COVID-19在城市轨道交通系统内的传播建模与预测

雷斌; 刘星良; 曹振; 郝亚睿; 张源; 陈新苗

doi:10.19818/j.cnki.1671-1637.2020.03.013

COVID-19在城市轨道交通系统内的传播建模与预测

doi: 10.19818/j.cnki.1671-1637.2020.03.013

雷斌^1,,
刘星良^2, ,,
曹振¹,
郝亚睿¹,
张源¹,
陈新苗¹

1.
西安建筑科技大学土木工程学院, 陕西西安 710055
2.
长安大学公路学院, 陕西西安 710064

基金项目:

国家重点研发计划项目 2016YFC0802208

陕西省交通科技项目 16-40K

陕西省自然科学基础研究计划 2017JQ5122

详细信息

作者简介:
雷斌(1978-), 男, 陕西西安人, 西安建筑科技大学教授, 工学博士, 从事城市轨道交通研究

通讯作者:
刘星良(1989-), 男, 陕西西安人, 长安大学工学博士研究生

中图分类号: U113
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出版历程
- 收稿日期: 2020-03-25
- 刊出日期: 2020-06-25

Modeling and forecasting of COVID-19 spread in urban rail transit system

1.
School of Civil Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, Shaanxi, China
2.
School of Highway, Chang'an University, Xi'an 710064, Shaanxi, China

Funds:

National Key Research and Development Program of China 2016YFC0802208

Shaanxi Transportation Technology Project 16-40K

Shaanxi Natural Science Basic Research Program 2017JQ5122

More Information

Author Bio:
LEI Bin(1978-), male, professor, PhD, leibin@xauat.edu.cn

Corresponding author: LIU Xing-liang(1989-), male, doctoral student, xingliangl125@outlook.com

摘要

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)。
- 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
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).
- urban rail transit /
- COVID-19 /
- spreading model /
- possible infection /
- passenger density /
- transport strategy

HTML全文

图 1 城市轨道交通站内人员分布

Figure 1. Passenger distribution in urban rail transit station

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图 2 城市轨道交通车厢内人员分布

Figure 2. Passenger distribution in urban rail transit vehicle

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图 3 各案例不同载运水平下的可能感染人数预测结果

Figure 3. Prediction results of possible infections under different loading levels in each case

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图 4 可能感染人数与城市轨道交通站内和车厢人数的关系

Figure 4. Relationships between possible infections and passenger numbers in station and vehicle

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图 5 上下车人数与可能感染人数之间的关系

Figure 5. Relationship among on/off board number and possible infections

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图 6 载运水平、经过站数与可能感染人数之间的关系

Figure 6. Relationships among loading level, stop number and possible infections

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表 1 COVID-19模拟病例的地铁出行信息

Table 1. Metro travel informations of COVID-19 simulation cases

案例编号	始发站	始发日期	始发时间	换乘站	中转时间	终点站	终点时间
1	S4线、站点29	2020-01-14	14:30			S4线、站点7	15:20
2	S4线、站点7	2020-01-18	13:00	S4线、站点15	13:24	S1线、站点8	13:44
3	S4线、站点7	2020-01-20	19:40			S4线、站点8	19:49
4	S4线、站点9	2020-01-20	20:30			S4线、站点7	20:36
5	S2线、站点8	2020-01-21	10:20	S2线、站点10	10:25	S1线、站点17	10:37
6	S3线、站点15	2020-01-21	13:30			S3线、站点18	13:38
7	S4线、站点15	2020-01-21	16:00			S4线、站点7	16:19
8	S4线、站点7	2020-01-22	15:00	S4线、站点15	15:21	S1线、站点8	15:38
9	S4线、站点15	2020-01-22	23:00			S4线、站点7	23:19
10	S4线、站点14	2020-01-25	7:00	S4线、站点15	7:07	S2线、站点10	7:13
11	S1线、站点15	2020-01-27	20:54	S1线、站点18	21:10	S3线、站点23	21:57
12	S3线、站点23	2020-01-28	11:40			S3线、站点8	12:44
13	S4线、站点14	2020-02-04	10:00	S4线、站点15	10:07	S1线、站点1	10:42

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表 2 COVID-19模拟病例的城市轨道交通出行数据

Table 2. Urban rail transit travel data of COVID-19 simulation cases

编号	客运水平	入站过程		中间过程				出站
编号	客运水平	始发站站内人数	初始乘车车厢人数	上半程上下车人数比例	换乘站站内人数	下半程乘车车厢人数	下半程上下车人数比例	终点站站内人数
1	模拟	80	13	0.79、1.08、1.37、0.94、2.01、1.16、1.20、1.47、1.15、1.12、1.15、1.03、1.42、1.21、1.14、0.93、1.11、0.59、0.68、1.17				38
	平均	57	9					35
	高峰	97	16					67
2	模拟	25	28	1.03、0.64、0.67、1.36、0.83、1.25、1.38	100	62	1.37、1.07、1.53、1.37、1.36	25
	平均	23	26		90	56		23
	高峰	44	50		182	113		44
3	模拟	20	25	0.61、2.00				120
	平均	16	20					64
	高峰	30	38					126
4	模拟	50	62	0.85				30
	平均	33	40					28
	高峰	63	78					54
5	模拟	60	55	1.31	200	87	0.85、0.69	80
	平均	70	64		162	71		74
	高峰	115	105		242	105		138
6	模拟	45	32	1.90、1.66				38
	平均	40	28					32
	高峰	81	58					49
7	模拟	28	49	1.20、1.16、0.93、1.06、0.85、0.83、1.14				70
	平均	22	39					57
	高峰	45	78					109
8	模拟	20	31	0.91、1.10、0.72、1.18、0.89、0.93、1.23	110	43	1.13、1.29、1.17、1.11、1.08	10
	平均	18	27		67	26		9
	高峰	34	53		135	53		17
9	模拟	40	25	0.39、0.28、0.52、0.32、1.17、5.07、0.34				20
	平均	110	69					37
	高峰	224	140					70
10	模拟	13	5		20	4		30
	平均	129	50		103	65		142
	高峰	202	78		205	135		226
11	模拟	15	4	1.83、0.93	25	3	1.28、0.48、0.43、0.35、0.51、0.44、1.16、6.31	8
	平均	26	41		22	44		10
	高峰	50	83		49	86		19
12	模拟	7	3	0.21、0.76、1.31、1.55、1.58、1.38、1.72、0.79、1.12、1.31、0.98、1.34、1.38、1.27				24
	平均	19	22					88
	高峰	28	38					153
13	模拟	12	3		14	3	1.13、0.70、1.31、1.32、1.57、1.07、2.46、1.61、1.18、1.02、1.53、0.76	3
	平均	134	55		63	25		41
	高峰	227	86		122	61		76

下载: 导出CSV

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