网联环境下考虑非优先车辆延误的公交优先信号控制方法

谭百宏; 邱志军; 张祎; 何书贤

doi:10.3963/j.jssn.1674-4861.2022.03.009

网联环境下考虑非优先车辆延误的公交优先信号控制方法

doi: 10.3963/j.jssn.1674-4861.2022.03.009

谭百宏^{1, 2,},
邱志军^1, ,,
张祎^{1, 2},
何书贤^{1, 2}

1.
武汉理工大学智能交通系统研究中心武汉 430063
2.
武汉理工大学交通与物流工程学院武汉 430063

基金项目:

国家自然科学基金项目 52172332

道路交通安全公安部重点实验室开放课题项目 2020ZDSYSKFKT06

详细信息

作者简介:
谭百宏（1996—），硕士研究生.研究方向：交通自适应控制.E-mail: 292849@whut.edu.cn

通讯作者:
邱志军（1979—），博士，教授.研究方向：智能交通系统、车路协同. E-mail: zqiu@whut.edu.cn

中图分类号: U491.4
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- 被引次数: 0
出版历程
- 收稿日期: 2022-01-13
- 网络出版日期: 2022-07-25

A Signal Control Method for Bus Priority Considering the Delay of Non-priority Vehicles in a Connected-vehicle Environment

TAN Baihong^{1, 2
,},
QIU Zhijun^{1
, ,},
ZHANG Yi^{1, 2},
HE Shuxian^{1, 2}

1.
Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430063, China
2.
School of Transportation and Logistics, Wuhan University of Technology, Wuhan 430063, China

摘要

摘要: 网联环境具有数据采集和交互方面的优势，能更精确地评估交通需求，更科学地实施交通管控措施。根据公交车与非优先车辆权重及延误分布差异，研究了考虑非优先车辆延误的公交优先单点信号控制方法。利用交叉口车辆轨迹数据计算轨迹样本到达率参数，根据车辆到达交叉口的分布特征构建各相位的车辆到达率概率函数，并采用极大似然估计预测到达率，基于交通流冲击波模型分别计算出各相位的排队波、驶离波和消散波波速。公交车数量少权重较高且网联化程度高，利用基于冲击波的时距图推导延误表达式；而非优先车辆数量多单车权重低且网联化程度低，利用基于到达率的定数理论推导延误表达式。按乘员数对公交车延误值和非优先车辆延误值进行加权，以加权延误最小为目标函数建立了混合整数线性规划模型，解得相位时长整数解，并反馈到信号机系统实现公交优先自适应信号控制。以武汉市车城北路与东风大道交叉口为对象，采集不同时段交叉口流量数据，利用SUMO软件开展仿真实验，结果表明：相比优化前，低、中、高流量情况下公交车单车平均延误时间分别减少25.63%、25.25%、18.32%；同等条件下平均每周期非优先车辆延误时间分别减少8.80%、4.68%、1.99%；同等条件下平均每周期加权延误时间分别减少20.98%、9.39%、12.70%。证明所提方法能较好地适配交通需求，且流量较低时效果最好。
- 智能交通 /
- 公交优先 /
- 交通延误 /
- 混合整数线性规划
Abstract: A connected-vehicle(CV)environment facilitates the collection of traffic data and the interactions among road users; therefore, it can contribute to more accurate evaluation of travel demand and traffic control. This paper investigates a signal control method at a single intersection for bus priority based on the weights for and delay distributions of bus and the other, non-priority vehicles. First, the arrival rates are calculated based on the trajectory data of connected buses and non-priority vehicles in the intersection, and the corresponding probability function of each phase is developed according to the distribution pattern of vehicle arrivals, based on which the probability of arrival rate is calculated using a maximum likelihood estimation model. Second, the wave speed of queuing, discharge, and departure are calculated respectively, using a traffic flow shock wave model. Third, the model specification for bus delay is carried out using the time-distance diagram of the shock-wave velocity, based on the fact that the number of buses in the traffic flow is less than regular vehicles while their weights are higher. Meanwhile, the model specification for non-priority vehicles is carried out using the Fixed Number Theory based on vehicles' arrival rate, considering the number of non-priority vehicles in traffic flow is larger while the weight of non-priority vehicle is lower, and most of them are not connected. The weighted delay of the intersection is calculated based on the number of passengers in vehicles. Finally, a mixed integer linear programming model is established to minimize the weighted delay, whose solution will then be used for optimizing signal control systems. To check the validity of the proposed method, a case study of the intersection of North Checheng Road and Dongfeng Avenue in the City of Wuhan is carried out. Traffic flow data of buses and non-priority vehicles at the intersection in different periods are collected, and an simulation experiment is accomplished based on Simulation of Urban Mobility(SUMO)Package. Experimental results show that the average delays for buses reduce by 25.63%, 25.25%, and 18.32%, under the scenario of low, medium, and high traffic flow rate, respectively. Compared with those before optimization, the average delays for non-priority vehicles in a single cycle under the same scenarios reduce by 8.80%, 4.68%, and 1.99%, respectively; and the average weighted delay in a single cycle under the same scenarios reduce by 20.98%, 9.39%, and 12.70%, respectively. The above results show that the proposed method is suitable and performs better in different traffic settings.
- intelligent transportation system /
- transit bus signal priority /
- traffic delay /
- mixed integer linear programing

