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• A robustness index considering cascading failures caused by failed units is proposed. • Robustness improvement by structure adjustment is studied for fixed characteristics. • TS with directed and undirected edge swap operators is designed to solve the problem. • A case study of structure adjustment for improving robustness in the HST is performed. • Robustness of the optimized HST is greatly enhanced and hardly affected by failures. To enhance the ability of a high-speed train (HST) to provide services under various adverse conditions, this paper studies robustness improvement of the HST by adjusting its structure. We model the HST as an interdependent machine-electricity-communication network (IMECN) composed of a machine network (MN), an electricity network (EN) and a communication network (CN), and propose a robustness metric of the IMECN subject to node failures considering failure propagation. Then, a robustness optimization model is constructed for structure adjustment. A Tabu search algorithm with directed and undirected edge exchange operators is designed to solve this problem. A case study on a practical HST is used to verify the feasibility and effectiveness of the proposed method. The results show that robustness of the IMECN is significantly improved by structure adjustment. Furthermore, most nodes have little effect on robustness, and impact of failures on any node is minimized after optimization. In addition, in terms of topology, nodes with low and high degrees in the MN and CN reconnect with those with a similar degree, their clustering coefficients become larger, and closeness of all nodes in the subnetworks increases. Finally, adjusting the structures leads to a slight difference of loads in the EN and CN. [ABSTRACT FROM AUTHOR]

• A high-speed train is modeled as an interdependent network. • A robustness metric is proposed based on network theory and practical factors. • Assessment and optimization models of resilience are constructed. • The tabu search algorithm is developed to solve the optimization problem. • The optimal repair strategy of failed units in the high-speed train is analyzed. High-speed trains (HSTs) are used to transport passengers to destinations efficiently and safely. However, when HST units fail, strategies for repairing them are not available. Hence, to optimize repair strategies, an interdependent network is used to model an HST, and a metric of resilience based on network theory is introduced. Based on practical factors, an interdependent machine–electricity–communication network (IMECN) and related cascading failure models are developed. Comprehensive robustness metrics for the IMECN and nodes are proposed considering the topology of networks and functional features of nodes. Accordingly, a resilience optimization model of the IMECN subject to disturbances is formulated and solved using a tabu search (TS) algorithm. Finally, a real-world HST is considered to analyze the optimal repair strategy for different numbers of failed nodes. Analysis results show that the recovery process under the optimal repair strategy has at most three stages. When a few nodes and less than half of the nodes fail, the highest and lowest resilience levels of the IMECN are achieved, respectively. Moreover, the characteristics of the preferentially repaired node in terms of importance, vulnerability, network type, and interdependency are analyzed. Results indicate that the optimal repair strategy is not necessarily determined by topological metrics. [ABSTRACT FROM AUTHOR]

• A high-speed train is modeled based on network theory. • A multi-objective optimization model for identifying critical elements is proposed. • A novel importance metric of critical elements is designed. • The proposed model is verified by a practical high-speed train. • Critical nodes, intra-links and inter-links for a high-speed train are identified. This paper proposes a multi-objective optimization-based approach to identify critical elements, including units and interactions within and between systems, in a high-speed train (HST). In the framework, network theory is used to model the HST as an interdependent machine-electricity-communication network (IMECN) composed of a machine network (MN), an electricity network (EN) and a communication network (CN). Cascading failure models for the subnetworks and IMECN, and topological and functional metrics for robustness are developed. We then formulate a multi-objective optimization model for maximizing the impact of the failure of critical elements on the topological and functional robustness of the IMECN and minimizing their number. We use NSGA-II to solve the optimization problem. Considering a practical HST as a case study, we apply the multi-objective optimization framework to search the groups of critical nodes, intra-links and inter-links. The results show that critical nodes, intra-links and inter-links of the IMECN are within the MN and CN. In particular, end nodes of the critical intra-links and inter-links may also be critical, and the critical elements of subnetworks tend to also be critical for the IMECN. In addition, we find that the critical nodes, intra-links and inter-links are not related to their topological importance. [ABSTRACT FROM AUTHOR]

