Multibehavior recommendation reflect users’ personalized preferences from multiple perspectives by exploring the dependencies among various user behaviors (such as clicks, favorites, and purchases). However, they still struggle with the sparsity of target behaviors. While some approaches have integrated contrastive learning techniques, they face two significant challenges: 1) roughly summing the contrastive tasks overlooks users’ personalized behavior patterns; and 2) unreasonable denoising methods for auxiliary behaviors disrupt the original user behavior sequence. To address these challenges, we propose a novel method called explicit personalized contrastive recommendation based on multibehavior denoising (MB-PCD). Specifically, we design an explicit personalized behavior pattern extraction module to mine users’ explicit personalized behavior patterns and behavior semantics, incorporating these insights into the contrastive task. This allows the model to more accurately capture the complex dependencies between behaviors. In addition, we create a customized denoising module tailored to the characteristics of user behaviors, effectively denoising without disrupting the original order of behaviors. Furthermore, we introduce a multibehavior information fusion module to tackle the issue of target behavior sparsity. Extensive experiments across three datasets demonstrate that our method consistently outperforms various state-of-the-art (SOTA) approaches. Analysis experiments further validate that our method effectively enhances the model’s robustness.

Due to the flexibility and superior Line-of-Sight (LoS), Unmanned Aerial Vehicles (UAVs) have shown significant potential in Internet of Things (IoT) data collection. As the scale of IoT expands rapidly, higher demands are placed on energy efficiency and data transmission reliability. However, the limited battery life of UAVs restricts the application of a single UAV in large-scale, high-density wireless networks for data collection and data being sent to cloud for processing leads to poor Quality of Service (QoS) in traditional networks. To address these challenges, this paper aims to minimize UAV energy consumption while ensuring data collection reliability, proposing an energy-efficient data collection scheme in a cooperative multi-UAV scene. This scheme divides the non-convex problem into three subproblems for solution. First, to ensure the reliability of data transmission, introducing the guarantee of outage probability as a constraint, hovering altitude of the UAV is optimized to achieve the maximum coverage radius in the target area. Second, an Affinity Propagation (AP) clustering algorithm is introduced to partition the geographical area into the clusters with the minimum number which corresponds to the number of UAV movements. Finally, the set of horizontal position of the UAV hovering points can be optimized. And then, based on the three-dimensional (3D) coordinates, a hierarchical path planning algorithm for multiple UAVs is proposed which is formulated as a minimize maximum multiple traveling salesman problem (min-max MTSP) and solved effectively. Simulation results demonstrate that compared to existing methods, the proposed multi-UAV data collection scheme can ensure the reliability and decrease energy consumption.

Train autonomous circumambulate system (TACS) epitomizes a forefront advancement in train control technology, enabling autonomous route triggering, autonomous operation adjustment, autonomous train protection, and autonomous resource management. An essential challenge pertains to the real-time communication and processing capabilities required by the train control systems in TACS. In this article, we propose using edge computing (EC) in TACS to provide real-time communication and computation service for train control. To adapt to the complexity of rail transit operating environment and maintain punctuality and passenger comfort during train operation, we utilize meta-learning to update the traditional train dynamics model and harness the iterative random shooting (IRS) algorithm to optimize the autonomous train control process. Recognizing the limitations of onboard computing capabilities, we propose a model-based meta reinforcement learning approach to obtain the optimal task offloading policy. The optimal policy evaluates the channel conditions and computing resource utilization of onboard devices, and wisely determines whether to perform local computing or transmit data to EC devices for processing. In addition, our approach uses meta reinforcement learning to train the environment dynamics model prior such that, when combined with recent data, this prior can be rapidly adapted to the local environment. The model-based meta reinforcement learning approach is quite suited for the urban rail transit system where different rail lines have different operating environments, and we do not have enough data to finish a regular training task. Empirical evidence demonstrates that our proposed framework furnishes the train autonomous control system with reliable and real-time computing services, thereby significantly enhancing operational efficiency through our novel adaptive computation offloading policy.

