Chen Tian, Zhang Yimu, Qian Xinwu, Li Jian
Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, 4800 Cao'an Road, Shanghai 201804, China.
Urban Mobility Institute, Tongji University, 4800 Cao'an Road, Shanghai 201804, China.
Transp Res Part C Emerg Technol. 2022 Apr;137:103587. doi: 10.1016/j.trc.2022.103587. Epub 2022 Feb 7.
Contact tracing is an effective measure by which to prevent further infections in public transportation systems. Considering the large number of people infected during the COVID-19 pandemic, digital contact tracing is expected to be quicker and more effective than traditional manual contact tracing, which is slow and labor-intensive. In this study, we introduce a knowledge graph-based framework for fusing multi-source data from public transportation systems to construct contact networks, design algorithms to model epidemic spread, and verify the validity of an effective digital contact tracing method. In particular, we take advantage of the trip chaining model to integrate multi-source public transportation data to construct a knowledge graph. A contact network is then extracted from the constructed knowledge graph, and a breadth-first search algorithm is developed to efficiently trace infected passengers in the contact network. The proposed framework and algorithms are validated by a case study using smart card transaction data from transit systems in Xiamen, China. We show that the knowledge graph provides an efficient framework for contact tracing with the reconstructed contact network, and the average positive tracing rate is over 96%.
接触者追踪是一种在公共交通系统中预防进一步感染的有效措施。考虑到在新冠疫情期间有大量人员感染，数字接触者追踪预计比传统的手动接触者追踪更快、更有效，传统方法既缓慢又耗费人力。在本研究中，我们引入了一个基于知识图谱的框架，用于融合来自公共交通系统的多源数据以构建接触网络、设计模拟疫情传播的算法，并验证一种有效的数字接触者追踪方法的有效性。具体而言，我们利用行程链模型整合多源公共交通数据来构建知识图谱。然后从构建的知识图谱中提取接触网络，并开发一种广度优先搜索算法以在接触网络中高效追踪受感染乘客。所提出的框架和算法通过使用来自中国厦门公交系统的智能卡交易数据进行案例研究得到了验证。我们表明，知识图谱为借助重建的接触网络进行接触者追踪提供了一个高效框架，且平均阳性追踪率超过96%。
