| COVID-19在城市轨道交通系统内的传播建模与预测 |
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| 引用本文： | 雷斌,刘星良,曹振,郝亚睿,张源,陈新苗.COVID-19在城市轨道交通系统内的传播建模与预测[J].交通运输工程学报,2020,20(3):139-149. |
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| 作者姓名： | 雷斌 刘星良 曹振 郝亚睿 张源 陈新苗 |
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| 作者单位： | 1.西安建筑科技大学 土木工程学院, 陕西 西安 7100552.长安大学 公路学院, 陕西 西安 710064 |
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| 基金项目： | 陕西省自然科学基础研究计划;陕西省交通科技项目;国家重点研发计划 |
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| 摘 要： | 考虑城市轨道交通出行特征, 将新冠疫情下城市轨道交通系统内的乘客分为易感者、感染者、暴露者; 假设病毒自由传播, 以疫情发生初期的病例为研究对象, 结合相关研究, 选择病毒传染概率为0.41;将感染者乘坐城市轨道交通的过程分为出入站阶段和乘车阶段, 考虑病毒有效传播范围、人群分布特征、人群流动特征, 建立新冠疫情在城市轨道交通系统内部的传播模型; 以某市地铁为模拟案例, 假如有13个感染者乘坐地铁, 结合历史客运数据确定模型参数的取值, 预测不同载运水平下可能造成的感染人数, 同时研究与可能感染人数相关的各类要素。研究结果表明: 当载运水平降低至平均水平的10%时, 多数案例的可能感染人数降低至1人以下, 证明了城市轨道交通客流管控强度的有效性, 起终点站内人数折减引起的感染人数变化(小于20%)低于车厢人数折减引起的变化(60%~80%), 说明相比起终点站内人数, 车厢内的人群密集程度对可能感染人数的影响更加显著; 在经停时, 假如上下车人数之比不大于1, 则能有效控制可能感染人数的升高; 当经停站数与可能感染人数非线性正相关时, 载运水平、经停站数、可能感染人数之间的函数关系具有较高的拟合优度(决定系数为0.700 1)。
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| 关 键 词： | 城市轨道交通 新型冠状病毒 传播模型 可能感染人数 人群密集程度 运输方案 |
| 收稿时间： | 2020-03-25 |
Modeling and forecasting of COVID-19 spread in urban rail transit system |
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| LEI Bin,LIU Xing-liang,CAO Zhen,HAO Ya-rui,ZHANG Yuan,CHEN Xin-miao.Modeling and forecasting of COVID-19 spread in urban rail transit system[J].Journal of Traffic and Transportation Engineering,2020,20(3):139-149. |
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| Authors: | LEI Bin LIU Xing-liang CAO Zhen HAO Ya-rui ZHANG Yuan CHEN Xin-miao |
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| Affiliation: | 1.School of Civil Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, Shaanxi, China2.School of Highway, Chang'an University, Xi'an 710064, Shaanxi, China |
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| Abstract: | Passenger group in urban rail transit system under the COVID-19 epidemic was divided into susceptible, infected and exposed ones considering urban rail transit travel characters. In the research environment of COVID-19 free spread and the case sampling time of early spread, the infection probability of 0.41 was selected based on related studies. The urban rail transit ridership in COVID-19 case was divided into inbound/outbound phase and riding phase. Considering COVID-19 effective spread range, passengers distribution and moving characters, the COVID-19 spreading model in the urban rail transit system was built. Taking the metro system in a certain city as the simulation case, 13 patients in metro ridership cases were assumed. With the accessibility of historical passenger data, the parameters in the model were determined. The possible infections in different loading levels were forecasted, and the elements related to possible infections were discussed. Analysis result indicates that when the loading level decreases to 10% of the average level, the possible infections in most cases are less than 1, which proves the effectiveness of current urban rail transit passenger control strength. The change in possible infections caused by the reduction in passenger number in the start/terminal(less than 20%) is less than that caused by the reduction in passenger number in the vehicle(60%-80%). Therefore, the passenger density in the vehicle has more significant impact on the possible infection compared to the passenger number in the start/terminal. When passing the stop, if the ratio of the on/off board passenger numbers is no more than 1, the possible infections can be controlled effectively. If making the possible infections nonlinearly and positively correlate to the stop number, the function among the loading level, stop number and possible infections will have better performance(determination coefficient is 0.700 1). |
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