突发公共卫生事件下应急定制公交线路优化

马昌喜; 王超; 郝威; 刘晶; 张兆磊

doi:10.19818/j.cnki.1671-1637.2020.03.008

突发公共卫生事件下应急定制公交线路优化

doi: 10.19818/j.cnki.1671-1637.2020.03.008

马昌喜^1,,
王超¹,
郝威^2, ,,
刘晶²,
张兆磊²

1.
兰州交通大学交通运输学院, 甘肃兰州 730070
2.
长沙理工大学交通运输工程学院, 湖南长沙 410114

基金项目:

国家自然科学基金项目 71861023

教育部人文社会科学研究项目 18YJC630118

中国黑山科技部国际合作项目 3-2

详细信息

作者简介:
马昌喜(1979-), 男, 湖北汉川人, 兰州交通大学教授, 工学博士, 从事交通运输系统优化与设计研究

通讯作者:
郝威(1983-), 男, 山西太原人, 长沙理工大学教授, 工学博士

中图分类号: U491
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出版历程
- 收稿日期: 2020-03-03
- 刊出日期: 2020-06-25

Emergency customized bus route optimization under public health emergencies

1.
School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou 730070, Gansu, China
2.
School of Traffic and Transportation Engineering, Changsha University of Science and Technology, Changsha 410114, Hunan, China

Funds:

National Natural Science Foundation of China 71861023

Humanities and Social Sciences Research Program of Ministry of Education 18YJC630118

International Cooperation Project of Ministry of Science and Technology of China Montenegro 3-2

More Information

Author Bio:
MA Chang-xi(1979), male, professor, PhD, machangxi@mail.lzjtu.cn

Corresponding author: HAO Wei(1983-), male, professor, PhD, haowei@csust.edu.cn

摘要

摘要: 考虑突发公共卫生事件下的疫情防控要求, 构建了一种应急定制公交线路优化方法; 对城市中已经封闭的小区和路段进行筛查, 并将这些小区和路段设置为应急定制公交禁行区域; 以所有应急定制公交总运行时长最短为目标, 以乘客上座率不超过安全阈值为约束, 同时考虑供需匹配, 构建了突发公共卫生事件下应急定制公交线路优化模型; 设计了遗传算法来求解该模型, 采用三段式混合编码方式进行染色体编码, 3段染色体分别由定制公交停车场编号、上车站点编号和下车站点编号组成, 运用贪婪策略解码染色体; 采用模拟案例验证了模型与算法的可行性, 并将优化结果与正常情况下基于相同客运任务的定制公交线路优化方案进行了对比。研究结果表明: 在完成相同客运任务的情况下, 应急定制公交线路所需车辆数比正常情况下多2辆, 车辆的总运行时长也比正常情况下增加6.997 h; 正常情况下的定制公交线路优化模型不能直接用于突发公共卫生事件场景, 针对应急场景构建的定制公交线路优化模型与算法能从众多备选方案中快速计算得到优化方案, 不仅能满足防疫要求, 还能满足人们的出行需求。
- 应急交通 /
- 突发公共卫生事件 /
- 定制公交 /
- 线路 /
- 优化 /
- 遗传算法
Abstract: Considering the requirements of epidemic prevention and control under public health emergencies, an optimization method of emergency customized bus routes was constructed. The closed areas and road sections in the city were screened and set as the emergency customized bus forbidden areas. Taking the occupancy rate of passengers not exceeding the safety threshold as the constraint, and considering the matching of supply and demand, the optimization model of public health emergency customized bus route was constructed by minimizing the total running time of all emergency customized buses. The genetic algorithm was designed to solve the model. The chromosome was encoded by the three-segment hybrid coding method. The three chromosomes were composed of the customized bus parking lot number, boarding station number and alighting station number. The chromosomes were decoded by using the greedy strategy. A simulation case was used to verify the feasibilities of the optimization model and algorithm, and the optimization results were compared with the customized bus route optimization scheme based on the same passenger transport task under the normal circumstance. Research result shows that the number of vehicles required for the emergency customized bus route under public health emergencies is 2 more than that under the normal circumstance. The total travel time of vehicles increases by 6.997 h compared with that under the normal circumstance. The customized bus route optimization model under the normal circumstance cannot be directly used in public health emergency scenarios. The optimization model and algorithm of customized bus route based on the emergency scenario can obtain the optimized scheme from many alternatives through the fast calculation. It can not only meet the requirement of epidemic prevention, but also meet people's travel needs under public health emergencies.
- emergency traffic /
- public health emergency /
- customized bus /
- route /
- optimization /
- genetic algorithm

