, we make our code and datasets publicly available to facilitate reproducibility and foster further research in this critical area.

High-frequency dynamic targets introduce substantial appearance variations in LiDAR scans of the same location over time, posing a major challenge for place recognition. To tackle this, we propose DyLPR, a cascaded PR framework that integrates a LiDAR depth inpainting network and a place recognition network (PTN-Net), leveraging the complementary strengths of convolutional neural networks (CNNs) and transformer architectures. Specifically, a supervised encoder–decoder combining CNNs and transformers is employed to effectively handle dynamic masks across multiple scales. A semantic auxiliary branch and a hybrid loss function are further introduced to enhance both structural consistency and texture fidelity, resulting in more accurate and realistic depth inpainting. PTN-Net employs a pyramidal convolutional backbone with parallel Transformer-NetVLAD modules to capture long-range multiscale dependencies and adaptively aggregate salient features, while context gating refines integration to improve descriptor compactness and discriminability. Extensive evaluations on the LiDAR depth inpainting dataset, constructed from benchmark SemanticKITTI and real-vehicle data, demonstrate that our method achieves competitive inpainting performance and outperforms existing approaches in place recognition under dynamic conditions. For instance, DyLPR improves Recall@1 by an absolute 5.4% over the best baseline.

The periodic operation pattern of high-speed train (HST) grants the immense potential for iterative learning control (ILC) approach regulating the displacement and velocity, but the non-repetitive uncertainties caused by carrying loads, random disturbances, etc., may weaken the capability of controller. Further, the typical operating situations of rail transit, e.g., station entrance/exit, slowdown sections, can compress the safety margin of HST, increasing the difficulty of precise tracking. In this paper, an adaptive ILC scheme is proposed for HST subject to the safety constraints, where the unknown iteration-varying parameters and the modeling inaccuracies are handled deliberately. Our technical route could be divided into two phases. The transformation mechanism of tracking errors, that can convert the control problem of constrained systems into an unconstrained form, is first established to guarantee that HST is always located within the safety zone. On this basis, the iterative learning controller is devised through integrating the hyperbolic tangent function and iteration-related sequence, where the neural network is leveraged to approximate the unmodeled lumps. The main innovative features lie in that, the iteration-dependent terms of control system are evolved into the parametric compensation components of controller and the iterative convergence parts, while the nested structure of control law is built to accommodate the iteration-variation of loads. As a result, the proposed approach can theoretically achieve the zero-error tracking of HST in the presence of the non-repetitive uncertainties and safety constraints, which indicates the better performance and practicability than the existing ones.

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

Reinforcement Learning (RL) has demonstrated excellent decision-making potential in platoon coordination problems. However, due to the variability of coordination goals, the complexity of the decision problem, and the time-consumption of trial-and-error in manual design, finding a well performance reward function to guide RL training to solve complex platoon coordination problems remains challenging. In this paper, we formally define the Platoon Coordination Reward Design Problem (PCRDP), extending the RL-based cooperative platoon coordination problem to incorporate automated reward function generation. To address PCRDP, we propose a Large Language Model (LLM)-based Platoon coordination Reward Design (PCRD) framework, which systematically automates reward function discovery through LLM-driven initialization and iterative optimization. In this method, LLM first initializes reward functions based on environment code and task requirements with an Analysis and Initial Reward (AIR) module, and then iteratively optimizes them based on training feedback with an evolutionary module. The AIR module guides LLM to deepen their understanding of code and tasks through a chain of thought, effectively mitigating hallucination risks in code generation. The evolutionary module fine-tunes and reconstructs the reward function, achieving a balance between exploration diversity and convergence stability for training. To validate our approach, we establish six challenging coordination scenarios with varying complexity levels within the Yangtze River Delta transportation network simulation. Comparative experimental results demonstrate that RL agents utilizing PCRD-generated reward functions consistently outperform human-engineered reward functions, achieving an average of 10% higher performance metrics in all scenarios.

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

This paper proposes a real-time trajectory optimization framework for railway trains, which integrates denoising diffusion models with particle swarm optimization (PSO), aiming to address the need for dynamic adjustments under emergency speed restriction scenarios. In contrast to existing reinforcement learning approaches for trajectory optimization, the proposed method utilizes the diffusion model to learn the distributional characteristics of feasible trajectories under complex conditions. This enables the generation of high-quality initial solutions that can adapt to previously unseen scenarios in real time. Subsequently, complex constraints and external uncertainties are handled through the PSO-based optimization process. At the same time, multiple objectives such as travel time, energy consumption, and passenger comfort are jointly considered and effectively balanced. The effectiveness of the proposed framework, particularly in unfamiliar scenarios, is validated through a series of comparative experiments with the Deep Deterministic Policy Gradient (DDPG) algorithm.

Visual place recognition (VPR) improves the localization accuracy of agents in complex environments by extracting effective environmental representations for place matching, and it does not rely on additional high-precision, high-cost sensors or digital maps. However, current VPR research still faces limitations when simultaneously addressing the challenges of viewpoint changes and dynamic target interference, where it is not easy to obtain stable and reliable viewpoint-invariant environmental representations. To address these challenges, inspired by the human ability to recognize scenes, this paper customizes a place recognition network called VDI-Net based on multi-view images, immune to viewpoint changes and dynamic interference. Specifically, a dynamic target filtering module (DTF) is proposed to effectively filter out dynamic targets in complex environments. To tackle the challenge of viewpoint changes, a vision-surround Mamba module (VSM) is introduced to enhance the rotational invariance of features across different viewpoints. In the comparative validation of the nuScenes dataset and our real-vehicle collection dataset, the experimental results show that our proposed method achieves satisfactory performance in addressing the challenges of viewpoint changes and dynamic target interference. The proposed method outperforms current representative VPR baselines and surpasses some classical LiDAR-based and multimodal place recognition baselines.

Traditional cognitive travel modeling typically employs a unified cognitive model to simulate representative travel behaviors, which may usually result in a weak characterization of user heterogeneity in paths, modes, and other factors. Large language model (LLM), by contrast, has significantly enhanced the anthropomorphic and personalized features of intelligent systems. To integrate their advantages, this article proposes LLM-driven cognitive modeling to generate more diverse and personalized travel demands. The new method sufficiently exploits LLM such as the llama as a basis and provides personalized travel plans so that more heterogenous travel demands could be generated. Additionally, introducing LLM into cognitive modeling can significantly reduce the time of model development, thus accelerating the research or engineering deployment. By calibrating and testing with one month’s data from public transportation (buses and subways) in Beijing, our method, compared to traditional cognitive models, not only achieves better accuracy in reproducing typical travel patterns, but also generates more diverse ones, providing a more comprehensive input for computational experiments on traffic management and control strategies.