• The M-LSCSFN for an HSEMU system describes system structure and functions. • The layer reliability is calculated based on MPs using specific operators. • The M-LSCSFN reliability is estimated with the layer reliability. • The HSEMU system reliability is evaluated in terms of the M-LSCSFN reliability. • A practical case is shown to demonstrate the proposed method. This paper proposes a new reliability assessment method for a high-speed electric multiple unit (HSEMU) system based on the expanded multi-state flow network. An HSEMU system is abstracted as a multi-layer multi-source multi-commodity multi-state flow network (M-LSCSFN), where minimum maintenance units (MMUs) and three types of connections form nodes and edges in different layers, and functional characteristics are regarded as network parameters. In the M-LSCSFN, multi layers describe different types of connections and their relationships; multi-source and multi-commodity reflect system features; whereas multi-state flow indicates the dynamic realization of system functions. Considering that an HSEMU system can be divided into multiple independent holistic functions, each holistic function has a corresponding sub-M-LSCSFN, and each layer of this sub-M-LSCSFN is a multi-source multi-commodity multi-state flow network (M-SCSFN). In order to assess system reliability, layer reliability (i.e. , the M-SCSFN reliability) is first defined as the probability that the amounts of each commodity transmitted successfully from source nodes to sink nodes will not be less than the required demands. A corresponding algorithm is proposed to calculate layer reliability in terms of minimal paths (MPs) using the quick inclusion-exclusion technique (QIE). The sub-M-LSCSFN reliability is subsequently calculated based on the layer reliability and the conditional relationships among different layers. Based on these, the HSEMU system reliability is defined as the ability of a system to complete specified multiple functions under stated conditions for a specified period of time, and can be evaluated using the reliability of sub-M-LSCSFNs via the proposed operator. Finally, a case study of the "Fuxing" HSEMU system is analyzed to demonstrate the applicability of the proposed method and algorithm the effectiveness of the proposed method is demonstrated in an HSEMU system reliability assessment case study. [ABSTRACT FROM AUTHOR]

• The high-speed railway network is modeled as a three-layer network. • Network-specific characteristics are considered in performance evaluation instead of pure complex network analysis. • Railway stations are prioritized and compared within different layers of the network. • The current physical rail network in China is generally sufficient to satisfy the requirements of its transportation accessibility. • Management implications are provided to facilitate the rail network planning, transportation organization and operations. High-speed railways, as dominant transportation patterns, constitute a large-scale complex network. Network performance is critical for high-level transportation services in transportation management. However, existing studies generally adopt pure topological indicators to evaluate performance in a single network and without considering the specific characteristics of rail transportation. This paper modeled the high-speed railway as a three-layer network including topological, functional, and service layers and assessed the integrated network performance from the view of transportation accessibility. In addition, the rail stations were prioritized and their performances in different layers were compared to analyze whether the total integrated performance was restricted by its connection abilities, functional capacities, and transportation service level. Finally, a case study of the high-speed rail network in China and comparisons with other traditional evaluation methods were conducted to verify the feasibility and superiority of the model. The findings of this study enable to provide management implications for rail transportation planning, organization, and operations. [ABSTRACT FROM AUTHOR]

• The main accident causes at the macro level are not applicable to specific scenes. • The distribution of accident causes concentrates with the refinement of the scene. • A data-driven Bayesian network construction approach is proposed. • The significant causes in various specific scenes can be accurately identified. As the freight railway system is a typical complex system, freight railway accidents have various and complex accident scenes. A Data-Driven Bayesian Network (DDBN) with random variables representing scene elements, accident causes, and accident consequences as nodes, and conditional dependencies between nodes as edges is proposed to identify the most significant accident causes in various and complex specific accident scenes. First, an unsupervised-supervised method is designed to define the states of nodes in the DDBN, considering the characteristics of the data involving both continuous and discrete states. Second, a greedy algorithm is proposed to mine the causal sequence between nodes, and the direction of edges in DDBN is established accordingly. Then, an NB-K2-MLE approach is proposed to generate the structure and parameters of the DDBN from data. Finally, a risk calculation function based on DDBN is proposed to calculate the risk of various accident causes in specific scenes. In empirical analysis based on real accident data, the inference accuracy of DDBN reached 87.92 %, with precision and recall exceeding 70 %. More importantly, the research results indicate that the distribution of accident causes will concentrate with the refinement and specific of the scene, and the main accident causes at the macro level cannot be fully applicable to accident prevention in specific scenes. The DDBN constructed in this study can provide data support for the determination of the significant accident causes and the development of targeted accident prevention strategies in various and complex specific scenes. [ABSTRACT FROM AUTHOR]