Risk Mapping for Daily High‐Speed Railway Disturbances Based on Operation Loss

Confronted with the disturbances arising from various risk events, it is crucial to accurately measure the severity of risks in the dispatching section for efficient train operation and transportation management of a high‐speed railway (HSR). This paper proposes a risk mapping method for daily HSR disturbances based on a self‐formulated operation loss model, aiming to assist in identifying the spatiotemporal transportation bottlenecks and mitigating the propagation of risks. The calculation models for operation loss under risk disturbances are first established, with a focus on the instantaneous operation loss (IOL) of affected trains and the cumulative operation loss (COL) of the dispatching section, giving specific considerations on delay status, train importance, and operation scheme. Based on the delay characteristics observed in various risk scenarios, the variation curves of IOL for affected trains and dispatching sections are categorized into triangular and trapezoidal patterns. Combining the historical data statistics, the spatiotemporal risk distribution matrix is then established by occurrence probability calculation, event probability decomposition, and grid operation loss calculation, using well‐designed algorithms. Meanwhile, the importance of risk scenario features is analyzed through LightGBM classification to identify key attributes. To validate the feasibility of the proposed approach, a case study has been conducted on weekday risk disturbances in a dispatching section administrated by the Shanghai Railway Bureau. The results demonstrate that this approach can accurately depict the distribution of risk severity by considering both operation losses and decomposed probabilities, where the average COL of station risks ranges from 0.14 to 0.64, while the average COL of section risks ranges from 0.09 to 0.49. Furthermore, the attributes contributing to the risk severity can be effectively extracted for various scenarios, such as the primary delay, risk position, and train speed heterogeneity. Finally, a discussion on the generalizability and challenges of applying this method provides further verification and detailed explanations for HSR risk mapping.