In this article, a correlation-aided neural network (CANN) framework is proposed for distributed process monitoring of large-scale industrial automation systems. First, to model the nonlinear data relationship of multiple subsystems, a feature space is learned for each subsystem by means of neural network-based nonlinear projection. Second, different from conventional distributed monitoring methods, a distributed learning method and a novel objective function are proposed, where the influence of uncertainties is decreased by considering the correlation of variables among multiple subsystems. Third, a process monitoring method is proposed based on the CANN framework. The impact of the fault is analyzed, and the optimal monitoring performance is achieved by considering the correlation of all subsystems. To further reduce communication overhead among multiple subsystems, a tradeoff between monitoring performance and communication overhead is made based on gradients of the CANN model. Theoretical analysis demonstrates the superiority of the proposed method. The case studies on a multizone heating, ventilation, and air-conditioning system and the Tennessee Eastman benchmark process are given to evaluate and compare the effectiveness of the proposed method.

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

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

The safe, punctual, and reliable operation of high-speed railway (HSR) networks is crucial for ensuring system efficiency and enhancing passenger experience. However, due to the complexity of HSR systems and long operation routes, failures in system components can lead to unexpected incidents, causing deviations from the scheduled operations and, subsequently, delays. These delays, particularly large delays, significantly impact the overall performance and passenger experience of the network. Thus, accurate prediction of train delays, especially in large-delay scenarios, is essential for optimizing train scheduling and restoring normal operations in a timely manner. To address this challenge, this study proposes a deep learning architecture based on spatio-temporal feature extraction for accurate train delay prediction. A graph attention network-long short-term memory block is introduced to capture the spatio-temporal evolution features of different trains. In addition, a sequence forecasting approach and a mixture of experts module are integrated to model the complex relationships between the target train’s delay and its previous states. The delay evolution features are incorporated into the loss function, allowing for more accurate predictions. Experimental results show that the proposed model outperforms the baseline models, achieving at least an improvement of 50.62% and 27.89% in root-mean-squared error and mean absolute error, respectively. When the error tolerance is set within 3 min, the prediction accuracy reaches 96.68%. Experimental results demonstrate the superior performance of the proposed model.

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

The paper studies the faded communication-based coordinated model-free adaptive iterative learning control (MFAILC) of multiple high-speed trains (MHSTs) against periodic denial-of-service (PDoS) attacks. First, considering the nonlinearity and uncertainty of the train operation, the dynamic model of MHSTs is constructed, and then followed by the newly established linear data-relationship model. Next, the random faded channel is expressed by Rice fading model, and the PDoS attacks are introduced with the help of the random coefficients. After giving the theoretical analysis, the compensation scheme is conducted, and the research is further extended to the switching topologies. Finally, a set of numerical tests is conducted to confirm the practicability of the MFAILC approaches.Note to Practitioners—HSTs have the characteristics of high speed, high safety, etc. The practical problems that motivate this work are the complexity of train model, the instability of the networks and the urgent requirement to further improve the operation efficiency. Meanwhile, the possible application areas include the automatic operation of HSTs and the cooperative operation of train groups. Specifically, the potential of this work includes: 1) eliminating the requirement of detailed modeling of train dynamics; 2) providing a theoretical basis for reliable train operation in an unstable network environment, and 3) improving the efficiency of train group operation through cooperation. Nevertheless, the limitation of this paper is that the results have not been verified on the actual trains and railways. To extend it to be more practical, we will further investigate more practical constraints in the train operation environment, such as the constraints of the traction network, the constraints of the track adhesion condition, and also continue to optimize the controller parameters iteratively.

China’s high-speed railway (HSR) has entered the era of networked operation. Any internal disturbance or eternal disturbance may result in delays of some trains and even cascading delays, which will not only reduce the traffic efficiency of HSR, but also break passengers’ travel and lower their satisfaction. Studying the delay propagation mechanism could assist the dispatcher in suppressing the negative effect of disturbances. However, current studies seldom consider the withholding strategy’s impact on delay propagation. Inspired by this, this article proposes a novel bi-directional delay propagation model combined with the trains’ operation trajectory and stations’ withholding strategy. Moreover, the operation constraint, station capacity constraint, and interlocking constraint are also considered. Then, the primary delay under section disruption (SD) and section temporary speed limit (STSL) are derived based on the location of the disturbance, duration time of the disturbance, and the operation strategy. Then, a max-plus algebra-based delay propagation model is established to compute the corresponding secondary delays. Also, the All Pair Critical Path algorithm is modified to incorporate the station capacity constraint in the searching process. Simulations based on the real China HSR subnetwork are implemented to verify the proposed model. Compared with the current study, the proposed model could accurately unfold the delay propagation in the opposite train heading direction. Besides, the relationship among disturbance duration, primary delay, and accumulative delay for the SD scenario and the relationship among temporarily limited velocity, primary delay, and accumulative delay for the STSL scenario are revealed.

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

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

The independent hierarchy of train dispatching command and train operation control in the existing urban rail transit systems restricts the improvement of operational efficiency and emergency handling capability. This article focuses on integrating train regulation and speed profile optimization by utilizing a feature learning and hybrid search algorithm. Specifically, a genetic algorithm (GA) is used to optimize the train speed profile for a fixed interval running time, and then, the generated labeled sample data are used to train a convolutional neural network (CNN) to learn and extract the features of the optimal speed profile. The nonlinear mapping relationship between input and output variables in trajectory optimization is characterized by a well-trained CNN to reduce the computation time of the optimal speed profile during train regulation. The input variables comprise line conditions and interval running times, while the output variables include the corresponding energy consumption and operating condition switching points of the optimal speed profile. An integrated model of train regulation and operation control is developed with the objective of minimizing total train delay time and energy consumption. To ensure convergence and global search capability, we design a hybrid search algorithm-based train regulation algorithm. Simulation experiments are conducted using data from the Beijing Yizhuang line to validate the effectiveness of the proposed model and algorithms. The experimental results demonstrate that the proposed method can provide an optimal scheme for train regulation and speed profiles.

This article presents a new safe fault-tolerant control scheme for train formation by designing an adaptive event-triggered reinforced performance technique. The new features and merits of the proposed method are as threefold aspects. 1) The proposed method integrates safety constraints on train position and speed into the control design process. A nonlinear transformation function is employed to convert constrained train states into unconstrained error variables, which simplifies the dealing of nonlinearities arising from safety constraints, ensuring trains maintain safe tracking distances and speeds. 2) by utilizing the projection algorithm, the proposed method estimates and compensates for actuator failures using safety-constrained train position and speed data. This method demonstrates superior tracking accuracy compared to existing fault-tolerant control algorithms, even in scenarios with actuator faults. In addition, considering the continuous control challenges due to the complex physical structure of high-speed trains, an event-triggered approach is introduced to alleviate unnecessary operations and minimize wear and tear on mechanical components caused by frequent updates. 3) In comparison to pioneering so-called prescribed performance control methodology, where the tracking errors are kept within predefined boundary functions regardless of control gains and other parameters, the “reinforced performance” of this work ensures that defined errors are guaranteed to evolve within regions characterized by predefined boundary functions and control parameters simultaneously, correspondingly, the ultimate convergence regions can be adjusted to be arbitrarily small by choosing proper control parameters.