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

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

With the advancement of Urban Rail Transits (URTs), the demand for artificial intelligence (AI) based URTs services grows exponentially. Edge intelligence (EI) leverages computing resources on the network edge to provide realtime intelligent services in close proximity. As it enables fast distributed learning, EI is envisioned to be a potential component of URTs, and ideal EI service provision is a critical concern for the intelligent development of URTs. The existing EI-related research concentrates on the computation offloading of general AI-based tasks, whereas both the edge server deployment and AI model training process are not explicitly designed for URTs. The URTs AI service characteristics such as model training demand, priority, and security are largely ignored. In this paper, we propose a novel collaborative EI service provision framework for URTs. Blockchain is used along with the EI server to construct a trusted computing infrastructure. To address the EI service credit crisis, a blockchain-based trust management mechanism including short-term reward incentives and long-term reputation evaluation is designed in the trusted computing infrastructure. An HRL-based collaborative training service optimization model is proposed to improve the learning efficiency and edge resource utilization rate in URTs. Specifically, the proposed two-stage collaborative optimization model jointly considers high-level service scheduling and low-level task offloading. In addition, we present an intelligent train control model based on the state-ofthe-art decision transformer (DT), with the training service as a case study to demonstrate the effectiveness of the proposed collaborative EI service provision. Extensive simulation results show that the proposed EI service provision framework can provide trusted, efficient, and high-quality AI training services, simultaneously improving URTs operational efficiency.

With the significant advantages of system complexity and operating costs, train autonomous circumambulate system (TACS) is gradually replacing the traditional communication-based train control system as the next-generation train operation control system development direction. As train operation and control become more decentralized and autonomous, real-time and accurate obstacle detection, apart from route-level protection, is quite desirable in TACS. Most of the existing researches about obstacle detection focus on detection algorithm optimization based on the once-deployed lifelong use principle, whereas model reoptimization based on the actual operating environment under unexpected situations and model sharing among multiusers are largely ignored. In this article, we design a novel obstacle detection system in TACS based on blockchain-empowered edge intelligence (EI). To make full use of the massive raw unannotated data collected online, we first propose an semisupervised learning-based TACS obstacle detection model. Considering the resource-hungry model training, we introduce EI into TACS and propose a multiagent reinforcement learning-based task offloading algorithm for secure and efficient computation offloading coordination. Furthermore, we propose a blockchain-based model sharing scheme to facilitate the multimodel parameter exchange and improve the obstacle detection accuracy. Extensive simulation results show that the designed obstacle detection system can effectively improve the TACS obstacle detection performance.

A novel approach based on virtual coupling of train control systems in subway lines has the potential to enhance transportation efficiency. Y-shaped lines have proven to be valuable in improving efficiency, and the rational application of the virtual coupling strategy on these lines can help to alleviate passenger flow pressure. However, train operations can be affected by uncertain disturbances that require real-time adjustments. Due to the complexity of train scheduling on Y-shaped lines, delays may be difficult to manage if they are scheduled according to a single timetable. In this paper, we propose a flexible adjustment of the planned schedule on Y-shaped lines, which considers the switching sequence of turnouts while ensuring safety. We present a mixed-integer programming (MIP) model that optimizes the total train delays, number of stranded passengers, and passenger waiting time. The simulation results demonstrate that our model effectively improves train punctuality while reducing stranded passengers and passenger waiting time.

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

Train autonomous circumambulate systems (TACS) are a new-generation train control systems. They are characterized by autonomous travel path planning, autonomous protection, and autonomous train operation adjustment. One crucial problem in TACS is real-time communication and computation of autonomous train control systems. Trains need to obtain the real-time state of all the other trains and derive real-time intelligent control commands in TACS. With high capacity and reliable 5G technologies, edge intelligence (EI) can perform complex computing tasks offloaded from trains with little delay. In this paper, we develop a collaborative train and edge computing framework for TACS to provide real-time communication and computation service for train control. To reduce the tracking deviations and ensure the train operation punctuality, ride comfort, and energy-saving ability, we adopt the model predictive control (MPC) algorithm to optimize the autonomous train control process. To cope with the limited onboard computing power, we propose a meta reinforcement learning (MRL) based collaborative computing method to solve the computation offloading problem. Compared with the existing RL-based offloading policy that requires sufficient data samples for training, MRL can rapidly adapt to different computation offloading environments, which is exceptionally suited for the urban rail transit system where different rail lines have different operating environments, and we do not have enough data to finish a regular reinforcement learning and training task. Experimental results illustrate that the proposed framework can provide TACS with reliable and real-time computing services. The train operational efficiency can be significantly improved with our proposed collaborative computing train control algorithm.

