当前位置: X-MOL 学术Transp. Res. Rec. J. Transp. Res. Board › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Estimation and Mitigation of Epidemic Risk on a Public Transit Route using Automatic Passenger Count Data
Transportation Research Record: Journal of the Transportation Research Board ( IF 1.6 ) Pub Date : 2021-02-01 , DOI: 10.1177/0361198120985133
Pramesh Kumar 1 , Alireza Khani 1 , Eric Lind 2 , John Levin 2
Affiliation  

This paper studies the potential spread of infectious disease through passenger encounters in a public transit system using automatic passenger count (APC) data. An algorithmic procedure is proposed to evaluate three different measures to quantify these encounters. The first two measures quantify the increased possibility of disease spread from passenger interaction when traveling between different origin–destination pairs. The third measure evaluates an aggregate measure quantifying the relative risk of boarding at a particular stop of the transit route. For calculating these measures, compressed sensing is employed to estimate a sparse passenger flow matrix planted in the underdetermined system of equations obtained from the APC data. Using the APC data of Route 5 in Minneapolis/St. Paul region during the COVID-19 pandemic, it was found that all three measures grow abruptly with the number of passengers on board. The passenger contact network is densely connected, which further increases the potential risk of disease transmission. To reduce the relative risk, it is proposed to restrict the number of passengers on-board and analyze the effect of this using a simulation framework. It was found that a considerable reduction in the relative risk can be achieved when the maximum number of passengers on-board is restricted below 15. To account for the reduced capacity and still maintain reasonable passenger wait times, it would then be necessary to increase the frequency of the route.



中文翻译:

使用自动乘客计数数据估算和缓解公共交通路线上的流行病风险

本文使用自动乘客计数(APC)数据研究了在公共交通系统中因乘客遭遇而引起的传染病传播。提出了一种算法程序来评估三种不同的量度以量化这些遭遇。前两个措施量化了在不同始发地-目的地对之间旅行时,乘客与人之间的互动所传播疾病的可能性增加。第三项措施是对总体措施进行量化,以量化在特定路线中途登车的相对风险。为了计算这些量度,采用压缩传感来估计在从APC数据获得的欠定方程组中植入的稀疏乘客流矩阵。使用明尼阿波利斯/圣保罗的5号公路的APC数据。在COVID-19大流行期间的保罗地区,结果发现,随乘船人数的增加,所有这三项措施都会突然增长。乘客联系网络紧密相连,这进一步增加了疾病传播的潜在风险。为了降低相对风险,建议限制机上乘客的数量,并使用模拟框架分析其影响。已经发现,当机上最大乘客人数限制在15岁以下时,可以相对降低相对危险度。为解决容量下降的问题并仍保持合理的乘客等待时间,则有必要增加路线的频率。为了降低相对风险,建议限制机上乘客的数量,并使用模拟框架分析其影响。已经发现,当机上最大乘客人数限制在15岁以下时,可以相对降低相对危险度。为解决容量下降的问题并仍保持合理的乘客等待时间,则有必要增加路线的频率。为了降低相对风险,建议限制机上乘客的数量,并使用模拟框架分析其影响。已经发现,当机上最大乘客人数限制在15岁以下时,可以相对降低相对危险度。为解决容量下降的问题并仍保持合理的乘客等待时间,则有必要增加路线的频率。

更新日期:2021-02-01
down
wechat
bug