Optimization-based methods are commonly applied in autonomous driving trajectory planners, which transform the continuous-time trajectory planning problem into a finite nonlinear program with constraints imposed at finite collocation points. However, potential violations between adjacent collocation points can occur. To address this issue thoroughly, we propose a safety-guaranteed collision-avoidance model to mitigate collision risks within optimization-based trajectory planners. This model introduces an “embodied footprint”, an enlarged representation of the vehicle’s nominal footprint. If the embodied footprints do not collide with obstacles at finite collocation points, then the ego vehicle’s nominal footprint is guaranteed to be collision-free at any of the infinite moments between adjacent collocation points. According to our theoretical analysis, we define the geometric size of an embodied footprint as a simple function of vehicle velocity and curvature. Particularly, we propose a trajectory optimizer with the embodied footprints that can theoretically set an appropriate number of collocation points prior to the optimization process. We conduct this research to enhance the foundation of optimization-based planners in robotics. Comparative simulations and field tests validate the completeness, solution speed, and solution quality of our proposal.

Wind speed forecasting provides the upcoming wind information and is important to the safe operation of High-Speed Railway (HSR). However, it remains a challenge due to the stochastic and highly varying characteristics of wind. In this paper, we propose a novel Transformer-based method for short-term wind speed forecasting, named WindTrans. Two major cruxes are addressed. First, the task is performed on fine-grained wind speed gathered from multiple sensors. These data present dynamic intra-series and inter-series correlations, which are hard for previous methods to recover. We advance a Transformer-based deep learning model, which has two distinctive characteristics: (1) a graph encoder, which captures the dynamic spatial correlation among wind speeds at different locations, and (2) a temporal decoder to model long sequence wind speed time series, which is resistant to noise in time series. Second, wind speed patterns gradually evolve in long-term periods, thus deactivating prediction models trained on historical data. To tackle this bottleneck, we put forward an experience replay-based scheme to renew the model regularly. To ensure that the renewed model still dominates historical wind patterns, we store and replay only a small portion of historical data named episodic memory. A simple but efficient strategy is designed to constitute episodic memory and thus relieve the computation burden. Experiments conducted on two real-world datasets demonstrate the superiority of our method over existing approaches. Particularly, WindTrans surpasses state-of-the-art methods by up to 36.7%, 29.3% and 13.3% improvement in MAPE measure for 1 hour ahead prediction on 10-minute, 5-minute, and 1-minute-based tasks, respectively. Furthermore, via our continual learning scheme, the model retains competitive performance with only 6.9% datum stored and retrained on.

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

The efficient and safe evacuation of passengers is an important foundation for ensuring the service level of subway stations and an important part of promoting the development of smart urban rail. Due to the complex structure of stations, strong heterogeneity of passenger flow, and high uncertainty of road interruption in emergencies, passenger evacuation dynamics have received widespread attention and become an inevitable research trend. In order to deeply understand the current research focus and development trend related to emergency evacuation of passengers in subway stations, 196 relevant published literature from 2000 to 2023 are analyzed, and a systematic review is provided. The publication trend of literature, the distribution of countries and institutions, the co-occurrence of keywords, and the types of evacuation are comprehensively reviewed from the perspective of bibliometrics. It can be observed that the number of literature related to passenger evacuation in subway stations has shown explosive growth, especially in the past three years. Special attentions are paid to the current main research hot spots, where passenger evacuation behavior is studied from multiple dimensions, including passenger evacuation dynamics modeling, evacuation simulation, evacuation behavior analysis, and evacuation optimization. Challenges restricting the improvement of passenger emergency evacuation capability are identified, and corresponding possible research directions are proposed and discussed.

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

Inspired by the behavior law of human social groups, a new swarm intelligence algorithm named the dual-population social group optimization (DPSGO) algorithm is proposed in this article. Based on the primitive social group optimization (SGO) algorithm, dual-population grouping technology, reverse learning technology, immigration migration technology, and Gaussian mutation are introduced to further simulate the behavior law of actual human social groups. Experimental results and performance comparison show that the DPSGO algorithm has a better searchability and convergence rate. In addition, aiming at the socially hot issue of aviation safety, the simulation and experimental results show that the temperature measurement error can be reduced to less than 7.5 °C by using the DPSGO algorithm combined with reflected radiation correction to process the aeroengine multispectral radiation temperature measurement data. This article is of great significance to the design and optimization of swarm intelligence algorithms by using the behavior law of human social groups and provides valuable guidance for enhancing the safety monitoring of aeroengines.

Setting up guidance equipment and leaders is widely used as an effective measure to improve the operation and evacuation efficiency of subway stations for the safety of passengers. Cooperation among different kinds of guidance modals can benefit the passenger evacuation process and reduce the operating and management costs as well as the risk of injury to people in subway stations. This article proposes a framework of multimodal cooperative guidance (MMCG) systems for commanding the crowd evacuation in case of emergency, where three types of guidance modes are considered. The bi-level MMCG optimization models are constructed to determine the optimal quantities and initial locations of multimodal guidance. The MMCG schemes are designed by minimizing cost functions taking into account the constraints of the number of guidance, valid coverage, and guiding expectation. An extended social force (SF) model is proposed to study crowd evacuation dynamics with multimodal guidance. The computational experiments are conducted to evaluate the performance of the proposed cooperative guidance schemes at the subway platform scenario. Three unimodal guidance schemes and a contrasted scheme without guidance are also proposed as comparison schemes. The results show that the crowd evacuation efficiency and the utilization ratio of exits are improved by taking into account the cooperation among different guidance modals.

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

Replacing traffic signals with roadside vehicle-to-infrastructure systems in the era of connected and autonomous vehicles (CAVs) is promising. Managing CAVs in a signal-free intersection, known as autonomous intersection management (AIM), controls the driving behavior of each intersection-traverse CAV to maximize the throughput. Although AIM improves the gross throughput, the fairness of each individual vehicle in its right of way is not seriously considered. This study sets up an AIM system in the cyber–physical–social space to trade traverse priorities quantitatively and fairly. To that end, one needs an AIM method that is optimal and stable, otherwise no convincing trades of traverse priorities could be made. This study proposes a near-optimal lane-free AIM method based on numerical optimal control, wherein log-exp functions are deployed to convexify nondifferentiable collision-avoidance constraints. Besides that, a parameterized social force model (SFM) is proposed to provide a tunable initial guess for numerical optimal control. By tuning the urgency weights in SFM, one may get cooperative trajectories in different homotopy classes, which are further utilized to decide the amount of virtual currency to reward those CAVs who tend to share their traverse priorities. The overall method improves the traverse throughput with individual fairness respected. In experiencing this system, passengers learn how to behave with politeness when they drive manually. Experiments show the efficiency and robustness of the AIM method and also show the efficacy of the overall priority-sharing system.

