Safe travel path planning method for railway passengers during pandemic

doi:10.16265/j.cnki.issn1003-3033.2023.06.0770

Abstract

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

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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References 18

[1]	ELIAS W, ZATMEH-KANJ S. Extent to which COVID-19 will affect future use of the train in Israel[J]. Transport Policy, 2021, 110: 215-224. doi: 10.1016/j.tranpol.2021.06.008 pmid: 34127894
[2]	ZHAO Pengjun, GAO Yukun. Public transit travel choice in the post COVID-19 pandemic era: an application of the extended theory of planned behavior[J]. Travel Behaviour and Society, 2022, 28: 181-195. doi: 10.1016/j.tbs.2022.04.002 pmid: 35402163
[3]	BOGOCH I I, CREATORE M I, CETRON M S, et al. Assessment of the potential for international dissemination of Ebola virus via commercial air travel during the 2014 West African outbreak[J]. Lancet, 2015, 385(9962): 29-35. doi: S0140-6736(14)61828-6 pmid: 25458732
[4]	JONES R M, MASAGO Y, BARTRAND T, et al. Characterizing the risk of infection from mycobacterium tuberculosis in commercial passenger aircraft using quantitative microbial risk assessment[J]. Risk Analysis, 2009, 29(3): 355-365. doi: 10.1111/j.1539-6924.2008.01161.x pmid: 19076326
[5]	ZHAO Shi, ZHUANG Zian, RAN Jinjun, et al. The association between domestic train transportation and novel coronavirus (2019-nCoV) outbreak in China from 2019 to 2020: a data-driven correlational report[J]. Travel Medicine and Infectious Disease, 2020, 33: DOI: 10.1016/j.tmaid.2020.101568. doi: 10.1016/j.tmaid.2020.101568
[6]	WEI Ye, WANG Jiaoe, SONG Wei, et al. Spread of COVID-19 in China: analysis from a city-based epidemic and mobility model[J]. Cities, 2021, 110: DOI: 10.1016/j.cities.2020.103010. doi: 10.1016/j.cities.2020.103010
[7]	ZHANG Linfeng, YANG Hangjun, WANG Kun, et al. Measuring imported case risk of COVID-19 from inbound international flights:a case study on China[J]. Journal of Air Transport Management, 2020, 89: DOI: 10.1016/j.jairtraman.2020.101918. doi: 10.1016/j.jairtraman.2020.101918
[8]	林家泉, 孙凤山, 李亚冲. 客舱内呼吸道病原体传播机制与感染风险评估[J]. 中国安全科学学报, 2020, 30(2): 146-151. doi: 10.16265/j.cnki.issn1003-3033.2020.02.023
	LIN Jiaquan, SUN Fengshan, LI Yachong. Transmission mechanism of respiratory pathogens in aircraft cabin and infection risk assessment[J]. China Safety Science Journal, 2020, 30(2): 146-151. doi: 10.16265/j.cnki.issn1003-3033.2020.02.023
[9]	RAVEAU S, GUO Zhan, MUNOZ J C, et al. A behavioural comparison of route choice on metro networks: time, transfers, crowding, topology and socio-demographics[J]. Transportation Research Part A: Policy and Practice, 2014, 66: 185-195. doi: 10.1016/j.tra.2014.05.010
[10]	JIANG Yu, CEDER A. Incorporating personalization and bounded rationality into stochastic transit assignment model[J]. Transportation Research Part C: Emerging Technologies, 2021, 127: DOI: 10.1016/j.trc.2021.103127. doi: 10.1016/j.trc.2021.103127
[11]	ZHANG Yongsheng, YAO Enjian, ZHANG Junyi, et al. Estimating metro passengers' path choices by combining self-reported revealed preference and smart card data[J]. Transportation Research Part C: Emerging Technologies, 2018, 92: 76-89. doi: 10.1016/j.trc.2018.04.019
[12]	ZENG Jin, JIN Xiaonan, DONG Chunjiao, et al. A transfer route choice model of railway passengers based on the maximum utility[C]. Proceeding in International Conference on Transportation and Development 2016:Projects and Practices for Prosperity, 2016: 422-433.
[13]	TIAN Zhiqiang, SUN Guofeng, CHEN Dingjun, et al. Method for determining the valid travel route of railways based on generalised cost under the syncretic railway network[J]. Journal of Advanced Transportation, 2020, 2020: DOI: 10.1155/2020/8287648. doi: 10.1155/2020/8287648
[14]	赵宇刚, 洪雁, 杨华峰. 基于路网规划的铁路旅客出行路径选择研究[J]. 铁道经济研究, 2014 (1): 39-43.
	ZHAO Yugang, HONG Yan, YANG Huafeng. Study on railway passenger travel route choice based on road network planning[J]. Railway Economics Research, 2014 (1): 39-43.
[15]	常志朋. 遵循底线思维的重大公共卫生风险评估模型[J]. 中国安全科学学报, 2020, 30(9): 171-178.
	CHANG Zhipeng. Evaluation model of major public health risk based on bottom-line thinking[J]. China Safety Science Journal, 2020, 30(9): 171-178.
[16]	ISO31000:2009, Risk management-principles and guidelines[S].
[17]	LI Tao, RONG Lili, ZHANG Anming. Assessing regional risk of COVID-19 infection from Wuhan via high-speed rail[J]. Transport Policy, 2021, 106: 226-238. doi: 10.1016/j.tranpol.2021.04.009 pmid: 33867701
[18]	夏晓梅, 范炳全. 基于广义费用的多用户公路网平衡配流模型[J]. 交通运输工程学报, 2009, 9(3): 76-80.
	XIA Xiaomei, FAN Bingquan. Multi-user highway network equilibrium model based on generalized cost[J]. Journal of Traffic and Transportation Engineering, 2009, 9(3): 76-80.

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