
CLC number:
On-line Access: 2025-08-27
Received: 2024-10-15
Revision Accepted: 2024-12-05
Crosschecked: 2025-08-28
Cited: 0
Clicked: 1035
Citations: Bibtex RefMan EndNote GB/T7714
Jianqi LI, Rongjun CHENG. A real-time adaptive signal control method for multi-intersections in mixed connected vehicle environments[J]. Journal of Zhejiang University Science A,in press.Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/jzus.A2400488 @article{title="A real-time adaptive signal control method for multi-intersections in mixed connected vehicle environments", %0 Journal Article TY - JOUR
混合网联车辆环境下的多交叉实时自适应信号控制方法机构:宁波大学,海运学院,中国宁波,315211 目的:传统基于路侧交通传感器采集数据的信号配时参数优化方法不能及时地获取实时的交通流运行状况,而网联车辆收集的实时数据可用于优化交叉口的信号控制参数,从而提高交通运行效率。本研究设计了一种适用于低渗透率多交叉口主干道的实时自适应信号控制方法。通过利用部分网联车辆收集的车辆到达信息,以快速确定最优信号相位和时序(SPaT),优化干线上各交叉口信号配时参数以提升交通运行效率。 创新点:1.设计了适用于低网联车渗透水平条件下的干线自适应信号控制方法,有效利用部分网联车辆采集的实时交通流数据优化信号配时参数,减少干线上车辆行驶延误;2.所提出的自适应信号控制方法考虑了网联车辆的探测距离,并在SUMO仿真软件中进行了模拟实验验证其有效性。 方法:1.通过所设计的干线自适应信号控制策略的整体框架,建立完整的适用于部分网联车辆环境下的信号参数优化控制流程(图2);2.通过建立的自适应信号控制优化模型,利用网联车辆采集到的实时交通流信息优化每个信号周期的信号配时方案(公式(1)~(5));3.通过微观交通仿真软件SUMO对所提出的干线自适应信号控制策略进行验证,并将所提信号控制策略的性能与传统方法进行比较(表2~4,图4)。 结论:1.利用网联车辆收集的实时交通流数据动态优化主干道上各交叉口的SPaT可以有效的减少干线上车辆的行驶延误;2.在智能网联环境下,只利用部分网联车辆采集的数据即可满足交叉口信号参数优化所需的数据量;3.网联车辆的探测距离比网联车辆的渗透率对所提策略控制性能的影响更大。 关键词组: Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article
Reference[1]BingB,CarterA,1995.Scoot: the World’s Foremost Adaptive Traffic Control System.UK and International Press,Dorking,UK, p.176-180. ![]() [2]DasD,AltekarNV,HeadKL,2023.Priority-based traffic signal coordination system with multi-modal priority and vehicle actuation in a connected vehicle environment.Transportation Research Record: Journal of the Transportation Research Board,2677(5):666-681. ![]() [3]FengYH,HeadKL,KhoshmaghamS,et al.,2015.A real-time adaptive signal control in a connected vehicle environment.Transportation Research Part C: Emerging Technologies,55:460-473. ![]() [4]FuTT,WangLY,GargS,et al.,2024.Adaptive signal light timing for regional traffic optimization based on graph convolutional network empowered traffic forecasting.Information Fusion,103:102072. ![]() [5]GuoQQ,LiL,BanXG,2019.Urban traffic signal control with connected and automated vehicles: a survey.Transportation Research Part C: Emerging Technologies,101:313-334. ![]() [6]HuangLB,QuXH,2023.Improving traffic signal control operations using proximal policy optimization.IET Intelligent Transport Systems,17:592-605. ![]() [7]JiQ,LyuH,YangH,et al.,2023.Bifurcation control of solid angle car-following model through a time-delay feedback method.Journal of Zhejiang University-SCIENCE A (Applied Physics and Engineering),24(9):828-840. ![]() [8]KodiJH,AliMS,KitaliAE,et al.,2024.Influence of adaptive signal control technology (ASCT) on severity of intersection-related crashes.Journal of Transportation Safety & Security,16(4):375-389. ![]() [9]LiJQ,YangH,ChengRJ,et al.,2024.A dynamic temporal and spatial speed control strategy for partially connected automated vehicles at a signalized arterial.Physica A: Statistical Mechanics and Its Applications,653:130099. ![]() [10]LiYR,PengLQ,2024.Elevating adaptive traffic signal control in semi-autonomous traffic dynamics by using connected and automated vehicles as probes.IET Intelligent Transport Systems,18(6):1016-1030. ![]() [11]LiYS,ZhangY,LiXD,et al.,2024.Regional multi-agent cooperative reinforcement learning for city-level traffic grid signal control.IEEE/CAA Journal of Automatica