China’s High-Speed Railway (HSR) has entered the era of networked operation. Unpredictable emergencies may disrupt the normal operation of the railway, resulting in train delays. The delays may even cause cascading delays due to the track constraint. This paper proposed a delay propagation model by max-plus algebra, considering the constraint of the HSR network, to predict the spatial-temporal range of cascading delays under emergencies. Moreover, the operation strategy re-management in sections and the re-timing strategy due to the station capacity constraint are studied under emergencies, forming a chain model of emergencies, primary delays, and secondary delays. Simulation results show that the proposed model could accurately predict the secondary delay under different emergencies.

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

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

Under the condition of network operation of high-speed railways (HSRs), the influence of disruptions on the train control and dispatching at the current line and related lines is more and more significant. This article focuses on the timetable cooperative rescheduling problem with multidispatching sections of HSRs from a macroscopic perspective in the case of large disruptions. The problem is formulated as a mixed-integer linear program (MILP) model on the objectives of minimizing the weighted sum of the arrival delay time of trains, the delay time of depart trains at the handover station, and the number of delays of trains at all stations. The strategies of retiming and reordering are adopted to generate the rescheduling scheme and reduce delay propagation by making full use of three kinds of buffer time reserved in the timetable, i.e., buffer times of train operation in the station, train running in the section, and electric multiple unit (EMU) connection. A case study of the timetable rescheduling at the two adjacent dispatching sections of the Beijing–Shanghai HSRs line is conducted to evaluate the performance of the proposed integrated rescheduling approach. The relationship between computing time and the quality of rescheduling schemes is also investigated. The computational results show that the proposed approach can generate a conflict-free timetable with the minimum arrival delay time and the number of delays of all trains at all stations compared to the nonintegrated rescheduling approach and the benchmark solution of the first-come-first-serve (FCFS) approach. The propagation of delay between dispatching sections is also greatly suppressed. The results can provide support for dispatchers to making reasonable rescheduling decisions in the case of large disruptions.

This article addresses a consensus problem of second-order discrete-time agents in general directed networks with nonuniform position constraints, switching topologies, and communication delays. A projection operation is performed to ensure the agents stay in some given convex sets, and a distributed algorithm is employed for the consensus achievement of all agents. The analysis approach is to use a linear transformation to convert the original system into an equivalent system and then merge the nonlinear error term into the convex null of the agents' states so as to prove the consensus convergence of the system based on the properties of the non-negative matrices. It is shown that all agents finally converge to a consensus point while their positions stay in the corresponding constraint sets as long as the union of the communication graphs among each certain time interval is strongly connected even when the communication delays are considered. Finally, numerical simulation examples are given to show the theoretical results.

This article is concerned with an expansive errors (EE)-based fuzzy adaptive prescribed performance control of a class of multiple-input and multiple-output nonlinear systems in the presence of unknown interconnection nonlinearities using fuzzy residual approximation technique. Based on an newly defined expansive error, residual nonlinearities’ fuzzy approximation scheme is proposed. The merits of designed control can be presented as twofold: 1) by incorporating an EE-based technique into pioneering prescribed performance control methodology, the defined intermediate and output tracking errors are kept within the regions represented by preassigned functions and control parameters simultaneously and 2) expansive errors-related residual nonlinearity is approximated by fuzzy logic systems, which further ameliorates the output tracking performance by compensating the affects raised by composite inner and interconnection nonlinearities among subsystems. With the designed expansive errors-based fuzzy adaptive prescribed performance control, all the closed-loop signals are kept bounded in the sense of Lyapunov stability theorem. Comparative simulation studies are presented to verify the effectiveness and advantages of theoretical findings.

The coordinated control problem for a multiple high-speed train (HST) system subject to unknown communication delays is systematically investigated in this paper. Taking into consideration the inertial lag of the servo motor, a third-order nonlinear control model is constructed to capture the dynamics of a train in real-world operations. By virtue of the backstepping linearization technique, the coordinated control of trains is formulated as a stabilization problem for a linear multiple-input multiple-output system with an unknown input delay. Distributed control laws with a time-varying low gain parameter are designed, besides solving the stabilization problem, to guarantee a fast convergency rate during the train status adjustment process. Numerical examples are provided to illustrate that the time-varying low gain parameter design achieves better control performance compared with the traditional constant low gain feedback design in terms of the convergency rate and the system overshot, and that the proposed control method is effective in train tracking distance adjustment.

The precise operation control of high-speed trains is pivotal to maintain the safety and efficiency of trains, while the inevitable state delays will seriously attenuate the performance of control system. In this paper, an adaptive iterative learning control (ILC) approach for high-speed trains is presented in the presence of the nonlinearly parameterized uncertainties and multiple unknown state delays, aiming to drive that the displacements and velocities of trains can track the desired reference trajectories. To describe the operational dynamics of trains more realistically, the multi-particle model of trains involving multiple time-varying delays is established by analyzing the aerodynamic resistance, mechanical resistance, and coupler force acting on different cars. The proposed adaptive ILC scheme fully leverages various techniques, e.g., the hyperbolic tangent function, the parameter separation, to cope with the inherent nonlinearities, uncertainties and couplings of system. Specially, to eliminate the negative influence of unknown delays, an appropriate Krasovskii function is integrated into the Lyapunov criterion to devise the learning controller and check the stability of control systems. The novelties of our work lie in that the refinement model and periodical characteristic are simultaneously utilized to improve the practicability and performance of control scheme for the high-speed trains with multiple state delays.

Railway system performances depend on effective dynamic scheduling and train operation control. The fast expansion and increasing complexity of high-speed railway (HSR) networks raise new challenges in maintaining the punctuality and efficiency in daily operations, in particular, in the event of disruption. This paper aims to review the state-of-art in dynamic traffic scheduling, trains operation control, and their integration for safer, more punctuate, efficient, and resilient HSRs, whose origins may trace back to their counterparts in traditional railways. First, the existing two-tier hierarchy of scheduling and control in HSR’s daily operation is introduced. At the higher layer of scheduling, a general model of dynamic train scheduling is discussed, followed by reviewing the scheduling methodologies. At the lower layer of train operation control, recent progress in tracking control of high-speed trains is discussed, with focus on the latest advances in single train control and cooperative control for multiple trains. Then, as the trend of technological progress for future HSRs, the recent development of integrating dynamic scheduling and operation control is introduced, which is made possible by efficient information exchanges among the scheduling subsystem and the train control subsystem. A three-layer integration framework and associated co-optimization methodologies are presented by introducing a co-optimization layer that bridges the separated scheduling and control layers. Finally, this review is concluded with discussions on open questions and possible directions for future research.