HTML全文

图 1 路网示意

Figure 1. Schematic of road network

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图 2 算法流程

Figure 2. Algorithm flow

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图 3 筛查后的路网示意

Figure 3. Schematic of road network after screening

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图 4 疫情影响下应急定制公交运行时长分布

Figure 4. Running time distribution of emergency customized buses under impact of epidemic

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图 5 疫情影响下乘客上座率分布

Figure 5. Distribution of passenger attendance rates under impact of epidemic

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图 6 正常情况下定制公交运行时间分布

Figure 6. Running time distribution of customized buses under normal circumstance

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图 7 正常情况下乘客上座率分布

Figure 7. Distribution of passenger attendance rate under normal circumstance

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图 8 疫情影响下安全阈值系数与所需车辆总数的关系

Figure 8. Relationship between safety threshold coefficient and total number of vehicles needed under influence of epidemic

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图 9 疫情影响下安全阈值系数与车辆运行时长的关系

Figure 9. Relationship between safety threshold coefficient and vehicle running time under influence of epidemic

下载: 全尺寸图片幻灯片

表 1 第1阶段解码结果

Table 1. Decoding result of first stage

停车场编号	上车站点
a	11-10-7
b	8-5-3-6
c	4-15-12-9
d	2-1-13-14

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表 2 第2阶段解码结果

Table 2. Decoding result of second stage

停车场编号	车辆编号	上车站点
a	1	11
	2	10
	3	7
b	1	8-5-3
b	2	6
c	1	4
	2	15
	3	12
	4	9
d	1	2-1
	2	13
	3	14

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表 3 站点信息

Table 3. Site information

上车站点编号	下车站点编号	乘客人数
1	38	8
2	24	7
3	28	12
4	21	6
5	32	4
6	37	3
7	34	5
8	25	4
9	23	7
10	39	6
11	40	8
12	22	6
13	26	4
14	27	8
15	31	5
16	33	6
17	29	9
18	30	4
19	35	11
20	36	7

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表 4 疫情影响下的求解结果

Table 4. Solution result under influence of epidemic

公交车编号	途经站点	运行时长/h	乘客数量/人	上座率/%	所有车辆的乘客平均上座率/%	各停车场参与服务的车辆数/veh
1	a-11-3-28-40-a	1.945	20	50.0	40.625	2
2	a-14-27-a	1.303	8	20.0		2
3	b-20-17-5-29-36-32-b	2.195	20	50.0		2
4	b-18-19-30-35-b	1.715	15	37.5		2
5	c-4-13-2-21-24-26-c	1.872	17	42.5		2
6	c-1-8-16-25-38-33-c	1.788	18	45.0		2
7	d-12-6-7-10-22-39-37-34-d	2.195	20	50.0		2
8	d-15-9-23-31-d	1.792	12	30.0		2
总计		14.805	130			8

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表 5 正常情况下的求解结果

Table 5. Solution result under normal circumstance

公交车编号	途经站点	运行时长/h	乘客数量/人	上座率/%	所有车辆的乘客平均上座率/%	各停车场参与服务的车辆数/veh
1	a-17-29-a	0.649	9	22.50	54.167	1
2	b-19-12-7-15-11-35-34-40-31-22-b	2.031	35	87.50		3
3	b-14-1-8-5-16-6-2-25-32-37-24-27-33-38-b	1.914	40	100.00
4	b-3-4-9-21-23-28-b	1.265	25	62.50
5	c-13-18-26-30-c	0.933	8	20.00		1
6	d-20-10-39-36-d	1.016	13	32.50		1
总计		7.808	130			6