Sinica,11(9):1987-1998. ![]() [12]LiangX,GulerSI,GayahVV,2023.Decentralized arterial traffic signal optimization with connected vehicle information.Journal of Intelligent Transportation Systems,27(2):145-160. ![]() [13]LittleJDC,KelsonMD,GartnerNH,1981.Maxband: a program for setting signals on arteries and triangular networks.Transportation Research Record,795:40-46. ![]() [14]LoHK,2006.A reliability framework for traffic signal control.IEEE Transactions on Intelligent Transportation Systems,7(2):250-260. ![]() [15]MaWJ,LiXP,YuCH,et al.,2024.Arterial signal timing based on probe vehicle trajectories under cyclic stochastic demand.IEEE Transactions on Intelligent Transportation Systems,25(10):13375-13392. ![]() [16]MoZB,LiWZ,FuYJ,et al.,2022.CVLight: decentralized learning for adaptive traffic signal control with connected vehicles.Transportation Research Part C: Emerging Technologies,141:103728. ![]() [17]MohammadiR,RoncoliC,MladenovićMN,2021.Signalised intersection control in a connected vehicle environment: user throughput maximisation strategy.IET Intelligent Transport Systems,15(3):463-482. ![]() [18]SimsAG,DobinsonKW,1980.The Sydney coordinated adaptive traffic (SCAT) system philosophy and benefits.IEEE Transactions on Vehicular Technology,29(2):130-137. ![]() [19]WanCH,HwangMC,2018.Value-based deep reinforcement learning for adaptive isolated intersection signal control.IET Intelligent Transport Systems,12(9):1005-1010. ![]() [20]WangJD,JiangSC,QiuY,et al.,2021.Traffic signal optimization under connected-vehicle environment: an overview.Journal of Advanced Transportation,2021(1):3584569. ![]() [21]WangQZ,YuanY,YangXF,et al.,2021.Adaptive and multi-path progression signal control under connected vehicle environment.Transportation Research Part C: Emerging Technologies,124:102965. ![]() [22]WuZY,WatersonB,2022.Urban intersection management strategies for autonomous/connected/conventional vehicle fleet mixtures.IEEE Transactions on Intelligent Transportation Systems,23(8):12084-12093. ![]() [23]XuLH,LuJ,ZhanFP,et al.,2019.An adaptive signal control using connected-vehicle data.Proceedings of the Institution of Civil Engineers-Transport,172(2):102-110. ![]() [24]YangTJ,FanW,2024.Transit signal priority under connected vehicle environment: deep reinforcement learning approach.Journal of Intelligent Transportation Systems,in press. ![]() [25]YangXF,ChengY,ChangGL,2015.A multi-path progression model for synchronization of arterial traffic signals.Transportation Research Part C: Emerging Technologies,53:93-111. ![]() [26]ZhangKW,CuiZY,MaWJ,2024.A survey on reinforcement learning-based control for signalized intersections with connected automated vehicles.Transport Reviews,44(6):1187-1208. ![]() [27]ZhaoJ,YaoTY,ZhangC,et al.,2024.Signal control for overflow prevention at intersections using partial connected vehicle data.Transportmetrica A: Transport Science,in press. ![]() [28]ZhengX,ReckerW,2013.An adaptive control algorithm for traffic-actuated signals.Transportation Research Part C: Emerging Technologies,30:93-115. ![]() [29]ZhengX,ReckerW,ChuLY,2010.Optimization of control parameters for adaptive traffic-actuated signal control.Journal of Intelligent Transportation Systems,14(2):95-108. ![]() Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou
310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn Copyright © 2000 - 2025 Journal of Zhejiang University-SCIENCE | ||||||||||||||



ORCID:
Open peer comments: Debate/Discuss/Question/Opinion
<1>