半自动驾驶公交车辆编组与调度优化

代壮; 陈汐; 马晓磊

doi:10.13700/j.bh.1001-5965.2019.0627

半自动驾驶公交车辆编组与调度优化

doi: 10.13700/j.bh.1001-5965.2019.0627

代壮¹,
陈汐¹,
马晓磊^{1, 2, ,}

1.
北京航空航天大学交通科学与工程学院, 北京 100083
2.
北京航空航天大学大数据科学与脑机智能高精尖创新中心, 北京 100083

基金项目:

国家自然科学基金 U1811463

国家自然科学基金 61773036

北京市自然科学基金 9172011

详细信息

作者简介:
代壮  男, 博士研究生。主要研究方向:智能网联交通

陈汐  男, 博士研究生。主要研究方向:公交调度优化

马晓磊  男, 博士, 副教授, 博士生导师。主要研究方向:智能交通系统

通讯作者:
马晓磊, E-mail: xiaolei@buaa.edu.cn

中图分类号: U121
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- 被引次数: 0
出版历程
- 收稿日期: 2019-12-16
- 录用日期: 2020-02-28
- 网络出版日期: 2020-12-20

Semi-autonomous driving bus platooning and scheduling optimization

DAI Zhuang¹,
CHEN Xi¹,
MA Xiaolei^{1, 2
, ,}

1.
School of Transportation Science and Engineering, Beihang University, Beijing 100083, China
2.
Big Data Brain Computing, Beihang University, Beijing 100083, China

Funds:

National Natural Science Foundation of China U1811463

National Natural Science Foundation of China 61773036

Beijing Natural Science Foundation 9172011

More Information

Corresponding author: MA Xiaolei, E-mail: xiaolei@buaa.edu.cn

摘要

摘要:
半自动驾驶公交车辆编组是指半自动驾驶公交单元通过车联网技术连接在一起，实现车辆协同驾驶和车辆容量动态设计的车辆组织技术。以半自动驾驶公交车辆编组为出发点，建立编组车辆动态运行模型，分析编组车辆到离站时间、乘客上下车过程、车辆容量限制和车载乘客数量变化等。在此基础上，以车辆运营成本和乘客候车时间成本之和为目标函数，以车辆编组大小和发车时刻为决策变量，建立半自动驾驶公交车辆调度优化模型。提出改进的遗传算法高效求解模型。以杭州55路公交线路为实证案例，仿真结果表明：相比于传统人工驾驶公交的车辆调度，基于半自动驾驶公交的车辆调度能降低29.2%的车辆运营成本和18.2%的乘客候车时间成本，所得结果证实了所建模型优化半自动驾驶公交车辆调度的有效性。
- 智能交通系统 /
- 半自动驾驶 /
- 车辆编组 /
- 公交车辆调度 /
- 遗传算法
Abstract:
Semi-autonomous driving bus platooning refers to the vehicle organization technology that connects bus units together through vehicle communication technologies to realize coordinated driving of vehicles and dynamic design of vehicle capacity. Based on semi-autonomous driving us platooning, a dynamic bus operation model is first proposed to model bus arrival and departure time at stops, passenger dwelling process, bus capacity constraint and onboard passenger dynamics. On this basis, a semi-autonomous driving bus scheduling optimization model is proposed to jointly optimize platooning size and bus dispatching time with the objective of the sum of the optimizing operating cost and passenger waiting time cost. An improved genetic algorithm is proposed to solve the model efficiently. The model is validated using a real-world example of bus route 55, Hangzhou, China. Simulation results show that the proposed semi-autonomous driving bus scheduling can reduce bus operating cost by 29.2% and reduce passenger waiting time cost by 18.2%, when compared with conventional human-driven bus scheduling. The result verifies the efficiency of the proposed model in scheduling semi-autonomous driving bus.
- intelligent transportation system /
- semi-autonomous driving /
- bus platooning /
- bus scheduling /
- genetic algorithm

HTML全文

图 1 半自动驾驶公交车辆编组

Figure 1. Semi-autonomous driving bus platooning

下载: 全尺寸图片幻灯片

图 2 遗传算法基因、染色体及种群

Figure 2. Gene, chromesome, and population of genetic algorithm

下载: 全尺寸图片幻灯片

图 3 染色体交叉及变异

Figure 3. Chromesome crossover and mutation

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图 4 乘客等候时间分布

Figure 4. Passenger waiting time distribution

下载: 全尺寸图片幻灯片

图 5 车辆轨迹与车辆编组

Figure 5. Bus trajectory and platooning

下载: 全尺寸图片幻灯片

表 1 杭州55路公交运营数据

Table 1. Operation data of route 55, Hangzhou

站点	t_j/s	t_j标准差/s	λ_j/(人次·h^-1)
1	32.08	6.06	16
2	162.99	25.46	125
3	72.12	14.58	55
4	95.17	12.30	48
5	218.56	24.55	74
6	171.42	18.28	80
7	63.76	10.29	35
8	82.04	13.58	50
9	108.02	23.56	129
10	154.06	21.87	30
11	110.12	16.93	53
12	241.00	36.93	47
13	44.15	6.93	52
14	249.82	36.34	43
15	141.47	22.19	32
16	149.49	27.07	9
17	71.04	7.40	4
18	116.51	17.40	14
19	133.10	10.10	10
20	140.60	19.51	3
21	41.77	9.52	0
注：t_j为车辆在站台j-1和站台j间的平均行驶时间，单位为s。

下载: 导出CSV

表 2 不同优化算法性能比较

Table 2. Performance comparison of different optimization algorithms

场景	算法	计算时间/min	目标函数值/元
场景1	遗传算法	0.7	14 702.6
	滚动时间窗	3.2	15 462.1
	滚动时间窗+动态规划	2.1	15 462.1
场景2	遗传算法	1.5	11 362.8
	滚动时间窗	6.2	11 612.4
	滚动时间窗+动态规划	4.7	11 612.4

下载: 导出CSV

表 3 成本分析

Table 3. Cost analysis

场景	车辆运营成本/元	乘客候车时间成本/元	总成本/元
场景1	6 048.0	8 654.6	14 702.6
场景2	4 280.0	7 082.8	11 362.8

下载: 导出CSV

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