基于混合需求的高铁快巴动态线路规划方法研究

doi:10.3976/j.issn.1002-4026.20230050

摘要/Abstract

摘要：

为满足旅客在高铁站的接驳及疏散需求,增强高铁站作为城市综合交通枢纽的作用,建立基于提前预约和实时混合需求的高铁快巴动态线路规划模型。在运营开始前,基于提前预约需求,兼顾公交公司的运营成本和乘客出行时间成本,建立线路规划模型;利用小生境技术对传统遗传算法进行改进,设计算法求解。运营开始后,允许实时需求插入既定路线,引入临时站点,以最小化系统变动成本为目标,建立整数规划模型决策动态线路规划方案。应用本方法在北京市北太平庄街道区域随机生成并求解30组需求算例。结果显示,该模型可以在两阶段生成最优的高铁快巴线路方案满足混合需求;与传统遗传算法相比,小生境遗传算法有效避免了算法早熟,运算结果更优,模型和算法具有可行性。

关键词: 高铁快巴, 混合需求, 动态线路, 临时站点

Abstract:

To meet the needs of passengers for connection and evacuation at high-speed rail stations and enhance the role of high-speed rail stations as urban comprehensive transportation hubs, a dynamic route planning model of a high-speed rail feeder bus is established based on mixed demand that includes reservation and real-time demands. Based on the reservation demand, considering the operation cost of a bus company as well as the travel time cost, the route planning model is established before the start of operation. An improved genetic algorithm was designed using niche methods to solve the problem. After the start of operation, real-time demand can be inserted into the established vehicle route with temporary stations. To realize a dynamic route planning scheme, an integer planning model is established to minimize the variable cost of the system. Using the proposed method,30 demand groups were randomly generated and solved in the Beitaipingzhuang street area, Beijing. Results show that the model can generate an optimal dynamic route planning scheme for a high-speed rail feeder bus in two periods to satisfy the mixed demand. Compared with traditional genetic algorithm, niche genetic algorithm can effectively avoid premature and obtain better results, thus confirming the feasibility of the proposed model and the niche genetic algorithm.

Key words: high-speed rail feeder bus, mixed demand, dynamic route, temporary station

中图分类号:

U491

王瑜琼. 基于混合需求的高铁快巴动态线路规划方法研究[J]. 山东科学, 2024, 37(1): 118-127.

WANG Yuqiong. Dynamic route planning method for a high-speed rail feeder bus based on mixed demand[J]. Shandong Science, 2024, 37(1): 118-127.

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站点编号	虚拟站点编号	预约数量	期望出发时间窗	最晚到达时间
1	1	2	[7:03,7:05]	7:58
2	2	2	[7:05,7:07]	8:00
3	3	1	[7:13,7:15]	8:06
4	4	2	[7:18,7:20]	8:10
5	5	2	[7:14,7:16]	8:10
6	6	1	[7:08,7:10]	8:15
	7	1	[7:07,7:09]	8:12
7	8	2	[7:03,7:05]	7:58
	9	2	[7:06,7:08]	8:00
8	10	1	[7:20,7:22]	8:20
9	11	2	[7:05,7:07]	7:57
	12	1	[7:25,7:27]	8:29
10	13	1	[7:11,7:13]	8:06
11	14	1	[7:16,7:18]	8:02
12	15	2	[7:15,7:17]	8:12
	16	1	[7:13,7:15]	8:10
13	17	2	[7:15,7:17]	8:15
总计		26

编号	生成时间	预约数量	周边固定站点
1	7:10	2	1、2
2	7:10	2	12
3	7:20	1	2、4
4	7:25	1	9、11

对比试验	参数取值	系统成本的平均值/元	实时需求响应率的平均值/%	人均成本的平均值/元
A组	交叉概率0.9、变异概率0.2、种群数100、最大进化代数200、峰半径5	783.6	89.4	23.5
B组	交叉概率0.9、变异概率0.2、种群数100、最大进化代数200	811.5	89.4	24.3
C组	同A组	757.8	54.6	24.5

车辆编号	核载/人	计划载客/人	线路长度/km	发车时刻	计划到达时刻	实际到达时刻	响应实时需求人数
1	14	11	15.9	7:00	7:52	7:54	2
2	14	11	15.4	7:10	8:01	8:05	3
3	9	4	14.9	7:20	8:07	8:10	1
总计	37	26	46.2				6