This paper presents a fuzzy adaptive protective control method for autonomous high-speed trains (HSTs) automatic operation using a new outstretched error feedback design approach. In order to stabilizing the error dynamics with respect to target position and speed profiles of controlled HSTs, nonlinear transformation in prescribed performance control methodology is used to convert running states subject to protective (constrained) information, imposed by automatic train protection (ATP) subsystem, to new coordinates in unconstrained form. By blending a new outstretched error feedback and fuzzy approximation, it is guaranteed above-mentioned errors are kept within regions characterized by error boundary (or prescribed performance) functions and control parameters simultaneously, which can be adjusted to arbitrarily small even without error decreasing boundary functions. Fuzzy approximation is used in compensating unknown running resistances. It is rigorously proved that the resulting closed-loop system is stable in sense of Lyapunov stability in the presence of unknown resistance, containing basis and aerodynamic resistances with uncertain parameters and piecewise continuously slope resistance over varying gradient profile. The proposed control is demonstrated to be effective by comparative simulations of train G1 running on Beijing-Shanghai railway line.

This paper proposes a novel train trajectory optimization approach for high-speed railways. We restrict our attention to single train operation scenarios with different scheduled/rescheduled running times aiming at generating optimal train recommended trajectories in real time, which can ensure punctuality and energy efficiency of train operation. A learning-based approach deep deterministic policy gradient (DDPG) is designed to generate optimal train trajectories based on the offline training from the interaction between the agent and the trajectory simulation environment. An allocating running time and selecting operation modes (ARTSOM) algorithm is proposed to improve train punctuality and give a series of discrete operation modes (full traction, cruising, coasting, full braking), and thus to produce a feasible training set for DDPG, which can speed up the training process. Numerical experiments show that an optimized speed profile can be generated by DDPG within seconds on a realistic railway line. In addition, the results demonstrate the generalization ability of trained DDPG in solving TTO problems with different running times and line conditions.

Emergency signage systems provide effective route guidance and evacuation information for pedestrians in case of emergencies such as fire, blackout, and explosion. This paper proposes a modified social force (SF) model to investigate crowd evacuation dynamics taking into account the influence of emergency signs. The perceiving probability model is formulated for the quantitative description of the probability that pedestrians can successfully notice the sign and clearly perceive the guidance information. Simulation experiments and controlled experiments are designed to calibrate the parameters of the proposed model. The effectiveness of the modified SF model is preliminarily verified by comparing the simulation results with experimental data and/or empirical results such as fundamental diagrams and self-organization phenomena. A case study of crowd evacuation simulations at a typical Beijing subway station is conducted to evaluate evacuation performance of three signage distribution schemes, i.e., Maximal Covering (MaxCover), Uniform, and Random, which are proposed by the maximal covering location and empirical approaches, as well as contrasted scheme without emergency signs. The effects of the quantity and distribution of emergency signs on crowd evacuation efficiency are studied quantitatively by simulations. The results show that installing emergency signs can improve evacuation efficiency no matter what distribution scheme is adopted. By choosing an appropriate distribution scheme i.e., MaxCover, the evacuation performance can be further improved and the evacuation time can be significantly reduced.

As a significant part of the next-generation smart grid, electric vehicles (EVs) are essential for most countries to achieve energy independence, secure energy supply, and alleviate the pressure on environmental protection and energy security. Although EVs have grown rapidly, the slow recharge time is still the biggest obstacle to a wider application. While gasoline vehicles can pump enough gasoline in less than ten minutes, which can carry themselves a few hundred miles. However, most of today’s fast-charging techniques take half an hour only to provide very limited miles of electric driving range.

In metro systems, the train passenger load is an important parameter that reflects both the utilization level of the trains provided by the operator and the comfort level of passengers in terms of crowdedness. In addition to minimizing the energy consumption and passengers' time cost, the train passenger load should also be optimized in order to maintain a high utilization rate of trains and an adequate level of comfort for passengers. In this paper, a multi-objective train timetable optimization procedure is proposed to minimize the total energy consumption, the average waiting time, and the average maximum load deviation. Case studies on the Beijing Yizhuang line show that the proposed approach could effectively reduce the total energy consumption, the average waiting time, and the average maximum load deviation, thus ensuring a high service quality and a low operational cost for the metro system.

This paper studies the dynamic performance of subway transportation systems. A three-layer model, consisting of a rail layer, a train layer, and a passenger layer, is proposed to describe the structure and operation of a metro system. Two parameters, namely, time efficiency and maximum load ratio, are proposed to assess the transport efficiency and the train utilization rate, respectively. Case studies of the metro networks in Beijing, Tokyo, and Hong Kong show that the time efficiency decreases nonlinearly with the increase of vehicle resource and passenger demand, whereas maximum load ratio varies in an opposite manner. For the three metro networks under study, the Tokyo metro system has the highest time efficiency when passengers adopt a shortest-path (SP) routing strategy, while the Hong Kong system's highest time efficiency exceeds the others' when passengers adopt a minimum-transfer-path (MTP) strategy. In general, the maximum time efficiency is higher when passengers adopt SP routing rather than MTP routing. Moreover, the Beijing metro system has the highest maximum load ratio, regardless of the passengers' routing behavior. This paper can be applied to a metro network to optimize the train departure interval under a certain passenger entrance rate, with the aim to maximize the time efficiency and maximum load ratio. Our model permits assessment of the operational effectiveness of metro systems, which helps the metro operators to improve the performance and reduce the cost.

Big data for social transportation brings unprecedented opportunities for us to solve the transportation problems that cannot be solved by traditional methods and build the next generation of the intelligent transportation system (ITS). As one of the important functions of the ITS, supply-demand difference prediction for autonomous vehicles provides a decision basis for its control. In this article, a new learning process is proposed with Multiple feature Extraction and Fusion utilizing the combination of deep and shallow Features (MEFF) (the spatial deep features, (short and long) temporal deep features, and fuzzy shallow (semantic) features). The spatial deep features are captured with residual network and dimension reduction in spatial deep block. The fuzzy shallow (semantic) features are captured with multiattention fuzzy mechanism in the fuzzy shallow block. With the fused spatial deep features and fuzzy shallow features, the temporal deep features are captured with long short-term memory (LSTM) and attention mechanism in the temporal and prediction block to get the final prediction results. Based on two different distributions of membership attention (mean distribution and Gaussian distribution) in the fuzzy shallow block, our process MEFF has two methods, i.e., MEFF-mean method and MEFF-Gaussian method. Extensive experiments show that our methods provide more accurate and stable prediction results than the existing state-of-art-methods.