HTML全文

图 1 智能网联交叉口示意图

Figure 1. Intelligent connected intersection diagram

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图 2 公交车时空轨迹图

Figure 2. Illustration of bus trajectory

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图 3 时间-车辆数曲线图

Figure 3. Time-vehicle number diagram

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图 4 实验场景图

Figure 4. Experimental scenario

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图 5 优化前后延误对比

Figure 5. Delay comparison of before and after optimization

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表 1 公交延误预测精度表

Table 1. Bus delay estimation accuracy chart 单位: s

交通流状况	延误差均值	最大延误差	延误差标准差
低流量	1.35	6.54	5.67
中流量	1.48	3.52	1.93
高流量	2.27	5.64	3.49

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表 2 加权延误预测精度表

Table 2. Weighted delay estimation accuracy chart

组别	预计延误比	实测延误比	比值
1	1.13	1.19	0.95
2	2.07	2.24	0.92
3	1.24	1.31	0.94
4	1.37	1.18	1.16
5	3.69	3.87	0.95
6	4.62	4.45	1.04

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表 3 仿真参数设置

Table 3. Simulation parameters

参数	取值或分布
网联公交通信距离/m	500
Krauss最小车头时距/s	N(1.1, 0.2)
Krauss静止安全距离/m	N(1.5, 0.5)
Krauss控制参数σ	N(0.5, 0.2)
社会车辆长度/m	5
公交车长度/m	12
公交车静止安全距离/m	3

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表 4 交通流量参数表

Table 4. Traffic flow parameters chart

交通流状况	组别	车辆类型	流量/(veh/h)						饱和度/%
交通流状况	组别	车辆类型	西北	东南	西南	东北	合计	总流量	饱和度/%
	1	社会车	352	385	266	214	1 217	1 280	31.26
	1	公交车	25	26	6	6	63	1 280	31.26
低流量	2	社会车	382	410	272	220	1 284	1 350	32.89
	2	公交车	28	25	6	7	66	1 350	32.89
	3	社会车	377	416	258	207	1 258	1 324	31.80
	3	公交车	27	27	6	6	66	1 324	31.80
	4	社会车	532	615	380	356	1 883	1 979	45.65
	4	公交车	39	42	8	7	96	1 979	45.65
中流量	5	社会车	526	662	402	368	1 958	2 062	46.94
	5	公交车	42	46	7	9	104	2 062	46.94
	6	社会车	545	635	406	362	1 948	2 047	47.86
	6	公交车	39	41	9	10	99	2 047	47.86
	7	社会车	862	693	479	394	2 428	2 559	66.06
	7	公交车	58	53	10	10	131	2 559	66.06
高流量	8	社会车	882	782	505	404	2 573	2 703	68.53
	8	公交车	54	54	11	11	130	2 703	68.53
	9	社会车	933	837	531	452	2 753	2 883	72.13
	9	公交车	56	51	11	12	130	2 883	72.13

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