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

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

Due to the high integration of wireless communication and networking technologies, the communication-based train control (CBTC) systems are exposed to additional cyber-attack surfaces, allowing sophisticated attackers to combine cyber attack vectors with physical attack means to achieve malicious goals. Notably, the decentralized authentication features are missing in existing communication protocols which make the CBTC be easily compromised by data tampering attacks, and lead to serious operational accidents. With outstanding advantages in decentralized authentication, blockchain provides new effective solutions for decentralized identity authentication in CBTC. Consequently, it is critical to study the complex physical consequences of cyber breaches from a cross-layer defense perspective. In this paper, we propose a novel cross-layer defense method for cyber security in blockchain empowered CBTC against data tampering attacks. In the physical layer, the joint Kalman filter and$\chi ^{2} $detector is proposed for the train state estimation and detection. In the cyber layer, an asymmetric encryption-based secure communication protocol with identity authentication and the blockchain-based distributed key management system with the adaptive consensus mechanism are designed for data communication security. Considering the unavailable direct observation of the CBTC cyber security states, a partially observable Markov (POMDP) decision model is constructed to derive the optimal adaptive consensus strategies for balancing cyber security and efficiency. Extensive simulation results show that the proposed blockchain empowered CBTC cross-layer defense method can effectively improve the cyber security protection capability and minimize the impact of data tampering attacks on the train operation.

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

Train operation is highly influenced by the rail track state and the surrounding environment. An abnormal obstacle on the rail track will pose a severe threat to the safe operation of urban rail transit. The existing general obstacle detection approaches do not consider the specific urban rail environment and requirements. In this paper, we propose an edge intelligence (EI)-based obstacle intrusion detection system to detect accurate obstacle intrusion in real-time. A two-stage lightweight deep learning model is designed to detect obstacle intrusion and obtain the distance from the train to the obstacle. Edge computing (EC) and 5G are used to conduct the detection model and improve the real-time detection performance. A multi-agent reinforcement learning-based offloading and service migration model is formulated to optimize the edge computing resource. Experimental results show that the two-stage intrusion detection model with the reinforcement learning (RL)-based edge resource optimization model can achieve higher detection accuracy and real-time performance compared to traditional methods.

Communication-Based Train Control (CBTC) systems use wireless communication to confirm safe train operation for train-ground transmission. CBTC consists of four subsystems, and the Data Communication System (DCS) is one of the most important parts, which plays a vital role in train-ground transmission. Many devices are exposed to the environment in DCS, trains operation in bad weather easily leads to a series of hardware failures. When DCS occurs one mistake, it will threaten the efficiency of CBTC and even the safety of passengers. Since DCS occurs failures inevitably, it is necessary to analyze and identify the mistakes more quickly and accurately. This paper proposes a GAN-Bert based edge-to-edge model to identify DCS faults. Bidirectional Encoder Representations from Transformers (BERT) extracts text features from fault diagnosis with Masked Language Model(MLM) and Next Sentence Prediction(NSP). Meanwhile, Generative Adversarial Network (GAN) is used to perform DCS fault diagnosis. At the same time, edge-to-edge collaboration contributes to training a better model. We use raw DCS logs and compare prediction results between the GAN-Bert and Bert-only models. The simulation results represent that the GAN-Bert based model presents higher accuracy.

The communication-based train control (CBTC) system ensures the high efficiency and orderliness of trains and is widely used in urban rail transit networks. The adoption of wireless communication and network techniques makes the CBTC systems more vulnerable to cyber attacks. Identity authentication is an effective approach to improve system security. The existing identity authentication mechanisms in CBTC adopt a centralized key management system sensitive to single-point failures. To improve system security, in this article, we deploy a blockchain in CBTC systems. The client that runs the blockchain program not only acts as blockchain nodes to provide distributed key management for the CBTC system but they also work as a relay node to authenticate the communication between train control nodes in CBTC systems. Based on the blockchain-empowered distributed security scheme, the block producer selection and onboard blockchain client handoff decision problem are studied. With the objective to minimize the impact of the key updating process on CBTC system performance and keep the system security under a reasonable level, we formulate the block producer selection and onboard blockchain client handoff decision problem using the deep reinforcement learning approach. Extensive simulation results illustrate that the proposed blockchain-empowered security scheme can significantly improve the CBTC system security, and CBTC systems need to sacrifice part performance to ensure system security.