This paper provides a critical review on the state-of-the-art pedestrian and evacuation dynamics so as to comprehensively comprehend the motion behaviors of pedestrians from observations to simulation aspects. Types of typical data collection methods, namely the field survey, the controlled experiment, and the animal experiment, are classified, and the connections and differences of these three observation methods are explored. Pedestrians' complex behaviors characterized by the self-organization phenomena and movement data characterized by the fundamental diagram are then studied after the data collections, which can be used to calibrate and validate the pedestrian models. The mathematical models for pedestrian dynamics from both tactical level and operational level are also highlighted. The simulation data produced by the mathematical models could further reproduce pedestrian behaviors during the observations and contribute to decision makings for improving the evacuation efficiency. The applications of pedestrian models for behavior analysis, evacuation simulation, and layout design are also presented. Some challenges and future directions in the pedestrian and evacuation dynamics are also put forward. Findings presented in this study are helpful for researchers who want to understand the pedestrian and evacuation dynamics and to perform further research in this field.

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.

As one of the important functions of the intelligent transportation system (ITS), supply-demand prediction for autonomous vehicles provides a decision basis for its control. In this paper, we present two prediction models (i.e. ARLP model and Advanced ARLP model) based on two system environments that only the current day's historical data is available or several days' historical data are available. These two models jointly consider the spatial, temporal, and semantic relations. Spatial dependency is captured with residual network and dimension reduction. Short term temporal dependency is captured with LSTM. Long term temporal dependency and temporal shifting are captured with LSTM and attention mechanism. Semantic dependency is captured with multi-attention mechanism. Extensive experiments show that our frameworks provide more accurate prediction results than the existing methods.

In this paper, pedestrian evacuation in the view-limited condition is investigated by using an extended social force model that considers both visibility distance and herding behavior. At first, the relations between visibility distance and mean evacuation time, density evolution, respectively, are explored. Then, the effects of herding behavior on the evacuation features, i.e., mean value and frequency distribution of evacuation times, and the evacuation process are investigated. The results show that in a certain range, the larger the visibility distance is, the faster is the evacuation process, and different visibilities lead to different tendencies of density fluctuations. It is found that herding behavior plays a beneficial role in evacuation and group formation appears when herding behavior dominates the selection of directions. In addition, the dual effects of pedestrian density on mean evacuation time are discovered. Our research can provide the theoretical guidance for the formulation of evacuation strategies in the view-limited condition.

The cruise control problems for high-speed trains are investigated in this paper. Both a single train and multiple trains on a railway line are considered. The cars in a single train are modeled as a group of ordered particles connected by flexible couplers. Each car is viewed as an intelligent agent that communicates with its neighbors, making the train a multi-agent system. The information transmission topology among these agents is represented by a connected undirected graph. Distributed cooperative control laws are constructed that achieve displacement and speed consensus among cars at a desired profile, while guaranteeing the coupler displacements to be within a safety range and converge to the nominal value. For multiple trains on a railway line, each train has access to the information of the trains within its wireless communication range, making all cars in these trains a multi-agent system. The underlying communication topology is now a state-dependent undirected graph. Distributed control laws are designed such that, besides achieving coordinated control of cars among each train, consensus among trains at the desired displacement and speed profile and connectivity among trains are also achieved, while avoiding collision. Extensive simulation results are presented to illustrate the theoretical conclusions we have reached.

As demand for rail transportation continues to increase rapidly, significant challenges have emerged in many railway systems in terms of Capacity, Safety and Customer Satisfaction. To deal with these challenges, intelligent technologies, such as artificial intelligence, big data, and machine learning, have been gradually introduced to rail transportation. It is therefore timely and appropriate to have a focused investigation and discussion about Intelligent Rail Transportation. This special issue provides a forum for scientists and engineers working in academia, industry, and government to present their latest research findings and engineering experiences in developing and applying intelligent technologies to improve railway’s autonomy, cooperation, and integration.

If high-speed trains move under a moving block signaling (MBS) system, it is a quite challengeable and significant issue to cooperate the multiple high-speed trains, such that they are separated safely and governed steadily. As an intelligent, comprehensive, and advanced modern train operation control system, the MBS system will definitely, in the near future, replace the widely used fixed block signaling system due to its modern communication, multi-source positioning, and advanced control method. Due to the involvement of several subsystems and lots of track-side equipment, such as radio block center (RBC), global system for mobile communications-railway (GSM-R), and so on, it is a nontrivial task to develop a reliable MBS system under complex operational environments, particularly without a pivotally cooperative control method for the movement of multiple high-speed trains. This paper addresses the cooperative control for multiple high-speed trains to achieve prescribed performance tracking, i.e., the speed and the position of high-speed trains are guaranteed to be confined to specific speed limitations and allowed distances ratified by automatic train protection and moving authority, respectively. The proposed control requires no prior information of the empirical parameters of the operational resistances and online adjusts by proper adaptation laws. Theoretical analysis and simulation results are given to demonstrate the effectiveness of the proposed control methods.

For metro systems in over-crowded conditions, when an unexpected disturbance occurs, the operation of trains might be disturbed due to the high frequency and density of the metro traffic. A large number of passengers might be stranded on platforms due to service gaps and the limited free capacity of trains. In this paper, by introducing binary variables as selection indicators for ATO profiles which were preset in on-board ATO systems by metro signal suppliers, we develop a mixed integer programming (MIP) model for a metro train timetable rescheduling problem in order to jointly optimize the total train delay, the number of stranded passengers, and the energy consumption of trains. We formulate the total energy consumption as the difference between the tractive energy consumption and the regenerated energy by considering the mass of in-vehicle passengers. Then, we adopt commercial optimization software CPLEX to solve the proposed model, which can obtain tradeoff solutions in a short time. Finally, three numerical experiments based on real-world operational data are carried out to verify the effectiveness of the proposed method.

The adoption of passenger leaders could make crowd evacuation in urban railway transit (URT) stations more efficient. The number, location, and the actions of the leaders are the most important elements in an evacuation strategy and have a great impact on evacuation efficiency. This paper proposes a hybrid bi-level model to optimize the number and initial locations of leaders as well as the routes of leaders during the evacuation, which explicitly incorporates the passengers' guidance demand and multi-leader coordination mechanism. The leaders' initial locations are generated by solving the maximal covering location problem (upper level model) and their evacuation routes are determined by a co-simulation heuristic approach (lower level model). The social force model and its modifications are used to model the dynamics of common evacuees, leaders, and followers in simulation models. The convergence performance of the proposed co-simulation heuristic approach and the effectiveness of the optimal evacuation strategy have been investigated and demonstrated using a case study of a typical island platform of Beijing's URT station. Three other evacuation strategies are considered for comparison purposes in order to show the influence of the number and initial locations of leaders as well as the multi-leader coordination mechanism during the evacuation process. Our analysis supported by simulations shows the following: 1) the optimal number of leaders exists for a given human cost and guidance demand constraints; 2) the distribution of leaders for maximal covering makes the evacuation of the followers more efficient; and 3) the proposed optimal evacuation strategy has better performance in terms of shorter evacuation time and higher utilization of exits compared with other considered strategies.

