疫情背景下铁路旅客安全出行路径规划方法

doi:10.16265/j.cnki.issn1003-3033.2023.06.0770

中国安全科学学报 ›› 2023, Vol. 33 ›› Issue (6): 128-134.doi: 10.16265/j.cnki.issn1003-3033.2023.06.0770

疫情背景下铁路旅客安全出行路径规划方法

吕红霞¹^,²^,³(), 刘坤¹, 蒋雪莹¹, 潘金山¹^,²^,³^,^**()

1 西南交通大学交通运输与物流学院,四川成都 610031
2 综合交通运输智能化国家地方联合工程实验室,四川成都 610031
3 综合交通大数据应用技术国家工程实验室,四川成都 610031

收稿日期:2023-01-11 修回日期:2023-04-15 出版日期:2023-08-07
通讯作者:
**潘金山(1981—),男,湖北枝江人,博士研究生,高级工程师,研究方向为交通信息技术、运输组织优化。E-mail: jshpan@swjtu.cn。

作者简介:

吕红霞 (1969—),女,河北邯郸人,博士,教授,主要从事交通运输管理信息系统与决策、计算机编制列车运行图、交通运输安全等方面的研究。E-mail: hongxialu@163.com。

基金资助:
国家自然科学基金资助(52072314); 国家自然科学基金资助(52172321); 国家自然科学基金资助(52102391); 四川省科技计划项目(2022YFH0016); 四川省科技计划项目(2022YFQ0101); 交通运输部交通运输行业重点科技项目(2022-ZD7-132)

Safe travel path planning method for railway passengers during pandemic

LYU Hongxia¹^,²^,³(), LIU Kun¹, JIANG Xueying¹, PAN Jinshan¹^,²^,³^,^**()

1 School of Transportation and Logistics, Southwest Jiaotong University, Chengdu Sichuan 610031, China
2 National and Local Joint Engineering Laboratory of Comprehensive Intelligent Transportation, Chengdu Sichuan 610031, China
3 National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Chengdu Sichuan 610031, China

Received:2023-01-11 Revised:2023-04-15 Published:2023-08-07

摘要/Abstract

摘要：

为降低疫情期间旅客出行感染风险,助力科学精准的疫情防控,提出考虑实时区域感染风险的铁路旅客安全出行路径动态规划方法。首先,基于概率风险理论,将区域感染风险定义为区域疫情爆发概率和疫情影响程度的函数,考虑旅客交流强度、区域人口及流动性,评估区域感染风险;其次,综合考虑区域感染风险、旅行时间与旅行费用,运用Logit模型得到广义出行成本,将路网相邻服务节点间的广义成本作为网络边的权值,进而构建考虑旅行感染风险的铁路出行服务网络;最后,基于疫情背景下旅客出行需求与出行路径决策原则,建立以广义出行成本最低为目标的铁路旅客安全出行路径规划模型,并运用Dijkstra算法求解。以衡水至北京的路径选择为案例,将所提方法与仅考虑旅行时间、旅行费用的路径规划方法进行对比,并分析各路径方案实际客流分担率的变化。研究结果表明:基于铁路服务网络的旅客出行数据,利用所提方法可以评估疫情感染风险,且风险评估结果与实际感染人数存在显著相关性;旅行感染风险是影响旅客选择出行路径的重要因素,造成了广义出行成本显著增加,所提方法的路径规划结果与疫情期间旅客实际出行特征基本相符。

关键词: 疫情, 铁路旅客, 安全出行, 路径规划, 区域感染风险

Abstract:

In order to reduce the risk of passenger travel infection during the pandemic, and help scientific and accurate epidemic prevention and control, a dynamic planning method for railway passengers' safe travel path considering real-time regional infection risk was proposed. Firstly, based on the probabilistic risk theory, the regional infection risk was defined as the function of infection outbreak probability and impact level. The regional infection risk was evaluated by considering the passenger exchange intensity, regional population and mobility. Secondly, comprehensively considering the regional infection risk, travel time and ticket price, the generalized travel cost was obtained by the Logit model. The generalized travel cost between adjacent nodes as the weights of the network edges, and then the railway travel service network considering the travel infection risk was constructed. Finally, based on passenger travel demands and the principles of travel path decisions during the pandemic, a railway passenger safe travel path planning model with the goal of minimizing the generalized travel cost was established, and solved by the Dijkstra algorithm. Taking the path selection from Hengshui to Beijing as an example, the method proposed in this paper was compared with the path planning method that only considers travel time and travel costs, and the changes of the actual passenger flow sharing rate for each scheme were analyzed. The research results show that: based on the passenger travel data from the rail service network, the infection risk can be evaluated by the method proposed in the paper. And the results are significantly correlated with the active cases. The risk of travel infection is an important factor influencing travelers' choice of travel routes, causing a significant increase in travel costs, and the route planning results in the paper are generally consistent with the actual travel patterns during the pandemic.

Key words: pandemic, railway passengers, safety travel, path planning, regional infection risk

吕红霞, 刘坤, 蒋雪莹, 潘金山. 疫情背景下铁路旅客安全出行路径规划方法[J]. 中国安全科学学报, 2023, 33(6): 128-134.

LYU Hongxia, LIU Kun, JIANG Xueying, PAN Jinshan. Safe travel path planning method for railway passengers during pandemic[J]. China Safety Science Journal, 2023, 33(6): 128-134.

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路径编号	出行路径	车次	旅行时间/min	旅行费用/元
1	衡水—沧州—廊坊—北京	K1092	199	87.5
2	衡水—石家庄—保定—北京	D1664+G6722	168	156
3	衡水—沧州—天津—北京	D1647+G124	148	177

路径编号	出行路径	广义出行成本	实际客流分担率/%
1	衡水—沧州—廊坊—北京	136.5	52.4
2	衡水—沧州—天津—北京	163.3	24.1
3	衡水—石家庄—保定—北京	167.0	23.5

路径编号	出行路径	旅行感染风险	广义出行成本	实际客流分担率/%
1	衡水—沧州— 廊坊—北京	0.185	155.0	65.8
2	衡水—石家庄— 保定—北京	0.336	196.9	29.3
3	衡水—沧州— 天津—北京	1.119	278.9	4.9

疫情背景下铁路旅客安全出行路径规划方法

Safe travel path planning method for railway passengers during pandemic

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