基于调度的公交系统传输风险分析
Schedule-based Analysis of Transmission Risk in Public Transportation Systems
摘要：
包括COVID-19在内的空气传播疾病提出了公共交通系统传播风险的问题。然而，在公共交通传输风险缓解方法的有效性定量分析是缺乏的。本文开发了一个传输风险建模框架的基础上Wells-Riley模型作为输入的公交运营特性，时间表，始发地-目的地（OD）的需求，和病毒特征。该模型对运营商可以控制的各种因素以及可能成为更广泛政策决定主题的外部因素（例如口罩佩戴）敏感。该模型被用来评估作为OD流量的函数的传输风险，计划的操作，以及戴口罩，通风和感染率等因素。使用马萨诸塞州海湾交通管理局（MBTA）红线的实际数据，本文探讨了不同感染率情景下的传播风险，包括幅度和空间特征。本文从病毒载量相关因素和乘客载量因素两个方面对综合影响进行了评估。增加频率可以减轻传播风险，但不能完全补偿感染率的增加。训练的不同车厢上的不平衡乘客分布被示出为增加整个系统范围的感染概率。在决策过程中还应考虑到空间感染率模式，因为它已被证明会影响传播风险。对于有支线的线路，支线间的需求分配是重要的，通过调整支线间的间隔分配来平衡不同支线的列车负荷有助于降低风险。
英文摘要：
Airborne diseases, including COVID-19, raise the question of transmission risk in public transportation systems. However, quantitative analysis of the effectiveness of transmission risk mitigation methods in public transportation is lacking. The paper develops a transmission risk modeling framework based on the Wells-Riley model using as inputs transit operating characteristics, schedule, Origin-Destination (OD) demand, and virus characteristics. The model is sensitive to various factors that operators can control, as well as external factors that may be subject of broader policy decisions (e.g. mask wearing). The model is utilized to assess transmission risk as a function of OD flows, planned operations, and factors such as mask-wearing, ventilation, and infection rates. Using actual data from the Massachusetts Bay Transportation Authority (MBTA) Red Line, the paper explores the transmission risk under different infection rate scenarios, both in magnitude and spatial characteristics. The paper assesses the combined impact from viral load related factors and passenger load factors. Increasing frequency can mitigate transmission risk, but cannot fully compensate for increases in infection rates. Imbalanced passenger distribution on different cars of a train is shown to increase the overall system-wide infection probability. Spatial infection rate patterns should also be taken into account during policymaking as it is shown to impact transmission risk. For lines with branches, demand distribution among the branches is important and headway allocation adjustment among branches to balance the load on trains to different branches can help reduce risk.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