In this paper, we develop robust adaptive nonsingular terminal sliding mode (NTSM) control methodologies to solve the position and the velocity tracking control problem of the automatic train operation (ATO) system subject to unknown parameters, model uncertainty, and external disturbances. A novel nonlinear nonsingular terminal sliding manifold is proposed by considering that its parameter is unknown, which need to be estimated via a proposed non-negative adaptive law. And a corresponding novel robust adaptive NTSM control strategy, which enables the position tracking error and the velocity tracking error of the ATO system to converge to zero, and eliminates the singularity caused by terminal sliding mode controller, is proposed. Furthermore, unknown parameters of the sliding manifold and the ATO system can be estimated online by the proposed methodology. Simulation results show the effectiveness of the proposed methodologies in this paper.

In this paper, we propose a novel method called supernode graph structure representation to model the public transport network structure of the London city. Supernode is a set of geographically closely associated nodes. Using the supernode graph structure, the bus transport and the metro transport network structures are analyzed by treating them as independent mono-layer or multi-layer network structures. A method of spatial amalgamation is proposed to integrate the two transport layers. A set of most influential nodes in the network is identified by assigning node weight to each node with respect to both mono-layer and multi-layer analysis. The behavior of these influential nodes is better characterized by categorizing them as either emitter, absorber or neutral zones.

This paper focuses on the bibliographic analyses and collaboration patterns in the field of pedestrian and evacuation dynamics (PED) research covering the period of 1991-2017. The statistic analyses of most productive authors, institutions, countries/regions, and cited papers, as well as keywords and their trends, are conducted based on the data set collected from the Web of Science. The most productive and high-impact authors, institutions, and countries are identified. The results of bibliographic analyses show that Europe researchers dominate and guide the research of PED in that it not only has the most papers but also has six out of the ten most-cited papers. Helbing Dirk is an influential author in the research of PED field in that he has five out of the ten most-cited papers. Chinese institutions account for 70% of the top 10 most productive institution and three among the top 5 ranks. Meanwhile, researchers from China and USA have published nearly half of the papers in this field. In addition, we generate four networks, i.e., coauthorship network, coinstitution network, cocountry/region network, and document cocitation network to analyze collaboration patterns and evolution of PED research at different levels. The software of Citespace is adopted to visualize the topological interactions among authors, institutions, and countries/regions as well as document cocitations. The degree, betweenness, burst, and PageRank are selected and measured as indicators to identify the key nodes. Finally, some future directions are put forward. The results of this paper provide a better understanding of patterns, trends, and other important factors as a basis for directing research activities, sharing knowledge, and collaborating in the field of PED research.

The information exchange gap between current operation control and dynamic scheduling in high-speed railway systems (HRSs) still exists, and this gap has hindered the further integrative improvement of HRSs. This paper aims to explore a feasible solution to bridging the information exchange gap for further improving the efficiency of HRSs, with the parallel intelligent systems for integrated HRS operation control and dynamic scheduling first analyzed and constructed using the ACP approach, that is, "artificial systems" (A), "computational experiments," (C) and "parallel execution" (P). Then, on the basis of the constructed parallel intelligent systems, experiments on several typical scenarios in HRSs are conducted to achieve a set of control and management strategies for actual HRSs. Experimental results show that a number of powerful tools provided by the proposed parallel intelligent systems can be utilized not only to study the current HRSs, but also to further undertake research on integrated operation control and dynamic scheduling for HRSs.

This paper introduces new models to describe pedestrian crowd dynamics in a typical unidirectional environment, such as corridors, pathways, and railway platforms. Pedestrian movements are represented in a two-dimensional space that is further divided into narrow virtual lanes. Consequently, pedestrians either move in a lane following each other or change lanes, when it is desirable. Within this framework, the motions of pedestrians are modeled as a two-dimensional and two-time-scale hybrid system. A pedestrian's movement along the crowd direction is labeled as the x direction and modeled by a real-valued process, a solution of a differential equation in continuous time, the lane change is labeled as the y direction. In contrast to the x direction dynamics, the movements in the y direction only happen at some time epoch. Although the movements are still on the same time horizon as the x direction movements, with a slight abuse of notation and for simplicity and convenience, we use discrete time as the time indicator, and model the movements by a recursive equation taking values in a finite set. Under common assumptions of crowd movements, we prove that the crowd movements in the x direction will converge to a uniform distance distribution and the convergence rate is exponential. Furthermore, by using a velocity-distance function to represent the common crowd and traffic congestion scenarios, we show that all pedestrians will asymptotically move with a uniform group speed. In the y direction, when pedestrians naturally wish to change to faster lanes, we show that the numbers in each virtual lanes converge to a balanced distribution and hence achieves asymptotic consensus as shown typically in a crowd behavior. Stability and convergence analysis is carried out rigorously by using properties of circular matrices, stability of networked systems, and stochastic approximations. Simulation studies are used to demonstrate the main properties of our modeling approach and establish its usefulness in representing pedestrian dynamics.

This talk presents an observer-based neural adaptive dynamic surface control for MIMO uncertain nonlinear systems based on a nonlinear feedback technique, which tries to combine the merits of high gain feedback and low gain feedback in an easily manner. The core idea of such technique is that the feedback gain holds nonlinear mapping relationship with system states, which is achieved by a continuous differentiable nonlinear gain function. Caused by the compound property of nonlinear gain feedback, a non-quadratic Lyapunov function is designed to prove the closed-loop stability. Comparative results are shown to verify the effectiveness.

Regenerative braking technology has been widely used by subway trains, where regenerative braking energy (RBE) will be generated during train braking processes. An RBE usage method is proposed for subway trains, where train braking processes are predicted based on the field data of the train control system, and the braking information will be obtained in advance. The RBE will be calculated, and then, part or all of it will be distributed to neighboring trains in the same or adjacent power supply sections. Several feasible schemes for the RBE distribution are given, where the speed profiles of the neighboring trains will be adjusted to absorb the distributed RBE. An optimal RBE distribution scheme with the most effective usage of RBE will be obtained based on calculation results of the given schemes. For each adjusted speed profile of the neighboring train, a power process will be normally introduced. Existence of the optimal solution for speed profile adjustment is analyzed under the precondition that the train runs in a long speed-holding journey, and then, the necessary condition of the optimal solution is found by perturbance analysis. A simulation case of Yizhuang Line in Beijing subway is studied, and the simulation results show that the proposed method can use the RBE efficiently.

