面向传染病疫情防控的公共交通运行管理决策支持研究

近年来全球新发重大传染病疫情不断出现，已成为人类社会必须防范应对的重大风险。公共交通在传染病疫情防控过程中承担着阻断病毒传播和保障复工复产的功能，疫情期间公共交通运行管理的决策需求和技术支撑体系与日常情况有显著差异。现有研究多针对公交日常运行决策需求展开，虽有少量针对突发公共事件的应急管理决策支持的研究，但多针对自然灾害和事故灾难场景，无法迁移应用于传染病疫情防控。基于此，以新型冠状病毒肺炎（COVID-19）疫情为例，综合考虑突发公共卫生事件应急管理流程和疫情防控实际情况，系统梳理疫情不同阶段的防控目标和决策需求，提出一种面向传染病疫情防控的公共交通运行管理决策支持系统框架，建立基于公共卫生事件案例库、多源数据融合库、公交数据分析技术库和公交防疫策略库的功能架构，并设计不同功能模块的算法模型。研究以厦门为例，对提出的决策支持系统的功能架构和算法模型进行验证。研究结果表明，构建公交乘客出行链的成功率为89.7%，并可应用于疫情不同阶段的关联客流分析、感染者同乘人员的追溯分析、医护人员等防疫人员的通勤出行识别、公交运行满载率监控等方面。研究成果不仅对传染病疫情防控有实用价值，而且对突发公共事件应急管理决策支持方法亦有理论贡献。

Research on Decision Support for Public Transport Operations and Management for Epidemic Prevention and Control of Infectious Diseases

In recent years, globally emerging infectious diseases have become a major risk that society must proactively guard against. In the prevention and control of infectious diseases, public transport plays a role in blocking the spread of a virus and ensuring the resumption of work and production. During an epidemic period, the decision-making needs and technical support system of public transport operations and management are significantly different from that of the routine situation. Most of the existing research focuses on the decision-making needs of public transport daily operations. Although there have been a few studies on decision-making support for the management of public emergencies, most of them focus on natural or accidental disasters and cannot be transferred to the prevention and control of infectious diseases. Taking the 2019 coronavirus disease (COVID-19) epidemic as an example, this paper presents a decision-support framework for public transport operations and management for the prevention and control of infectious diseases. In this framework, the management process for public health emergencies and the prevention and control of the COVID-19 epidemic were comprehensively considered, and prevention-control objectives and decision-making needs at different stages of the epidemic were systematically analyzed. A functional architecture based on the public health incident case library, multi-source data fusion library, public transport data analysis technical library, and public transport epidemic prevention strategy library was established, and algorithms and models for each library were designed. Xiamen was used as an example to verify the functional architecture, algorithms, and models of the proposed decision-support system. The results show that the success rate for building the trip chain of public transport passengers was 89.7%, and that the trip chain data can be applied to an analysis of trip-related passenger flows, the retrospective analysis of passengers traveling with the infected person, the commuting travel identification of medical staff and other epidemic-prevention personnel, the monitoring of vehicle capacity rate, and other aspects at different stages of the epidemic. The research results not only have practical value for prevention and control measures for infectious diseases, but also make a theoretical contribution to decision support methods for the management of public emergencies.