The appropriate design of a vehicular ad hoc network (VANET) has become a pivotal way to build an efficient smart transportation system, which enables various applications associated with traffic safety and highly-efficient transportation. VANETs are vulnerable to the threat of malicious nodes stemming from its dynamicity and infrastructure-less nature and causing performance degradation. Recently, software-defined networking (SDN) has provided a feasible way to manage VANETs dynamically. In this article, we propose a novel software-defined trust based VANET architecture (SD-TDQL) in which the centralized SDN controller is served as a learning agent to get the optimal communication link policy using a deep$Q$-learning approach. The trust of each vehicle and the reverse delivery ratio are considered in a joint optimization problem, which is modeled as a Markov decision process with state space, action space, and reward function. Specifically, we use the expected transmission count ($ETX$) as a metric to evaluate the quality of the communication link for the connected vehicles’ communication. Moreover, we design a trust model to avoid the bad influence of malicious vehicles. Simulation results prove that the proposed SD-TDQL framework enhances the link quality.

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

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

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

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

Mobile-edge computing (MEC) is a promising paradigm to improve the quality of computation experience of mobile devices because it allows mobile devices to offload computing tasks to MEC servers, benefiting from the powerful computing resources of MEC servers. However, the existing computation-offloading works have also some open issues: 1) security and privacy issues; 2) cooperative computation offloading; and 3) dynamic optimization. To address the security and privacy issues, we employ the blockchain technology that ensures the reliability and irreversibility of data in MEC systems. Meanwhile, we jointly design and optimize the performance of blockchain and MEC. In this article, we develop a cooperative computation offloading and resource allocation framework for blockchain-enabled MEC systems. In the framework, we design a multiobjective function to maximize the computation rate of MEC systems and the transaction throughput of blockchain systems by jointly optimizing offloading decision, power allocation, block size, and block interval. Due to the dynamic characteristics of the wireless fading channel and the processing queues at MEC servers, the joint optimization is formulated as a Markov decision process (MDP). To tackle the dynamics and complexity of the blockchain-enabled MEC system, we develop an asynchronous advantage actor–critic-based cooperation computation offloading and resource allocation algorithm to solve the MDP problem. In the algorithm, deep neural networks are optimized by utilizing asynchronous gradient descent and eliminating the correlation of data. The simulation results show that the proposed algorithm converges fast and achieves significant performance improvements over existing schemes in terms of total reward.

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

The application of blockchain to mobile edge computing (MEC) systems has attracted great interests. However, the design and optimization of blockchain and MEC in most existing works are done separately, which will result in sub-optimal performance. In this paper, we propose a joint optimization framework for blockchain-enabled MEC systems to achieve the optimal trade-off between the performance of the MEC system and the performance of the blockchain system. Specifically, both MEC and blockchain are considered as services in the framework, where energy consumption and delay/time to finality (DTF) are the performance metrics for the MEC system and the blockchain system, respectively. We formulate an optimization problem to achieve the optimal trade-off through jointly optimizing user association, data rate allocation, block producer scheduling, and computational resource allocation. To solve the problem, we decouple the optimization variables for efficient algorithm design. In addition, we develop an iterative algorithm for user association and data rate allocation and a bisection algorithm for computing resource allocation. Simulation results show the convergence of the proposed algorithms, and the proposed scheme can achieve the optimal trade-off between energy consumption and DTF.

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

EEG-based hyper-scanning refers to two or more subjects engaged in a task together or performing the same action together while neurophysiological signals are simultaneously recorded from them. This is one of the manners for investigating between-subject neural activities involved in social interactions. Emotion perception plays an important role in human social interactions. Interaction and emotional state influence each other. In this study, we aim to investigate how between-subject interaction modulates emotion perception based on event related potentials (ERPs), connectivity analysis and classification analysis. We found that there are distinct differences appearing between paired subjects who performed the task together, which are early ERP components (N250 and N400), late ERP components (P1500 and N1500), and the greater amplitude in N250 for the seconding responding subject compared to the first one. In the exploration of connectivity using phase locking value (PLV), we found that there are significant differences among different frequency bands for each subject under positive and negative stimuli and the significant difference of hyper-connectivity existed in the gamma frequency band between positive and negative stimulus trials. In the classification analysis, we compared the hyper-features for two individual subjects separately, the performance was improved when hyper-features of the PLV was employed compared to the features of power spectrum density.

