With the continuous development of underground space, the vigorous development of underground rail transport has become an effective way to relieve the pressure of urban traffic. However, ground settlement caused by tunnels can lead to cracks, settlements, and collapses of nearby buildings, resulting in serious economic losses. To solve the problems of poor generalization performance of risk warning models and uneven allocation of maintenance resources in PHM for ground settlement in existing studies, an Internet of Things (IoT)-enabled risk warning and maintenance strategy optimization method is proposed in this paper. In risk warning, firstly, a weight optimization method with the decision objectives of variance maximization, correlation minimization, and estimation error minimization is introduced to find the optimal weights of the base learners in ensemble learning prediction. Secondly, a one-dimensional convolutional neural network-bidirectional long and short-term memory network (1D CNN-BiLSTM) is used to make further predictions on the prediction residuals. In maintenance strategy optimization, threshold-based optimization and cost-based risk-importance maintenance strategies are proposed based on the risk warning results of ground settlement. To test the enhanced effectiveness of the proposed method, a set of comprehensive simulations is carried out in Ningbo city rail transit as an example. The results show that the proposed risk warning method has smaller MAE, MAPE, and RMSE compared to other baseline methods. In addition, the proposed maintenance strategy reduces 12.5%, 16.3%, and 85.7% in terms of cost compared to the baseline method. Overall, the simulation results confirm the advantages of the proposed framework for IoT-enabled risk warning and maintenance strategy optimization in PHM.

Advanced Internet of Things (IoT) technology has a profound impact on improving the intelligence level of intelligent transportation systems (ITS) and promoting the sustainable development of urban transportation. However, how to use IoT to process traffic flow and make ITS develop towards automation and global control is still a challenge. Against this backdrop, a prospective traffic controlling model is proposed for ITS based on IoT to enhance the awareness of roads and the responsiveness of transportation system. When traffic congestion events occur, ITS can provide the optimal control strategy of vehicle-to-everything supported vehicles (V2X-supported vehicles) from a macro perspective to control the traffic flow globally and improve traffic efficiency. Specially, the optimal control strategies consider the potential congested road segments caused by congestion propagation. Meanwhile, this paper explores the impact of route choice behavior of V2X-supported vehicles on system performance. The simulation results show the optimal control strategies can alleviate congestion effectively and improve transportation system performance significantly by controlling vehicles.

The all-leveldminimal path vectors (d-MPs) are an important input for obtaining the performance metric of multistate networks, which is the basis of resilience evaluation and analysis. During the resilience process, network configurations may change, leading to changes in thed-MPs. However, when the network configurations are changed, the repeated searching ofd-MPs is time consuming because it is an NP-hard problem. Thus, a framework is proposed to obtain thed-MPs in different situations during the resilience process. First, twod-MP updating algorithms are used instead of repeated searching of thed-MPs when the maximum states of the components are changed. Taking the originald-MPs as the input, the proposed method updates thed-MPs by deleting the unqualifiedd-MPs or adding the qualifiedd-MPs during the resilience process. Second, ad-MP searching algorithm is employed to search the newd-MPs derived from the new components that are brought in during the recovery phase. Computational experiments show that the proposed updating methods are more efficient than the existingd-MP searching algorithm. Furthermore, the proposed newd-MP searching algorithm outperforms the repeated searching ofd-MPs. In addition, the proposed method can be applied to network design and optimal phase settings, where multiple attempts at network configuration adjustments may be necessary.

Minimal paths (MPs) play an important role in system reliability analysis, such as binary network reliability evaluation and multistate network reliability evaluation. Searching all MPs is an NP-hard problem. The direct-search-based algorithm implements the depth-first search (DFS) mechanism to find all MPs, which is a simple and efficient method. The current efficient DFS algorithm is based on backtracking and linked list by nodes. In this article, we propose several improvements to speed up the current algorithm. First, we find that the relative positional information of the network can be used to avoid visiting some unnecessary nodes. Thus, we improve the algorithm by incorporating the point in the polygon algorithm to avoid visiting nodes surrounded by other nodes in a network. Second, we further improve the current backtracking condition in which the time complexity of implementing each backtrack is reduced. In addition, we find that different search branches from a similar node are independent and can be executed in parallel. A parallel search mechanism is proposed and incorporated to speed up the searching process. Through computational experiments on several benchmark networks and real transportation networks, we demonstrate that the improved algorithm is more efficient than the existing algorithm. The proposed algorithm becomes more advantageous as the size of the network grows.

This article presents an improved state space decomposition (SSD) method for reliability evaluation of multistate networks. We observe that the decomposition process of the existing SSD is a sequential decomposition process. However, the SSD has the basis of parallel operations, as the decomposition process from each unspecified state is independent of each other. In addition, the existing SSD methods applied heuristic to select a proper minimal path vector (d-MP). When there is a tie during the process, the tie is broken arbitrarily. However, we find that different d-MPs with same function value of the current heuristic affect the efficiency of the decomposition procedure. Based on these observations, an improved SSD method is developed for reliability evaluation of multistate networks. First, a parallel mechanism is proposed and incorporated into the SSD algorithm. Second, several improved heuristics, namely R1, R2, R3, and R4, are developed to select a proper d-MP. The experimental results show that the proposed SSD method can improve the efficiency for reliability evaluation of multistate networks, which provides the reliability engineers and facility managers a more powerful tool for the design and maintenance of more complex multistate networks.

The reliability evaluation of multistate networks is primarily using minimal path (cut) vectors, namely d-MPs (d-MCs), which are the lower (upper) boundary vectors that satisfy the system demand d. The generation of all d-MPs (d-MCs) is an NP-hard problem. Thus, it is important to develop an efficient algorithm to search for d-MPs (d-MCs). Existing algorithms searching for d-MPs based on MPs all generate duplicate d-MP candidates, and extra steps are required to detect and remove those duplicates. In this article, we propose an improved algorithm to search for d-MPs for all d levels without generating duplicate d-MP candidates for two-terminal multistate networks. First, we discover and prove the mechanism of generating duplicate d-MP candidates based on MPs. Second, a novel method is proposed to prevent generating duplicate d-MP candidates. Third, an improved algorithm is developed by combining the proposed method to search for d-MPs without generating duplicate d-MP candidates for all d levels. Through computational experiments, it is found that the proposed algorithm is more efficient than existing algorithms for finding all d-MPs for all possible d values.

This paper develops ordering heuristics to improve the efficiency of reliability evaluation for multistate two-terminal networks given all minimal path vectors ( d-MPs for short). In the existing methods, all d-MPs are treated equally. However, we find that the importance of each d-MP is different, and different orderings affect the efficiency of reliability evaluation. Based on the above observations, we introduce the length definitions for d-MPs in a multistate two-terminal network, and develop four ordering heuristics, called O1, O2, O3, and O4, to improve the efficiency of the Recursive Sum of Disjoint Products (RSDP) method for evaluating network reliability. The results show that the proposed ordering heuristics can significantly improve the reliability evaluation efficiency, and O1 performs the best among the four methods. In addition, an ordering heuristic is developed for the reliability evaluation of multistate two-terminal networks given all minimal cut vectors ( d-MCs).