An urban rail transit (URT) system is an important component of an urban infrastructure system; however, it is vulnerable to disturbances, such as natural disasters and terrorist attacks. Constructing a highly resilient URT network has practical significance for enhancing its capability to respond to disturbances. In this paper, models are developed to optimize a URT network’s structure with regard to resilience and to enhance the resilience of a disrupted URT network. A bi-level programming model that aims to maximize a URT network’s global accessibility and global efficiency is formulated to optimize the structure of the network. A novel repair strategy, called the simulation repair strategy, is proposed to enhance the resilience of a disrupted URT network by optimizing the repair sequence of failed stations. The models are utilized to enhance the resilience of the Chengdu subway network. The result indicates that the bi-level programming model guides the construction of new links to optimize the structure of the Chengdu subway network. Deliberate attacks are more harmful to the Chengdu subway network than random attacks. The network’s operators need to pay attention to the operations of critical stations (e.g., Chunxi Road station and Tianfu Square station) to prevent disturbances from exerting considerable negative effects on the network’s normal operations. The simulation repair strategy exhibits higher repair efficiency than the conventional repair strategy, and it effectively enhances the resilience of the disrupted Chengdu subway network.

Operational disturbances within 30 min, namely, short-term operational disturbances (STODs), occur frequently during the daily operation of an urban rail transit (URT) system. Therefore, there is an urgent need to assess a network’s ability to respond to STODs to improve its operational level. The resilience of a URT network jointly considering turn-back operations, the occurrence time of STODs, and passenger travel experience is addressed. By considering passenger travel alternatives under STODs, we assessed the resilience of a network by utilizing the ratio of the average loss of a time-dependent performance indicator, which is referred to as the operational service level indicator in this paper. A simulation-based resilience assessment flowchart is also proposed. Numerical experiments conducted on the Chengdu subway network indicate that this network is more resilient to successive station failure than to simultaneous failure. Guaranteeing the normal operation of transfer stations is vital for enhancing the resilience of a network. The metric proposed in this paper is more practical for assessing resilience than the two commonly used metrics (performance loss and disturbance duration). Additionally, the experimental results highlight that turn-back operations at the stations have a significant impact on the resilience of a URT network. The sensitivity analysis of the parameters indicates that ensuring the normal operation of links connected by critical stations, preventing large-scale station failure, improving passenger tolerance to disturbance durations, and restoring the connectivity of disrupted links rapidly have practical significance for enhancing the resilience of a network.

Under the background of urbanization and the rapid development of urban rail transit (URT), serious attention has been focused on URT network reliability in recent years. In this work, in order to measure network reliability, three indicators are constructed based on passengers’ tolerable travel paths, passenger travel efficiency and passenger travel realization on a URT network. The passenger tolerability coefficient, which is the ratio of passengers’ tolerable travel time to the shortest possible travel time, is proposed and added to the indicators. It reflects passengers’ behavior with respect to choice of travel paths. The ratio of affected passenger volume (RAPV) is proposed to identify important stations. Finally, the connectivity reliability of Wuhan’s subway network is analyzed by simulating attacks on stations. The results show that the degree centrality, betweenness centrality and RAPV indicators of stations can effectively identify the important stations that have a significant impact on the connectivity reliability of the network. In particular, the RAPV indicator effectively identifies stations that have the greatest influence on passenger travel realization. The connectivity reliability of Wuhan’s subway network is sensitive to passenger tolerability coefficient, and reliability is greater during non-peak hours than during peak hours. In addition, the stations that are important to the connectivity reliability of the Wuhan subway have two features, i.e., they are located at the center of the city, and they are important for connecting subgraphs of the network.

Train platforming is critical for ensuring the safety and efficiency of train operations within the stations, especially when unexpected train delays occur. This paper studies the problem of reoptimization of train platforming in case of train delays, where the train station is modeled using the discretization of the platform track time-space resources. To solve the reoptimization problem, we propose a mixed integer linear programming (MILP) model, which minimizes the weighted sum of total train delays and the platform track assignment costs, subject to constraints defined by operational requirements. Moreover, we design an efficient heuristic algorithm to solve the MILP model such that it can speed up the reoptimization process with good solution precision. Furthermore, a real-world case is taken as an example to show the efficiency and effectiveness of the proposed model and algorithm. The computational results show that the MILP model established in this paper can describe the reoptimization of train platforming accurately, and it can be solved quickly by the proposed heuristic algorithm. In addition, the model and algorithm developed in this paper can provide an effective computer-aided decision-making tool for the train dispatchers in case of train delays.

Link capacity reductions, which occur often, degrade the service quality and performance of urban rail transit (URT) networks. To measure the reliability of a URT network when link capacity reductions occur in a given time period, the passengers’ generalized travel cost (GTC) is computed and passengers are divided into three categories. The GTC considers here the crowding in trains, seat availability, and perceived travel time. Passengers whose relative increase in GTC on a URT is below or above a preset threshold belong to category I or II, respectively, while passengers who cannot travel on the URT due to insufficient capacities on their paths belong to category III. Passenger trips in categories I are acceptable since their GTC increases only slightly with link capacity reductions. The fraction of acceptable trip (FAT) and total GTC increase ratio (TGCR) in a given time period are defined here as the network’s reliability and unreliability metrics, respectively. The ratio of affected passenger trip (RAPT) is proposed to identify each line’s most critical links. The reliability and unreliability metrics of Wuhan’s URT network during evening peak hours are computed when the capacities of the most critical link or multiple most critical links are reduced. The results show that the proposed RAPT indicator is effective in identifying the most critical links that greatly affect the reliability and performance of a URT network. For capacity reductions on a line’s most critical link, the proposed method can determine the capacity reduction ratio corresponding to network’s high FAT and low TGCR as well as the priorities of lines needing emergency measures to maintain high network reliability and performance. For capacity reductions on critical links of multiple lines, the proposed method can identify the number of reduction links and the capacity reduction ratio that the network can withstand while maintaining its reliability and performance above a specified level.

The resilience of an urban rail transit (URT) network when faced with disruptions is affected by the locations of stations equipped with turn-back (TB) tracks. However, limited studies have enhanced the resilience of a URT network by setting new TB tracks. The present work addresses this gap by proposing and solving a scenario model for improving the operation of a URT network under normal conditions and disruptions by considering uncertain disruptions. A solution algorithm combined with the non-dominated sorting genetic algorithm-II is proposed to solve the model. Numerical experiments conducted on the Chengdu subway system indicate that the resilience of a URT network is significantly affected by TB operations provided at stations equipped with TB tracks. Compared with a network without new TB tracks, the matching degree between passenger flow spatial distribution and TB convenience, and the network’s overall resilience metric (NORM) are improved by 12.05% and 0.58%, respectively, when five new TB tracks are installed. The solution effectiveness of the model is related to the number of new TB tracks, and the NORM decreases by an average of [ ] after adding new TB tracks to a station.