Urban rail transit plays an increasingly important role in urbanization processes. Communications‐Based Train Control (CBTC) Systems, Passenger Information Systems (PIS), and Closed Circuit Television (CCTV) are key applications of urban rail transit to ensure its normal operation. In existing urban rail transit systems, different applications are deployed with independent train ground communication systems. When the train ground communication systems are built repeatedly, limited wireless spectrum will be wasted, and the maintenance work will also become complicated. In this paper, we design a network virtualization based integrated train ground communication system, in which all the applications in urban rail transit can share the same physical infrastructure. In order to better satisfy the Quality of Service (QoS) requirement of each application, this paper proposes a virtual resource allocation algorithm based on QoS guarantee, base station load balance, and application station fairness. Moreover, with the latest achievement of distributed convex optimization, we exploit a novel distributed optimization method based on alternating direction method of multipliers (ADMM) to solve the virtual resource allocation problem. Extensive simulation results indicate that the QoS of the designed integrated train ground communication system can be improved significantly using the proposed algorithm.

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

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

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

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

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

Data communication technology is one of the key subsystems in communication-based train control CBTC, which is an automated train control system for railways that ensures safe operation of rail vehicles using data communications. In CBTC systems, less service availability could cause train derailment, collision or even catastrophic loss of lives or assets. Therefore, the availability of data communication should be carefully considered in designing CBTC systems. In this paper, we propose two wireless local area network WLAN-based data communication systems with redundancy in CBTC systems. The availability is analyzed using continuous time Markov chain CTMC model. We also model the WLAN-based data communication system behavior with deterministic and stochastic Petri net DSPN. The DSPN solution is used to show the soundness of our proposed CTMC model. Numerical examples illustrate that the proposed systems with redundancy can significantly improve service availability in CBTC systems. Copyright © 2012 John Wiley & Sons, Ltd.

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

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

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

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

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

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

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

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

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

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

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

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

In metro Passenger Information Systems (PISs), frequent train handoffs can cause severe video distortion. In this paper, we take an integrated design approach to jointly optimize application layer parameters and handoff decisions to improve video transmission quality over PISs. We present a train-ground video communication network based on fountain codes and IEEE 802.11p for metro PISs. The handoff decision and application layer parameters adaptation problem is formulated as a stochastic semi-Markov Decision Process (SMDP). Minimizing the end-to-end total video distortion is the objective in our model. Simulation results show that the proposed SMDP based optimization algorithm can significantly improve the end-to-end video transmission quality in metro PISs.

Train-ground communication technology is one of the key technologies for communication-based train control (CBTC), which is an automated train control system for railways that ensures safe operation of rail vehicles using data communications. In CBTC systems, less service availability could cause train derailment, collision or even catastrophic loss of lives or assets. Therefore, the availability of train-ground communication should be carefully considered in designing CBTC systems. In this paper, we propose two WLAN-based train-ground communication schemes with redundancy to improve the availability in CBTC systems. The availability is analyzed using continuous time Markov chain (CTMC) model. Numerical examples illustrate that the proposed schemes with redundancy can significantly improve the availability of WLAN-based train-ground communication in CBTC systems.

Communication-based train control (CBTC) system is an automated control system for railways that ensures the safe operation of rail vehicles using wireless data communications. The train-ground communication system is one of the key subsystem of CBTC. CBTC systems have stringent requirements for wireless communication availability and latency. In this paper, we propose a seamless handoff scheme based on stream control transmission protocol (SCTP) and IEEE 802.11p to provide high link availability for train-ground communication systems in CBTC. Moreover, we propose a handoff decision policy to improve SCTP throughput and achieve seamless handoff in train-ground communications systems. Simulation results illustrate the effectiveness of the proposed scheme.

As an advanced train control system, communication-based train control (CBTC) system can improve the utilization of railway network infrastructure and enhance the level of safety and service offered to customers. In this paper, we propose a seamless handoff scheme for CBTC systems based on Stream Control Transmission Protocol (SCTP) and IEEE 802.11p. The focus of our work is in the handoff decision phase, with the objectives of maximizing the SCTP throughput and minimizing the handoff latency. The handoff decision is modeled as a semi-Markov Decision Process (SMDP). Simulation results show that the proposed SMDP based handoff decision algorithm can significantly improve the SCTP throughput and decrease handoff delay.