安全疏散与客流疏运是地铁客流组织的主要组成部分, 是关系乘客安全的重点研究问题。为系统了解公共安全领域中地铁车站安全疏散与客流疏运的发展情况与研究趋势, 首先针对国内外已发表相关文献开展文献计量学分析, 包括发文统计量分析与关键词分析; 然后分别针对安全疏散和客流疏运方面, 从研究内容特点、研究方法等方面开展回顾和综述。分析表明：安全疏散方面, 实验手段存在规模受限、无法重现实际疏散过程等局限性, 难以大规模实施; 数值模拟方法作为主要分析手段, 仍需结合大数据、机器学习等智能算法对疏散路径进行动态优化。客流疏运方面, 现有客流模拟与控制优化措施存在自适应性不足、计算复杂度高等问题。虚拟现实、机器学习和图像识别等人工智能手段和大数据方法, 是下一步研究应重点关注的内容。

Significance: Safety evacuation and passenger transportation are key components of metro crowd control, requiring critical research related to passenger safety. With the rapid development of urban rail transit, a growing number of domestic and international scholars have conducted research on these components. To systematically understand developments and research trends in metro station safety evacuation and passenger transportation within the broader field of public safety, it is necessary to review and summarize relevant studies. Progress: First, relevant Chinese and English literature on metro station safety evacuation and passenger transportation was retrieved from the Web of Science database and the China National Knowledge Infrastructure database, and relevant information about the studies was recorded. Next, a bibliometric analysis was conducted, including publication volume statistics and keyword analysis. This study reviewed and summarized the characteristics of the research content and methodologies regarding both safety evacuation and passenger flow management. It summarized the advantages and disadvantages of existing research approaches and methods, providing future development directions. Bibliometric analysis showed that research on safety evacuation in China has developed rapidly, although studies on passenger transportation require further attention. To date, research on safety evacuation has focused primarily on metro station fires, whereas studies on passenger transportation have concentrated mainly on the organization and control of large passenger flows. In the field of safety evacuation, metro station safety evacuation is characterized by multi-level enclosed spaces, multi-stage evacuation routes, and large passenger flows. Field experiments are difficult to implement due to their limitations, and microscopic models such as the social force model and cellular automaton model have thus become the primary research tools. Future research still needs to integrate intelligent algorithms, such as big data and machine learning, to dynamically optimize evacuation routes. In the field of passenger transportation, metro station passenger flow is characterized by complex, multi-directional movements and batch arrivals, and research in this area mainly relies on numerical simulation methods. Existing research primarily aims to reduce passenger waiting times and train delay times and has achieved relative maturity in optimizing train schedules and improving transport capacity. Implementing passenger flow control measures has become the main approach to reducing passenger flow risks. However, in actual metro operations, there remains a deficiency in the networked multi-station collaborative response mechanism for passenger flow organization. Conclusions and Prospects: This study conducted a bibliometric analysis of the literature on safety evacuation and passenger transportation in metro stations. It reviewed the characteristics and study methodologies used in the literature, while discussing research progress and existing limitations. The study contributes to understanding the current state of research and development trends in metro station safety evacuation and passenger transportation. Furthermore, it argues that future studies in China should place greater emphasis on passenger transportation and incorporate advanced intelligent algorithms into research on both safety evacuation and passenger transportation.