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摘要: 考虑到列车密闭车厢内传染病的危害性, 研究了车厢内病毒的空间分布特性; 结合乘客间距离相关性分析结果, 构建了乘客感染预测模型, 对车厢内存在多感染者情况下每个乘客感染病毒的风险进行了评估; 为降低乘客乘车感染风险, 制定了列车乘客主动防护策略, 提出基于贪婪算法和变邻域局部搜索算法的混合启发式算法, 对车厢乘客布座问题进行优化求解; 通过基于距离的贪婪算法, 将列车固定坐标的乘客布座问题转换为最多乘客数最少病毒重叠区问题, 得到座位可行解, 并汇总各可行解得到可行域, 再基于变邻域的局部搜索算法改进座位可行解, 得到最优乘客布座方案。研究结果表明: 本文建立的感染概率评估模型可有效预测乘客感染病毒的风险, 结合基于混合启发式算法的主动防护措施可有效降低乘客乘车的感染风险; 针对短途旅客, 随着乘车人数和车厢内感染者的增加, 高风险感染者由1人增加至7人, 中风险感染者由0人增加至3人, 低风险感染者由47人增加至83人; 相较于无序就坐, 采用本文制定的布座策略可消除乘客感染风险。Abstract: Considering the danger of infectious disease in closed train compartment, the spatial distribution characteristics of the virus were studied. Combined with the correlation analysis result of the distances between passengers, the infection prediction model of passenger was constructed, and the risk of each passenger infected with virus in the case of existing multiple infections was evaluated. In order to reduce the infected risk of passenger, the active protection strategies for train passengers were formulated, and a hybrid heuristic algorithm based on greedy algorithm and variable neighborhood local search algorithm was proposed to optimize and solve the passenger seat arrangement problem. Through the distance-based greedy algorithm, the arrangement of passenger seats in the fixed coordinate was converted into the problem of the maximum number of passengers and the minimum number of virus overlapping areas, and the feasible solution of the seat was obtained. The feasible region was obtained by summing up the feasible solutions, the feasible solution of the seats was improved based on variable neighborhood local search algorithm, and the optimal scheme of seat arrangement was obtained. Research result shows that the risk of passengers infected with virus can be predicted effectively by the infection probability evaluation model, and the infection risk of passengers can be reduced effectively by the active protection measures combined with the hybrid heuristic algorithm. For the short-distance passengers, with the increase of the number of passengers and the number of people infected in the compartments, the number of people with high risk infection increases from 1 to 7, the number of people with medium risk infection increases from 0 to 3, and the number of people with low risk of infection increases from 47 to 83. Compared with the disordered sitting without control, the risk of passenger infection can be eliminated by the seat arrangement strategy.
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图 1 短途旅客感染风险预测结果
Figure 1. Prediction result of infection risks of short-distance passengers
图 2 不同传播半径下短途旅客病毒覆盖区域
Figure 2. Virus coverage areas of short-distance passengers under different spread radii
图 4 贪婪算法下短途旅客病毒覆盖区域
Figure 4. Virus coverage areas of short-distance passengers based on greedy algorithm
图 7 长途旅客感染风险预测结果
Figure 7. Prediction result of infection risks of long-distance passengers
表 1 CRH2主要车辆参数
Table 1. Main vehicle parameters of CRH2
参数 取值 动车组长度/m 25.7 拖车组长度/m 25.0 车体长度/m 20.14 车体宽度/m 3.38 车体高度/m 3.70 编组总重/t 345 一等座人数 51 二等座人数 504 餐车座位数 55 
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表 2 CRH2座位尺寸
Table 2. Seat size of CRH2
座位等级 座位宽度/mm 座间距/mm 过道宽度/mm 一等座 475 1 160 600 二等座 440 980 600
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表 3 乘客参数与病毒特性
Table 3. Passenger parameters and virus characteristics
参数 取值 感染者病毒产生率/(quanta·h-1) 100~5 000 乘客呼吸率/(m3·h-1) 0.47~0.49 乘客乘车时间/h 3 列车最大通风量/(m3·h-1) 2 000 乘客口罩的渗透系数 0.60~0.90 列车上座率/% 50~100