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表 6 疫情影响下α=0.45时的求解结果

Table 6. Solution result under influence of epidemic when α=0.45

公交车编号	途经站点	运行时长/h	乘客数量/人	上座率/%	所有车辆的乘客平均上座率/%	各停车场参与服务的车辆数/veh
1	a-19-18-6-30-37-35-a	2.038	18	45.00	32.500	1
2	b-7-4-34-21-b	1.476	11	27.50		4
3	b-11-17-40-29-b	1.822	17	42.50
4	b-12-15-16-33-22-31-b	1.797	17	42.50
5	b-5-32--b	0.964	4	10.00
6	c-13-10-26-39-c	1.360	10	25.00		2
7	c-3-28-c	1.267	12	30.00		2
8	d-1-9-23-38-d	1.665	15	37.50		3
9	d-2-14-24-27-d	1.915	15	37.50
10	d-20-8-25-36-d	1.401	11	27.50
总计		15.705	130			10

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表 7 疫情影响下α=0.40时的求解结果

Table 7. Solution result under influence of epidemic when α= 0.40

公交车编号	途经站点	运行时长/h	乘客数量/人	上座率/%	所有车辆的乘客平均上座率/%	各停车场参与服务的车辆数/veh
1	a-9-14-27-23-a	1.765	15	37.50	32.500	5
2	a-13-4-15-31-26-21-a	1.615	15	37.50
3	a-1-11-38-40-a	1.681	16	40.00
4	a-16-33-a	0.871	6	15.00
5	a-3-28-a	1.442	12	30.00
6	b-12-6-7-22-37-34-b	1.599	14	35.00		1
7	c-17-8-35-29-c	1.483	13	32.50		1
8	d-19-35-d	1.151	11	27.50		3
9	d-20-10-39-36-d	1.658	13	32.50
10	d-2-5-18-24-30-32-d	1.615	15	37.50
总计		14.880	130			10

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表 8 疫情影响情况下α=0.35时的求解结果

Table 8. Solution result under influence of epidemic when α= 0.35

公交车编号	途经站点	运行时长/h	乘客数量/人	上座率/%	所有车辆的乘客平均上座率/%	各停车场参与服务的车辆数/veh
1	a-15-4-31-21-a	1.301	11	27.50	25.000	4
2	a-8-1-38-25-a	1.467	12	30.00
3	a-9-6-5-37-32-23-a	1.699	14	35.00
4	a-18-11-30-40-a	1.492	12	30.00
5	b-20-16-33-36-b	1.508	13	32.50		4
6	b-13-26-b	0.939	4	10.00
7	b-3-28-b	1.342	12	30.00
8	b-12-22-b	0.796	6	15.00
9	c-17-29-c	1.069	9	22.50		3
10	c-14-7-27-34-c	1.708	13	32.50
11	c-2-24-c	1.037	7	17.50
12	d-10-39-d	1.096	6	15.00		2
13	d-19-35-d	1.151	11	27.50		2
总计		16.605	130			13

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表 9 疫情影响下α=0.30的求解结果

Table 9. Solution result under influence of epidemic when α= 0.30

公交车编号	途经站点	运行时长/h	乘客数量/人	上座率/%	所有车辆的乘客平均上座率/%	各停车场参与服务的车辆数/veh
1	a-16-8-25-33-a	1.260	10	25.00	23.214	4
2	a-12-22-a	0.846	6	15.00
3	a-14-27-a	1.303	8	20.00
4	a-13-1-26-38-a	1.567	12	30.00
5	b-19-35-b	1.376	11	27.50		2
6	b-17-29-b	1.144	9	22.50		2
7	c-9-23-c	0.962	7	17.50		6
8	c-4-21-c	0.996	6	15.00
9	c-20-36-c	1.087	7	17.50
10	c-10-15-31-39-c	1.351	11	27.50
11	c-3-28-c	1.267	12	30.00
12	c-18-6-37-30-c	1.312	7	17.50
13	d-2-7-24-34-d	1.517	12	30.00		2
14	d-5-11-40-32-d	1.617	12	30.00		2
总计		17.605	130			14

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