[1]龙科军,高志波,吴〓伟,等.城市道路干线信号协调控制与车速引导集成优化[J].长安大学学报(自然科学版),2018,38(02):94-102.
　LONG Ke jun,GAO Zhi bo,WU Wei,et al.Integrated optimization for urban arterial traffic signal coordination andvehicle active speed guidance[J].Journal of Chang’an University (Natural Science Edition),2018,38(02):94-102.
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城市道路干线信号协调控制与车速引导集成优化()
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长安大学学报（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]

卷:
第38卷
期数:
2018年02期
页码:
94-102
栏目:
交通工程
出版日期:
2018-03-31

文章信息/Info

Title:
Integrated optimization for urban arterial traffic signal coordination andvehicle active speed guidance
作者:
龙科军; 高志波; 吴〓伟; 韩〓科; 段〓熙
（1. 长沙理工大学 智能道路与车路协同湖南省重点实验室,湖南 长沙 410114； 2. 伦敦帝国学院 土木与环境工程系,伦敦 SW7 2AZ; 3. 佛罗里达大学 土木与海岸工程系,佛罗里达 盖思斯威尔 FL32611）
Author(s):
LONG Kejun;  GAO Zhibo;  WU Wei;  HAN Ke;  DUAN Xi
(1. Hunan Key Laboratory of Smart Highway and Cooperative Vehicle Infrastructure System, ChangshaUniversity of Science and Technology, Changsha 410114, Hunan, China; 2. Department of Civil andEnvironmental Engineering, Imperial College London, London SW7 2AZ, UK; 3. Department ofCivil and Coastal Engineering, University of Florida, Gainesville FL32611, Florida, USA)
关键词:
交通工程; 信号协调; 粒子群算法; 速度引导; 车路协同
Keywords:
traffic engineering;  signal coordination;  particle swarm optimization;  speed guidance;  cooperative vehicle infrastructur
文献标志码:
A
摘要:
针对当前干线信号协调控制为基于车辆到达驱动的被动响应型控制的特点与不足，提出一种车联网环境下干线信号协调控制与车辆速度主动引导的协同优化方法。优化原理为：考虑车辆信号控制系统双向通信的环境下，在干线协调控制的基础上引入速度引导来调节车辆到达交叉口时刻，以避免车辆在红灯期间到达交叉口，减少停车次数并提高协调控制系统通行效率。首先，选择双向绿波带宽模型作为协调控制方案的基础；依据下游交叉口当前信号灯色和剩余时长，将车辆引导分为红灯引导和绿灯引导，分别给出最佳车速方程；基于最佳车速给出车辆到达交叉口时刻、交叉口延误和停车次数的估计方法。然后，以车辆引导速度和干线绿波相位差为控制变量，以绿波带宽最大、车辆延误与停车次数最小为目标，建立集成车速引导和干线绿波的优化模型；应用粒子群算法的多目标搜索算法对优化模型求解。选择长沙市湘江中路4个连续交叉口开展案例研究，分别应用普通干线绿波Maxband模型和提出的集成模型设计信号控制方案，并以VISSIM仿真平台进行效率评价。结果表明：集成模型能同时调节相位差和车辆速度，增大绿波带宽，减少停车次数；仿真周期内与Maxband模型相比，集成模型的上行和下行方向平均延误分别降低了24.8%和31.1%，平均停车次数分别减少了37.6%和41.7%，基于车速引导的集成模型能显著提高干线协调控制的效率。
Abstract:
In light of the characters and the deficiencies of passive response control based on vehicle arrival driven by the signal coordination control of present trunk lines, a collaborative optimization method for signal coordination control of trunk lines and active guidance of vehicle speed under the environment of vehicle network was proposed. The optimization principle was as follows, under the environment of twoway communication of vehiclesignal control system, speed guidance was introduced on the basis of coordination control of trunk lines to adjust vehicles arrival time at intersection, so as to avoid vehicles arriving at intersection during red light, reduce the number of parking and improve the efficiency of coordination control system. Firstly, the twoway green wave bandwidth was chosen as the basis of coordination control scheme, and according to the present signal color and the remaining time of the downstream intersection, the vehicle guidance was divided into red light guidance and green light guidance, and the optimal speed equations were given respectively; based on the optimal speed, the estimation method of vehicles arrival time, intersection delay and stopping number were given. Then the optimization model of integrated vehicle speed guidance and arterial green wave was established, in which the guiding speed and the phase difference of arterial green wave were taken as control variables, the maximization of bandwidth of green wave and the minimization of delays and stops phase difference of arterial green wave were taken as controls. The multiobjective search algorithm of particle swarm optimization was used to solve the optimization model. Four consecutive intersections of Xiangjiang Middle Road in Changsha City were selected to carry out case studies. The signal control scheme was designed by using the green wave Maxband model of the common arterial and the proposed integrated model respectively, and the efficiency was evaluated on the basis of VISSIM simulation platform. The results show that the proposed integrated model can adjust the phase difference and vehicle speed at the same time, increase the green wave bandwidth and reduce the parking times. In the simulation cycle, the average delay of uplink and downlink is reduced by 24.8% and 31.1% respectively, and the average parking times are reduced by 37.6% and 41.7% respectively， compared with Maxband model. The integrated model based on speed guidance can significantly improve the efficiency of coordination control of arterial lines. 4 tabs, 7 figs, 21 refs.

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更新日期/Last Update: 2018-04-03