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[1] 谢国, 金永泽, 黑新宏, 等. 列车动力学模型时变环境参数自适应辨识[J]. 自动化学报, 2019, 45(12): 2268-2280. https://www.cnki.com.cn/Article/CJFDTOTAL-MOTO201912008.htmXIE Guo, JIN Yong-ze, HEI Xin-hong, et al. Adaptive identification of time-varying environmental parameters in train dynamics model[J]. Acta Automatica Sinica, 2019, 45(12): 2268-2280. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-MOTO201912008.htm [2] CAO Yuan, WANG Zheng-chao, LIU Feng, et al. Bio-inspired speed curve optimization and sliding mode tracking control for subway trains[J]. IEEE Transactions on Vehicular Technology, 2017, 68(7): 6331-6342. [3] 谢国, 王竹欣, 黑新宏, 等. 面向热轴故障的高速列车轴温阈值预测模型[J]. 交通运输工程学报, 2018, 18(3): 129-137. doi: 10.3969/j.issn.1671-1637.2018.03.014XIE Guo, WANG Zhu-xin, HEI Xin-hong, et al. Axle temperature threshold prediction model of high-speed train for hot axle fault[J]. Journal of Traffic and Transportation Engineering, 2018, 18(3): 129-137. (in Chinese). doi: 10.3969/j.issn.1671-1637.2018.03.014 [4] CAO Yuan, SUN Yong-kui, XIE Guo, et al. Fault diagnosis of train plug door based on a hybrid criterion for IMFs selection and fractional wavelet package energy entropy[J]. IEEE Transactions on Vehicular Technology, 2019, 68(8): 7544-7551. doi: 10.1109/TVT.2019.2925903 [5] CAO Yuan, MA Lian-chuan, XIAO Shuo, et al. Standard analysis for transfer delay in CTCS-3[J]. Chinese Journal of Electronics, 2017, 26(5): 1057-1063. doi: 10.1049/cje.2017.08.024 [6] LI Tian-tian, BAI Yu-hua, LIU Zhao-rong, et al. In-train air quality assessment of the railway transit system in Beijing: a note[J]. Transportation Research Part D: Transport and Environment, 2007, 12(1): 64-67. doi: 10.1016/j.trd.2006.11.001 [7] 陶红歌, 陈焕新, 谢军龙, 等. 空调列车硬座车厢内污染物扩散规律研究[J]. 铁道学报, 2010, 32(3): 28-32. doi: 10.3969/j.issn.1001-8360.2010.03.005TAO Hong-ge, CHEN Huan-xin, XIE Jun-long, et al. Research on contamination dispersion in hard-seat compartments of air-conditioning train[J]. Journal of the China Railway Society, 2010, 32(3): 28-32. (in Chinese). doi: 10.3969/j.issn.1001-8360.2010.03.005 [8] HUANG Chao-lin, WANG Ye-ming, LI Xing-wang, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China[J]. Lancet, 2020, 395: 497-506. doi: 10.1016/S0140-6736(20)30183-5 [9] ZHANG Jin-nong, ZHOU Lu-qian, YANG Yu-qiong, et al. Therapeutic and triage strategies for 2019 novel coronavirus disease in fever clinics[J]. The Lancet Respiratory Medicine, 2020, 8(3): e11-e12. doi: 10.1016/S2213-2600(20)30071-0 [10] WANG Chen, PETER W H, FREDERICK G H, et al. A novel coronavirus outbreak of global health concern[J]. Lancet, 2020, 395: 470-473. doi: 10.1016/S0140-6736(20)30185-9 [11] 马进, 王声湧, 邓留, 等. 入境旅客列车上152例发热病例的流行病学分析[J]. 中华疾病控制杂志, 2011, 15(2): 135-137. https://www.cnki.com.cn/Article/CJFDTOTAL-JBKZ201102016.htmMA Jin, WANG Sheng-yong, DENG Liu, et al. Epidemiological analysis on 152 cases of fever from international trains[J]. Chinese Journal of Disease Control and Prevention, 2011, 15(2): 135-137. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-JBKZ201102016.htm [12] MOORE M, VALWAY S E, IHLE W, et al. A train passenger with pulmonary tuberculosis: evidence of limited transmission during travel[J]. Clinical Infectious Diseases, 1999, 28: 52-56. doi: 10.1086/515089 [13] CHRN S C, LIAO C M, LI S S, et al. A probabilistic transmission model to assess infection risk from mycobacterium tuberculosis in commercial passenger trains[J]. Society for Risk Analysis, 2010, 31(6): 930-939. [14] CUI Fu-qiang, LUO Hui-ming, ZHOU Lei, et al. Transmission of pandemic influenza: A (H1N1) virus in a train in China[J]. Journal of Epidemiology, 2011, 21(4): 