The emergence of virtual coupling (VC) technology has the potential to substantially enhance the capacity of existing railway infrastructure. However, the complexity of line conditions introduces considerable disturbances to convoy operations, negatively affecting both energy consumption and operational efficiency. To address this issue, this study proposes a double loop trajectory optimization method for high-speed trains in a convoy, incorporating line condition disturbances and train dynamic characteristics. The proposed approach begins with an analysis of operating sequences under varying line conditions, leading to the development of a multi-resolution sequence optimization model for the leading train (LT). Subsequently, a train-following model is introduced to define the acceleration adjustment rules for the following train (FT) in different operating state. On this basis, a cooperative optimization framework is established to integrate the above models and define the detailed optimization procedure. Finally, a double loop seeker optimization algorithm is developed to obtain the optimal solution for the proposed model. Numerical experiments using field data from the Wuhan-Guangzhou high-speed railway line demonstrate the effectiveness of the proposed method. The experimental result proves that our method can generate trajectories with superior energy-saving and time-efficient performance while consistently maintaining safe and stable separation between virtually coupled trains.

The Virtual coupling is proposed as a blocking mode to address the increasing demand for railway transport capacity, which takes operation efficiency further improve by separating trains with a relative braking distance. Nevertheless, an insufficient protection for complete avoidance safety risks in time with limited and fluctuate spacing separation between consecutive trains is introduced. A time-space occupancy band model is established to hold a safety protection in time-space dimension and assess the transport capacity for train operation under virtual coupling. In addition, a train trajectory planning method aimed at improvement of transport capacity, is proposed as a two-step program consisting of train followed operation trajectory planning based on Markov Decision Process and a trajectory multi-objective optimization for train convoy. In order to meet the requirement of the train trajectory dynamic adjustment under disturbance, an approach based on trajectory strategy set is designed by two stages to consider objectives of safety and punctuality. Based on the field data from the Wuhan-Guangzhou high-speed railway line, numerical experiments are conducted to validate the applicability of the proposed model and method. A comparative analysis of the track resource occupancy for several application condition under virtual coupling, and signaling systems is provided. The results indicate that the effective performance of proposed method in terms of trains separation and track resource occupancy, and show that virtual coupling is a more satisfactory blocking mode that could provide a higher track resource utilization while operation conditions are taken into account.

Virtual coupling is proposed as an innovative solution to meet the growing transport demand and to further improve the service quality of railways. Nevertheless, obtaining the optimal driving strategy that enhances its transport capacity and energy efficiency remains a challenging task. In order to achieve these objectives, this paper presents a novel convoy optimization method that optimizes the recommended trajectories for virtually coupled trains. A resilience adjustment model is firstly proposed to evaluate the coupling process and generate candidate trajectories according to the adjustment rules. In addition, the convoy optimization problem is formulated as a two-stage programming model consisting of a multi-objective programming stage and a least-cost goal programming stage, which determine the optimal trajectories for trains. Taking into account the requirements of practical applications, the solution method is finally designed to solve the proposed model and achieve dynamic updates of the recommended trajectories throughout train operations. Based on the field data from the Wuhan-Guangzhou high-speed railway line, numerical experiments are conducted to validate the effectiveness of the proposed method. The experimental results indicate that the proposed method shows the best performance in terms of infrastructure utilization and energy consumption, and the spacing between virtually coupled trains is well maintained regardless of ideal or disturbing conditions.

Since 2011, the private ride-hailing companies Didi (2019), Uber (2019) and Lyft (2021) have expanded into more and more cities. These ride-hailing services (RHS) bring convenience to our life; however, at the same time they, also raise security concerns for users. For example, several recent news items show that a considerable number of registered drivers whose licenses have been revoked are still taking RHS orders on the respective platforms; this phenomenon directly leads to insecurity on part of its users and the bad reputation of the ride-hailing service provider (SP). The traditional solution to solve this problem is to periodically check the validity of the drivers’ licenses; however, it is a considerably time-consuming and costly process since the SPs have to manually interact with the governing authorities. Therefore, in this paper, we have presented an auditable self-sovereign identity system (named AudiSSI), which provides an efficient approach for the SPs to manage their registered drivers’ qualifications in a decentralized and automatic manner. Further, using smart contract technology, we propose a safety guarantee insurance in the form of an auditing contract to enable the RHS rider to check their driver's qualifications before the trip starts and get incentives once they detect a disqualified driver. We designed an incentive mechanism and have provided a game theoretical analysis. Finally, we implemented a prototype of AudiSSI and deployed it on Hyperledger Indy and Fabric to show that self-sovereign identity system for RHS driver with qualification auditing is efficient and technically feasible.

