考虑公共卫生事件下客流需求的高铁开行方案

doi:10.16265/j.cnki.issn1003-3033.2023.06.1410

摘要/Abstract

摘要：

为解决公共卫生事件下的高铁列车开行方案的优化问题,以防控期望最高、乘客在公共卫生事件发生时换乘费用最少和企业运营成本最低为优化目标,构建以传染病防控政策下列车能力等为约束条件的多目标优化模型,并采用禁忌搜索-蜂群混合优化(TSABC)算法求解;利用Matlab程序,以武广高铁为例,验证模型与算法的科学性与合理性。结果表明:与传统人工蜂群算法相比,该算法具有更高的求解质量;相较于初始解的防控惩罚值,优化后的开行方案最优解降低28.90%,较非传染病防控条件下的开行方案降低40.53%,且总开行列车数减少21列。在防控公共卫生事件期间,在防控公共卫生事件期间,减少列车开行数量、压缩在高风险车站的停站和列车在相同风险等级车站停站等措施,可进一步优化开行方案。

关键词: 公共卫生事件, 客流需求, 高铁列车, 开行方案, 传染病防控

Abstract:

To optimize high-speed railway operation plan under public events, a multi-objective optimization model was established with the highest expectations of infectious disease prevention and control, the lowest interchange fees in public health events and the lowest enterprise operation cost as the optimization objective. It took the carrying capacity and train capacity under the infectious disease prevention and control policy as the constraints. The TSABC algorithm was designed to solve the model. Matlab programming was utilized to verify the rationality of the model and algorithm with the example of Wuhan-Guangzhou high-speed railway. The results indicate that compared with the artificial bee colony algorithm, the improved algorithm has better solution quality. Compared with the initial solution, the optimal solution has reduced by 28.90% in the prevention and control penalty value. Compared to the situation of non-infectious disease prevention and control, it is reduced by 40.53% and the total number of running trains decreased by 21. The result shows that during the prevention and control of public health events, the operation plan can be optimized by reducing the number of trains and stops at high-risk stations, and stopping at stations of the same risk level.

Key words: public health events, passenger demand, high speed railway, operation plan, infectious disease prevention and control

王越, 孟学雷, 秦永胜, 李依娜. 考虑公共卫生事件下客流需求的高铁开行方案[J]. 中国安全科学学报, 2023, 33(6): 166-173.

WANG Yue, MENG Xuelei, QIN Yongsheng, LI Yi'na. High-speed railway operation plan considering passenger demand under public health events[J]. China Safety Science Journal, 2023, 33(6): 166-173.

图/表 11

图1

图2

图3

图4

表1

跨风险等级传染病防控惩罚值

始发站风险类型	途径站风险类型及疫情风险惩罚值 $α n$
高风险	中风险 $α 1$ =0.25	低风险 $α 2$ =0.75
中风险	高风险 $α 3$ =0.65	低风险 $α 4$ =0.35
低风险	高风险 $α 5$ =0.80	中风险 $α 6$ =0.20

表1

图5

表2

图6

表3

表4

表5

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参数	取值	参数	取值	参数	取值
u_k/(元·km^-1)	170	σ/(元· 人^-1·min^-1)	25	λ₁	0.6
c_t/元	100	t_p/min	35	λ₂	0.2
N_s	8	I_r/min	10	λ₃	0.2

求解结果	Z	Z₁	Z₂	Z₃	列车数
正常情况	3 929 240	213.9	1 296 220 0	267 000	65
初始解	3 615 985	178.9	1 243 830 0	253 625	59
最优解	1 952 793	127.2	9 128 740	253 625	44

算法	Z	Z₁	Z₂	Z₃	列车数
文中算法	1 952 793	127.2	9 128 740	253 625	44
ABC算法	1 976 075.6	134.6	9 215 831	260 747	49

列车编号	停靠站点	编组类型	列车编号	停靠站点	编组类型	列车编号	停靠站点	编组类型	列车编号	停靠站点	编组类型
1	v₁、v₄、v₇、v₉、 v₁₀、v₁₂、v₁₅	1	12	v₁、v₂、v₄、v₆、v₇、 v₉、v₁₁、v₁₃、v₁₅	2	23	v₁、v₄、v₆、 v₁₁、v₁₂、v₁₅	2	34	v₁、v₄、v₈、 v₁₁、v₁₄、v₁₅	2
2	v₁、v₁₀、v₁₅	2	13	v₁、v₆、v₁₅	1	24	v₁、v₂、v₆、 v₉、v₁₂、v₁₅	1	35	v₁、v₄、v₆、 v₉、v₁₅	1
3	v₁、v₃、v₄、v₉、 v₁₁、v₁₂、v₁₅	1	14	v₁、v₄、v₆、v₈、 v₁₁、v₁₂、v₁₅	1	25	v₁、v₂、v₆、v₉、 v₁₁、v₁₂、v₁₅	1	36	v₁、v₄、v₇、 v₁₁、v₁₅	2
4	v₁、v₃、v₅、 v₆、v₁₅	2	15	v₁、v₂、v₆、 v₉、v₁₂、v₁₅	1	26	v₁、v₄、v₇、 v₁₀、v₁₅	1	37	v₁、v₆、 v₉、v₁₅	1
5	v₁、v₂、v₄、 v₇、v₁₁、v₁₄、v₁₅	1	16	v₁、v₃、v₆、v₇、 v₉、v₁₂、v₁₅	1	27	v₁、v₂、v₄、v₆、 v₉、v₁₁、v₁₅	1	38	v₁、v₄、v₇、 v₉、v₁₁、v₁₅	1
6	v₁、v₂、v₃、v₅、 v₇、v₉、v₁₁、v₁₅	2	17	v₁、v₆、v₉、v₁₅	1	28	v₁、v₂、v₆、 v₉、v₁₂、v₁₅	1	39	v₁、v₆、v₁₀、 v₁₁、v₁₃、v₁₅	2
7	v₁、v₆、v₉、v₁₅	1	18	v₁、v₃、v₄、v₇、 v₉、v₁₂、v₁₅	2	29	v₁、v₆、v₁₅	1	40	v₁、v₅、v₆、v₉、 v₁₂、v₁₄、v₁₅	1
8	v₁、v₂、v₆、 v₉、v₁₂、v₁₅	1	19	v₁、v₃、v₇、v₉、 v₁₀、v₁₄、v₁₅	1	30	v₁、v₆、v₁₁、 v₁₃、v₁₅	1	41	v₁、v₄、v₇、 v₁₀、v₁₃、v₁₅	1
9	v₁、v₃、v₄、v₆、 v₉、v₁₁、v₁₂、v₁₅	1	20	v₁、v₄、v₅、v₉、 v₁₂、v₁₄、v₁₅	2	31	v₁、v₂、v₄、v₆、 v₇、v₁₁、v₁₅	2	42	v₁、v₂、v₄、v₅、 v₉、v₁₁、v₁₂、v₁₅	1
10	v₁、v₆、v₁₅	1	21	v₁、v₂、v₄、v₈、 v₁₁、v₁₂、v₁₅	1	32	v₁、v₄、v₉、 v₁₁、v₁₂、v₁₅	1	43	v₁、v₄、v₆、 v₈、v₁₁、v₁₅	1
11	v₁、v₂、v₄、v₉、 v₁₁、v₁₃、v₁₅	1	22	v₁、v₆、v₁₁、v₁₅	1	33	v₁、v₆、v₉、 v₁₁、v₁₂、v₁₅	2	44	v₁、v₂、v₆、v₉、 v₁₂、v₁₅	1