Yadong Zhang

The Train Autonomous Control System (TACS) represents the future direction of next-generation urban rail transit development. To ensure its high safety, a comprehensive hazard analysis is required. While the Systems-Theoretic Accident Model and Processes (STAMP) theory demonstrates strong applicability in hazard analysis of complex technical systems, it faces challenges, including extensive analytical workload, low efficiency and difficulties in quantifying coupling relationships between hazardous factors. This paper extends the System-Theoretic Process Analysis (STPA) method based on the STAMP theory, proposing an automatic hazard identification algorithm. Building upon the automatic generation of unsafe control actions and causal factors, we establish a capacity-load risk propagation model among hazardous factors using complex network theory. By integrating improved network topology metrics to define model parameters and designing quantitative representations of safety constraints, we investigate risk propagation processes under different protection capabilities. Experimental validation through TACS train turn-back scenarios demonstrates that our method effectively identifies potential hazards with high efficiency and comprehensiveness. The results indicate that enhancing node safety constraints in the proposed risk propagation model successfully contains risk propagation scope and frequency for most hazardous nodes. The residual risk-propagating nodes are systematically identified as critical high-risk targets, providing scientifically grounded priorities for systemic risk control strategies.

Timely and precise short‐term passenger flow forecasting (STPFF) is a key to intelligent urban rail transit (URT) operation. The uncertainty of passenger travel behaviors leads to passenger flow data showing high nonlinearities and complex spatio‐temporal correlation. Therefore, the ability to capture such correlations determines the prediction performance of the model. However, current methods either consider only the physical topology of URT network or lack the ability to model global spatial dynamics, making it difficult to obtain satisfactory predictions. Therefore, a Multiple Spatio‐Temporal Features Fusion (MSTFF) network is proposed for STPFF in URT networks. Specifically, the connectivity and passenger flow similarity relationships in URT networks are encoded as physical adjacency graph and virtual functional similarity graph, respectively, and then these two complementary graphs are input into two designed Residual Graph Convolutional Gated Recurrent Unit (RGCGRU) modules to extract their spatio‐temporal features. Second, an Attention‐based Gated Recurrent Unit (AGRU) module is designed to capture global dynamic evolutionary trends in both spatial and temporal dimensions. Finally, a fully connected neural network is used to fuse these features. Experiments on two real passenger flow datasets of URT show that MSTFF performs well on various prediction tasks (15, 30, 45, and 60 min).

Accurate prediction of train delay is an important basis for the intelligent adjustment of train operation plans. This paper proposes a train delay prediction model that considers the delay propagation feature. The model consists of two parts. The first part is the extraction of delay propagation feature. The best delay classification scheme is determined through the clustering method of delay types for historical data based on the density-based spatial clustering of applications with noise algorithm (DBSCAN), and combining the best delay classification scheme and the k-nearest neighbor (KNN) algorithm to design the classification method of delay type for online data. The delay propagation factor is used to quantify the delay propagation relationship, and on this basis, the horizontal and vertical delay propagation feature are constructed. The second part is the delay prediction, which takes the train operation status feature and delay propagation feature as input feature, and use the gradient boosting decision tree (GBDT) algorithm to complete the prediction. The model was tested and simulated using the actual train operation data, and compared with random forest (RF), support vector regression (SVR) and multilayer perceptron (MLP). The results show that considering the delay propagation feature in the train delay prediction model can further improve the accuracy of train delay prediction. The delay prediction model proposed in this paper can provide a theoretical basis for the intelligentization of railway dispatching, enabling dispatchers to control delays more reasonably, and improve the quality of railway transportation services.