Urban rail transit (URT) is vulnerable to natural disasters and social emergencies including fire, storm and epidemic (such as COVID-19), and real-time origin-destination (OD) flow prediction provides URT operators with important information to ensure the safety of URT system. However, hindered by the high dimensionality of OD flow and the lack of supportive information reflecting the real-time passenger flow changes, study in this area is at the beginning stage. A novel model consisting of two stages is proposed for OD flow prediction. The first stage predicts the inflows of all stations by Long Short-Term Memory (LSTM) in real time, where the dimension is reduced compared with predicting OD flows directly. In the second stage, the notion of separation rate, namely, the proportion of inbound passengers bounding for another station, is estimated. Finally, The OD flow is predicted by multiplying the inflow and separation rate. Experiments based on Hangzhou Metro dataset show the proposed model outperforms the contrast model in weighted mean average error (WMAE) and weighted mean square error (WMSE). Results also suggest that the proposed prediction model performs better on weekdays than on weekends, and with greater accuracy on larger OD flows.

The artificial cooperative control methods are used to fully utilize the train capacity and reduce the passenger delays by coordinating the number of entering and boarding passengers at each station, but it ignores the potential risk associated with the aggregation attributes of passengers in the station under emergency and the unfairness caused by the imbalance of travel opportunities for different origin–destination (OD) passengers. A new parallel and cooperative control model under comprehensive emergency situation is constructed from the perspective of guaranteeing travel safety, improving travel fairness, and increasing travel efficiency to generate passenger flow control strategies. The model aims to maximize passenger turnovers, minimize passenger delays and reduce the difference in the ratio of stranded passengers at stations. The complex multi-objective integer programming problem involving nonlinear equation constraints is difficult to solve numerically. A dynamic programming model taking train sequence as stage variable is then constructed as well as an NSGA-II algorithm with multiple improved strategies (MIS-NSGA-II) is proposed to solve the model. Numerical experiments on a line of Beijing Rail Transit show that the MIS-NSGA-II outperforms the traditional NSGA-II, and that increasing the overload coefficient of the train and the tolerance coefficient of each area in the station can further alleviate passenger congestion and promote the operation efficiency. Compared with other control strategies mentioned in this study, the proposed control model from a comprehensively perspective, which can ensure the travel safety and promote the travel efficiency while forcefully decongesting the bottleneck areas and improving the fairness of passenger travel.

Traffic infrastructure safety is a core topic in traffic construction and development. As the impact of global climate change becomes more and more significant, extreme weather brings more and more safety issues to the normal operation of subway systems. Therefore, it is an urgent issue in the construction of subway systems to fully prepare for extreme weather and improve system resilience under external disturbances. The resilience of a complex system generally refers to its ability to adapt to external disturbances and return to a functional state. As one of several key infrastructure systems in large cities, a subway system needs to be highly resilient to cope with various risks, and it needs to recover quickly under uncertain weather conditions and other external damage events. In order to achieve the goal of conducting a real-time resilience assessment of a subway system, this study adopts the Bayesian network and the traditional failure mode and effect analysis (FMEA) method to realize resilience assessment with multiple performance indicators. Combined with the risk matrix method from FMEA, multiple important indicators of a subway system under the influence of extreme weather are obtained. These important indicators are integrated into the resilience assessment of the subway system within a Bayesian method. In this paper, the feasibility and applicability of the proposed method are verified by taking the Changping Line of the Beijing subway under extreme rainfall weather (>10 mm) as a case.

Emergency-related knowledge from urban rail transit (URT) safety event case base is crucial for realizing intelligent emergency response in URT. However, safety event case records are non-standard and unstructured, leading to challenges in data mining, information induction, and knowledge organization. To obtain emergency-related knowledge effectively from URT safety event cases, this paper proposes a knowledge acquisition model incorporating label semantics and provides a knowledge acquisition degree evaluation method. In this study, the knowledge acquisition task is formulated as a machine reading comprehension (MRC) task. This formulation can take full advantage of the rich semantic information of labels, which can compensate for the drawbacks of the traditional sequence labeling model. The experiments conducted on the URT safety event case base demonstrate the effectiveness of the proposed methods. The knowledge acquisition model performs well on different labels and achieves a performance boost over baseline models. Note to Practitioners —Urban rail transit (URT) safety events are caused by various factors, such as natural disasters like earthquakes and floods, equipment failures, and terrorist attacks. et al. These events may disrupt the normal operation of URT, pose a great risk to personal safety, and result in property losses. URT safety event cases encompass essential emergency-related experience and knowledge, which serve as valuable references for managing new emergencies and mitigating potential losses. However, the record format of URT safety event cases is non-standard, with the majority stored as fragmented textual data, posing challenges in effectively extracting and utilizing the valuable knowledge they contain. Considering the data characteristics of URT safety event cases, three different methods are developed for knowledge acquisition. Among these methods, a knowledge acquisition model is proposed to extract emergency-related knowledge from URT safety event cases efficiently and accurately.

Arc synthetic aperture radar (ArcSAR) is a remote imaging technology with the ability of wide field of view in the principle of ground-based synthetic aperture radar (GB-SAR). The existing ArcSAR imaging algorithms have the problems of low computational efficiency as the scene expands and reduced imaging accuracy caused by the slant-range approximation. To avoid these problems, this article proposes a polar coordinate format algorithm based on spherical wave decomposition. First, the echo signal in the form of spherical wave is decomposed into an integral of plane waves. Second, according to the integration, an angular filter can be constructed by virtue of the convolution theorem, which is used to eliminate the high-order phase error in the scanning angle direction. Next, the interpolation from the polar to rectangular coordinates on the filtered signal is performed to achieve decoupling of the scanning angle and wavenumber. Finally, the target focusing is achieved by 2-D inverse Fourier transform. In this article, the imaging process is analyzed through simulation experiments and compared with other imaging algorithms in terms of imaging accuracy and efficiency. Moreover, the actual data are used for verification.