Microarrays are one of the latest breakthroughs in experimental molecular biology, which already provide huge amount of valuable gene expression data. Biclustering algorithm was introduced to capture the coherence of a subset of genes and a subset of conditions. In this paper, we presented a MIWB algorithm to find biclusters of gene expression data. MIWB algorithm uses the weighted mutual information as similarity measure which can be simultaneously detected complex linear and nonlinear relationships between genes. Our algorithm first used the weighted mutual information to construct the seed gene set of each biculster, then we calculated each gene's probability belonging to each bicluster and complete the initial partition of genes set utilizing the given threshold, then by optimising the objective function we completed weights update and conditions set selection, by further repartition of the entire dataset and optimization of biclusters we obtained the final biclusters. We evaluated our algorithm on yeast gene expression dataset, and experimental results show that MIWB algorithm can generate large capacity biclusters with lower mean squared residue.

Consensus of multiagent systems (MASs) is an intriguing topic in recent years due to its widely used application in robotics, biology, computer, and social science. In the real world, the evolution of MAS is inevitably involved in dynamical environments and the recent development of MAS calls for novel tools for the analysis of MAS with dynamic topology. In addition, the interactions between agents are generally nonlinear and environmental noises are ubiquitous in the communication channels between agents. However, the existing investigation on MAS places little attention on nonlinear models and the inner relationship between external disturbance and consensus is still unclear. Facing these problems, this paper considers an MAS in which the interactions between agents are nonlinear and the communication between agents are infected by environmental noises. By using a novel method of nonsmooth Lyapunov candidate, it has been demonstrated that such an MAS can realize robust consensus under the conditions of jointly (sequentially) connected topology and bounded noises. Finally, simulation results validate the effectiveness of these criteria.

The friction between wheel and track is usually called adhesion force, and it is the critical factor for the movement of trains. On one hand, excessive driving force of a train may lead to insufficient utilization of the adhesion effect and cause wasted energy; on the other hand, insufficient driving force of a train brings inefficient train operation. To balance the issues of energy consumption, operational efficiency, and security, it is necessary to control a train to obtain its maximal adhesion force, particularly in the cases of fast acceleration and emergency braking. However, since engineering experiments indicate a complex nonlinear relationship between the adhesion force and the slip ratio of a train, such a control problem is difficult and challenging, particularly when the optimal slip ratio is unknown. Facing this problem, this paper proposes a novel control method based on the modification of the famous super-twisting sliding mode algorithm, and rigorous mathematical analysis is given to guarantee the ultimate boundedness of the proposed algorithm. Furthermore, by considering four different control scenarios, detailed control and estimation algorithms are both proposed. Simulation result verifies that the proposed control strategy can control the train to obtain its maximum adhesion force.

Emerging communication-based train control techniques are a critical foundation for automatic or semiautomatic train operation for guaranteed safety, line utilization, operation efficiency, and energy saving toward intelligent rail transportation systems. Multiple-train cooperative control encounters great challenges from train control, communications, interval coordination, and uncertainties in operational environments. This paper introduces cooperative control methods and corresponding stability criterions for multiple trains under moving-block signaling systems. Two coordination scenarios are considered, and corresponding control algorithms are proposed, and their stabilities are established using Lyapunov and invariant-set theorems. The proposed controllers hold the minimal computation complexity, i.e., only one parameter needs online tuning by virtue of an ingenious parameter estimation technique. The methodologies utilize the information of “nearest neighbor trains” through onboard sensors and train-train (T2T) communications but guarantee global deployment and performance of the multiple trains queuing. The control abilities of the algorithms under predecessor following and bidirectional architecture modes are analyzed and demonstrated to be effective via simulation studies.

Modeling and analyzing the behaviors and characteristics of crowds in emergency is a challenging task with significant practical meanings. In this paper, a fuzzy logic approach is proposed to describe crowd evacuation behaviors, taking into account the effect of assailants. First, the microscopic pedestrian model and the assailant model are developed according to their different intentions in evacuation scenarios. Pedestrians are further divided into three categories depending upon whether they are affected by assailants. The individual's behaviors are determined by the integration of recommendations of local obstacle-avoiding behavior, regional path-searching behavior, and global goal-seeking behavior with adjustable weighting factors, which are automatically adjusted based on the perceptual information obtained from the complex interaction with surrounding environments. Then, the proposed pedestrian model is validated by comparing the simulated fundamental diagram with a large variety of empirical and experimental data. Finally, simulations in a hall with a single exit are implemented. It is shown that the model can truly reappear typical collective phenomena such as “arching and clogging” and “faster-is-slower effect.” The variations of the model and scenario parameters, such as pedestrian's desired speed, exit width, assailant's desired speed, and duration of attack, greatly influence the evacuation efficiency. In addition, a novel “circuity phenomenon,” i.e., pedestrians will give up the direction of goal when they encounter assailants or they see assailants and, at the same time, perceive a very crowded exit, is observed in crowd evacuation simulations.

This paper proposes an optimization method of train scheduling for metro lines with a train dwell time model according to passenger demand. An optimization problem of train scheduling is established with constraints of a headway equation, passenger equation, and train dwell time equation, where the train dwell time is modeled as a function of boarding and alighting passenger volumes. The aim of the optimization problem is to minimize the waiting time of passengers and train operation cost. Lagrangian duality theory is adopted to solve this optimization problem with high dimensionality. Finally, simulation results illustrate that this method is efficient to generate the train schedule, which meets the passengers' exchanging requirements between trains and platforms. The contribution of this paper is that a dwell time model is introduced in train schedule optimization, which provides the possibility of reducing the operation cost in the precondition that the exchanging time of passengers between platforms and trains is assured.

Integrating artificial systems, computational experiments, and parallel execution (ACP) is an effective approach to modeling, simulating, and intervening real complex systems. Emergency response is an important issue in the operation of urban rail transport systems for ensuring the safety of people and property. Inspired by the ACP method, this paper introduces a basic framework of parallel control and management (PCM) for emergency response of urban rail transportation systems. The proposed framework is elaborated from three interdependent aspects: Points, Lines, and Networks. Points represent the modeling of urban rail stations, Lines describe the microscopic characteristics of urban rail connections between designated stations, and Networks present the macroscopic properties of all the urban rail connections. Based on the given framework, a series of parallel experiments, which were impossible to achieve in real systems, can now be conducted in the constructed artificial system. Furthermore, the constructed artificial system can be used to test and develop effective emergency control and management strategies for real rail transport systems. Therefore, this proposed framework will be able to enhance the reliability, security, robustness, and maneuverability of urban rail transport systems in case of an emergency.

This paper addresses an on-line approximation-based robust adaptive control problem for the automatic train operation (ATO) system under actuator saturation caused by constraints from serving motors. A robust adaptive control law is proposed, which is proved capable of on-line estimating of the unknown system parameters and stabilizing the closed-loop system. To cope with actuator saturation, another robust adaptive control is proposed for the ATO system, by explicitly considering the actuator saturation nonlinearity other than unknown system parameters, which is also proved capable of stabilizing the closed-loop system. Simulation results are presented to verify the effectiveness of the two proposed control laws.

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.