AI-Mediated Impacts of Equipment, Compliance, and Policy on Railway Management Efficiency: A Case Study of the Beijing Railway Bureau

Abstract

This research uses quantitative and qualitative research. mixed research methods, in-depth mixed-methods investigation, delving into the intricate ways in which artificial intelligence influences railway management efficiency through the mediating effects of policy factors, equipment status, and operational compliance among railway organizations in China. In an effort to comprehensively understand this complex relationship, the study combines a meticulously designed 751sample survey with 20 in-depth interviews. The survey sample is carefully selected to cover a wide range of railway sectors and organizational scales across China, ensuring representativeness. The in-depth interviews, on the other hand, are conducted with key stakeholders, including senior railway managers, operations directors, AI implementation specialists, equipment maintenance supervisors, and policy compliance officers, to gain rich qualitative insights. Research Objectives: (1) To analyze the impacts of equipment status, operational compliance, and policy factors on railway management efficiency in the Beijing Railway Bureau, China. (2)To examine how AI mediates the relationships between internal and policy factors and to propose an implementation framework for enhanced efficiency. This research aims to explore the elaborate mechanisms by which artificial intelligence, policy factors, equipment status, and operational compliance interact and jointly affect railway management efficiency, with the ultimate goal of enhancing the operational performance and safety capabilities of Chinese railway organizations in an increasingly complex and technology-driven transportation landscape. Literature Review use: Overview of Railway Management Efficiency in China, Underpinning Theory. (1) Systems Theory of Accident Causation. (2) Artificial Intelligence Theory. (3) Equipment Reliability Theory. The key findings of this study are both significant and far-reaching. It has been clearly demonstrated that artificial intelligence implementation exerts a positive and profound impact on policy implementation effectiveness, equipment performance optimization, and operational compliance monitoring initiatives. A well-integrated AI system serves as the cornerstone for promoting enhanced policy execution, equipment management, and compliance adherence within railway organizations. For instance, it streamlines policy. Quantitative Phase: A structured questionnaire is distributed to the sampled population, and quantitative data is analyzed to test relationships among core variables such as equipment status, compliance, policy, AI application, and management efficiency.

The results indicate that Qualitative Phase: Following the quantitative analysis, semi-structured interviews are conducted with selected informants to explore deeper explanations and contextual factors underlying the survey results. The phase-sequenced methodology serves to expand, enhance, and clarify findings through methodological triangulation. This approach, aligned with the "compatibility hypothesis," supports the pursuit of more comprehensive, nuanced, and actionable research conclusions Practical Guidance: The findings offer evidence-based guidance for stakeholders: to achieve operational excellence, railway organizations must focus on strengthening AI implementation to optimize policy execution, enhance equipment performance, and ensure comprehensive operational compliance. This validates strategic documents like China's "Intelligent Railway Development Outline." In short, AI is not just a technology but a critical enabler that transforms traditional railway management into an intelligent, data-driven ecosystem, maximizing efficiency and ensuring long-term sustainability in a complex transportation environment.