## 1 Introduction  
Railway infrastructure is subject to inevitable natural wear and structural deterioration owing to long-term exposure to train loads. This degradation in performance, along with the frequent occurrence of defects, poses a direct threat to transportation safety. Timely detection and remediation of such defects are critical to ensuring the smooth and safe operation of trains. Track maintenance operations are inherently multidisciplinary, often requiring close coordination among various departments, including mechanical and electrical systems. These operations are highly susceptible to multiple external factors, including meteorological conditions and geological environments [1] which significantly complicate safety risk management. Moreover, characteristics such as technical complexity, limited construction windows, and high-intensity workloads further elevate the likelihood of rule violations and safety incidents [2]. Given these challenges, systematically identifying and analyzing the risk factors associated with track maintenance operations is essential. Developing a comprehensive risk assessment model, one that incorporates spatiotemporal information to accurately determine the time and location of potential risks during individual maintenance activities, is important for improving the safety management capabilities of the industry. In the field of risk assessment for railway track maintenance and construction, most mainstream studies primarily utilize the analytic hierarchy process (AHP) and the fuzzy comprehensive evaluation (FCE) methods. Representative works include the following: [PERSON] [3] systematically identified the risk factors involved in turnout replacement operations and developed a risk assessment model based on the fuzzy AHP. However, this study did not account for the specific spatiotemporal occurrence of risks, resulting in limited accuracy in risk localization. [PERSON] [4] established a construction safety risk evaluation system for railway engineering mechanical sections, focusing on the operational characteristics of large-scale track maintenance machinery and applying AHP to assess the state of construction safety management. Nevertheless, this study also lacked refinement in the spatiotemporal positioning of risks. [PERSON] [5] identified risk factors associated with construction and signal coordination maintenance operations on conventional-speed railways using an expert survey method and applied FCE to assess operational risks. However, this study did not clarify the specific timing or spatial coordinates of risk occurrences. [PERSON] [6] developed a risk assessment system for large-scale railway marshaling station construction projects and validated it through a case study on a project along the Shanghai-Kumming Line. Nonetheless, the spatial analysis was limited to the overall project level, lacking precise localization of risk occurrence, and the temporal dimension was not addressed. [PERSON] [7] identified influencing factors for operations involving large-scale railway track maintenance machinery through a questionnaire survey. The study evaluated a 20 d intensive maintenance period from Chawu to Pinggu on the Datong-Qinhuangdao Line, covering various operations such as turnout replacement, track tamping, and track grinding. However, the study's consideration of the spatiotemporal dimension was relatively rough, failing to accurately identify the specific line mileage and time of risk occurrences. As a result, it could not clearly distinguish the risk characteristics of different operation types, limiting its effectiveness in supporting a refined evaluation of individual maintenance activities. From a methodological perspective, researchers in other fields commonly adopt various weighting methods, such as the fuzzy AHP [8], G1 method [9], and entropy weight method (EWM) [10], to achieve an optimized integration of subjective and objective weights.