Disaster areas involving floods and earthquakes are commonly large, with the rescue time being quite tight, suggesting multi-Unmanned Aerial Vehicles (UAV) exploration rather than employing a single UAV. For such scenarios, current UAV exploration is modeled as a Coverage Path Planning (CPP) problem to achieve full area coverage in the presence of obstacles. However, the UAV's endurance capability is limited, and the rescue time is constrained, prohibiting even multiple UAVs from completing disaster area coverage on time. Therefore, this paper defines a multi-Agent Endurance-limited CPP (MAEl-CPP) problem that is based on an a priori known heatmap of the disaster area, which affords to explore the most valuable areas under UAV limited energy constraints. Furthermore, we propose a path planning algorithm for the MAEl-CPP problem by ranking the possible disaster areas according to their importance through satellite or remote sensing aerial images and completing path planning according to this ranking. Experimental results demonstrate that the search efficiency of the proposed algorithm is 4.2 times that of the existing algorithm.

The application of blockchain to Vehicular Edge Computing (VEC) has attracted significant interests. As the Internet of Things plays an essential and fundamental role for data collecting, data analyzing, and data management in VEC, it is vital to guarantee the security of the data. However, the resource-constraint nature of edge node makes it challenging to meet the needs to maintain long life-cycle IoT data since vast volumes of IoT data quickly increase. In this paper, we propose Acce-chain, a storage-elastic blockchain based on different storage capacities at the edge. Acce-chain supports re-write operation to re-write the historical block with a newly generated block without breaking the hash links between the blocks. As a result, Acce-chain ensures that the hot data can be efficiently accessed at the edge without incurring much communication costs or increasing the total size of the chain. To guarantee the security of the re-write process, we propose a new cryptographic primitive named Dynamic Threshold Trapdoor Chameleon Hash (DTTCH). To guarantee the verifiability of query operation, we design a novel storage structure namedHybridStoreto ensure the verifiable query for on-chain/off-chain IoT data. As a result, Acce-chain achieves both authorized re-write and verifiable query simultaneously. We provide security analysis for the DTTCH scheme and the IoT data query algorithms. We evaluate Acce-chain through experiments and the results show that the performance of the re-write operation is feasible in real-world VEC settings, and the query efficiency can achieve up to several magnitudes better than which of the baseline. The results also demonstrate that Acce-chain can provide high service quality for the latency-sensitive VEC systems.

Resilience adjustment refers to the generation of a control strategy by evaluating the interaction between related factors. Tracking intervals of the high-speed train platoon change dynamically, which directly influences the operation safety and efficiency, and constrains the train operation trajectories. In China, the tracking interval is getting shorter. To ensure safety and improve efficiency, we research a dynamic interval resilience adjustment strategy based on the moving block system. Firstly, the optimal offline operation strategy is obtained by solving the multi-objective optimization model with the improved gravitational search algorithm (I-GSA). The resilience adjustment mechanism is developed to evaluate the tracking interval and choose the appropriate driving strategy to adjust operation states based on the resilience tracking interval model. Then, we study the relation between operation strategy and departure interval, and a seeker optimization algorithm (SOA) is used to obtain the optimal departure intervals and driving strategies. Simulations are conducted based on the sections between Chibi North station and Changsha South station in Wuhan-Guangzhou high-speed railway. The results indicate that the total operation time decreased by 191s and the operation safety can be ensured at any time.

As the most successful application of the sharing economy, ride-hailing service is popular worldwide and serves millions of users per day worldwide. Ride-hailing service providers (SPs) usually collect users’ personal data to improve their services via big data technologies. However, SPs may also use the collected user data to apply personalized prices to different users, which raises price fairness concerns. In this paper, we propose a smart price auditing system named Spas. Spas allows a user to purchaseFair Price Insurancein the form ofPrice Auditing Contract, then the price of ride-hailing service (RHS) order will be audited automatically once completed. According to the auditing result, the contract punishes misbehaving SPs and also compensates affected users automatically. By replacing an untrustworthy centralized auditor with carefully designed smart contracts, we construct a decentralized price auditing system which is trustworthy and transparent. We demonstrate a theoretical model for practical payment flows based on real RHS user data and we implement Spas in Hyperledger Fabric to show that decentralizing and automating price auditing for RHS with financial incentives is technically feasible.