271-277. doi: 10.2188/jea.JE20100119 [15] PESTRE V, MOREL B, ENCRENAZ N, et al. Transmission by super-spreading event of pandemic A/H1N1 2009 influenza during road and train travel[J]. Scandinavian Journal of Infectious Diseases, 2012, 44: 225-227. doi: 10.3109/00365548.2011.631936 [16] AHMAD N I S, MAHAT A, KHALID A K, et al. A novel mathematical model of airborne infection[J]. Journal of Information System and Technology Management, 2019, 4(11): 62-72. [17] FURUYA H. Risk of transmission of airborne infection during train commute based on mathematical model[J]. Environmental Health and Preventive Medicine, 2007, 12: 78-83. doi: 10.1007/BF02898153 [18] GOSCÉ L, JOHANSSON A. Analysing the link between public transport use and airborne transmission: mobility and contagion in the London underground[J]. Environmental Health, 2018, 17: 1-11. doi: 10.1186/s12940-017-0345-y [19] HU Ming-sheng, LIU Xin-xin, CAI Chuang-feng, et al. On infectious diseases transmission based on traffic network[C]//IEEE. 29th Chinese Control and Decision Conference. New York: IEEE, 2017: 1495-1498. [20] HENRIK S, DEREK A T C, JUSTIN L. Estimating infectious disease transmission distances using the overall distribution of cases[J]. Epidemics, 2016, 17: 10-18. doi: 10.1016/j.epidem.2016.10.001 [21] MILLER-LEIDEN S, LOHASCIO C, NAZAROFF W W. et al. Effectiveness of in-room air filtration and dilution ventilation for tuberculosis infection control[J]. Journal of the Air and Waste Management Association, 2012, 46: 869-882. [22] WELLS W F. On air-borne infection. StudyⅡ: droplets and droplet nuclei[J]. American Journal of Hygiene, 1934, 20(3): 611-618. [23] RILEY E C. The role of ventilation in the spread of measles in an elementary school[J]. Annals of the New York Academy of Ences, 1980, 353: 25-34. doi: 10.1111/j.1749-6632.1980.tb18902.x [24] RILEY E C, MURPHY G, RILEY R L. Airborne spread of measles in a suburban elementary school[J]. American Journal of Epidemiology, 1978, 107(5): 421-432. doi: 10.1093/oxfordjournals.aje.a112560 [25] RAY A, SANGHAVI S, SHAKKOTTAI S. Improved greedy algorithms for learning graphical models[J]. IEEE Transactions on Information Theory, 2015, 61(6): 3457-3468. doi: 10.1109/TIT.2015.2427354 [26] 王淼, 吴松涛, 李永哲, 等. 基于贪婪算法的离散单位圆盘覆盖问题研究[J]. 华南理工大学学报(自然科学版), 2019, 47(12): 78-85. doi: 10.12141/j.issn.1000-565X.190370WANG Miao, WU Song-tao, LI Yong-zhe, et al. A greedy heuristic-based approach to solving the discrete unit disk cover problem[J]. Journal of South China University of Technology (Natural Science Edition), 2019, 47(12): 78-85. (in Chinese). doi: 10.12141/j.issn.1000-565X.190370 [27] CHEN Mao, TANG Xiang-yang, SONG Ting, et al. Greedy heuristic algorithm for packing equal circles into a circular container[J]. Computers and Industrial Engineering, 2018, 119: 114-120. doi: 10.1016/j.cie.2018.03.030 [28] MENG Xiang-hu, LI Jun, DAI Xian-zhong, et al. Variable neighborhood search for a colored traveling salesman problem[J]. IEEE Transactions on Intelligent Transportation Systems, 2018, 19(4), 1018-1026. doi: 10.1109/TITS.2017.2706720 [29] LUIZ F O, SANTOS M, IWAYAMA R S, et al. A variable neighborhood search algorithm for the bin packing problem with compatible categories[J]. Expert Systems with Applications, 2019, 124: 209-225. doi: 10.1016/j.eswa.2019.01.052 [30] DAHMANI N, SAOUSSEN K, GHAZOUANI D. A variable neighborhood descent approach for the two-dimensional bin packing problem[J]. Electronic Notes in Discrete Mathematics, 2015, 47: 117-124. doi: 10.1016/j.endm.2014.11.016 -
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