Hierarchical adaptive cross‐coupled control of traffic signals and vehicle routes in large‐scale road network

IF 8.5 1区 工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Yizhuo Chang, Yilong Ren, Han Jiang, Daocheng Fu, Pinlong Cai, Zhiyong Cui, Aoyong Li, Haiyang Yu
{"title":"Hierarchical adaptive cross‐coupled control of traffic signals and vehicle routes in large‐scale road network","authors":"Yizhuo Chang, Yilong Ren, Han Jiang, Daocheng Fu, Pinlong Cai, Zhiyong Cui, Aoyong Li, Haiyang Yu","doi":"10.1111/mice.13508","DOIUrl":null,"url":null,"abstract":"Traffic signal timing and vehicle routing have been empirically demonstrated as the two most promising paradigms for network‐level urban road traffic management. However, mainstream studies based on Wardrop's theory continues to treat these two modules separately without achieving effective coupling. Optimization‐based methods face the challenge of increasing computational complexity as urban scales continue to expand, constrained to small‐scale road networks. To address the above challenges, this paper proposes HAC3, a hierarchical adaptive cross‐coupled control method for network‐wide traffic management. HAC3 utilizes a rolling horizon architecture, comprising a fast update stage and a slow update stage. The core of the slow update stage is a spatiotemporal superposition vehicle route planning (SSP) module, which assigns the optimal route to each connected vehicle (CV) based on the road network state and the traffic signal timing of each intersection, and clarifies priority in right‐of‐way allocation to avoid falling into local optimal. The fast update stage is used for multi‐intersection adaptive traffic signal control (TSC), taking the intersection state and vehicle routes as inputs to optimize the signal timing scheme. Through the asynchronous cross‐coupling optimization of the two stages, the road network efficiency can be improved while ensuring equilibrium. Experimental results show that HAC3 achieves superior convergence performance on both synthetic and real‐world road network data sets, outperforming baseline methods and proving its scalability to large‐scale road networks. Plug‐and‐play experiments indicate the proposed HAC3 framework can integrate with other mainstream signal control models.","PeriodicalId":156,"journal":{"name":"Computer-Aided Civil and Infrastructure Engineering","volume":"3 1","pages":""},"PeriodicalIF":8.5000,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer-Aided Civil and Infrastructure Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1111/mice.13508","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0

Abstract

Traffic signal timing and vehicle routing have been empirically demonstrated as the two most promising paradigms for network‐level urban road traffic management. However, mainstream studies based on Wardrop's theory continues to treat these two modules separately without achieving effective coupling. Optimization‐based methods face the challenge of increasing computational complexity as urban scales continue to expand, constrained to small‐scale road networks. To address the above challenges, this paper proposes HAC3, a hierarchical adaptive cross‐coupled control method for network‐wide traffic management. HAC3 utilizes a rolling horizon architecture, comprising a fast update stage and a slow update stage. The core of the slow update stage is a spatiotemporal superposition vehicle route planning (SSP) module, which assigns the optimal route to each connected vehicle (CV) based on the road network state and the traffic signal timing of each intersection, and clarifies priority in right‐of‐way allocation to avoid falling into local optimal. The fast update stage is used for multi‐intersection adaptive traffic signal control (TSC), taking the intersection state and vehicle routes as inputs to optimize the signal timing scheme. Through the asynchronous cross‐coupling optimization of the two stages, the road network efficiency can be improved while ensuring equilibrium. Experimental results show that HAC3 achieves superior convergence performance on both synthetic and real‐world road network data sets, outperforming baseline methods and proving its scalability to large‐scale road networks. Plug‐and‐play experiments indicate the proposed HAC3 framework can integrate with other mainstream signal control models.
大规模道路网络中交通信号和车辆路线的分层自适应交叉耦合控制
交通信号配时和车辆路径已被实证证明是网络级城市道路交通管理中最有前途的两种模式。然而,基于Wardrop理论的主流研究仍然将这两个模块分开对待,没有实现有效的耦合。随着城市规模的不断扩大,基于优化的方法面临着计算复杂性不断增加的挑战,这受到小规模道路网络的限制。为了解决上述挑战,本文提出了HAC3,一种用于全网流量管理的分层自适应交叉耦合控制方法。HAC3采用滚动地平线架构,包括快速更新阶段和慢速更新阶段。慢更新阶段的核心是时空叠加车辆路径规划(SSP)模块,该模块根据路网状态和各个交叉口的交通信号配时为每辆联网车辆分配最优路径,明确路权分配的优先级,避免陷入局部最优。将快速更新阶段用于多交叉口自适应交通信号控制(TSC),以交叉口状态和车辆路径为输入,优化信号配时方案。通过两阶段的异步交叉耦合优化,可以在保证均衡的前提下提高路网效率。实验结果表明,HAC3在合成和现实世界道路网络数据集上都取得了卓越的收敛性能,优于基线方法,并证明了其在大规模道路网络中的可扩展性。即插即用实验表明,所提出的HAC3框架可以与其他主流信号控制模型集成。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
17.60
自引率
19.80%
发文量
146
审稿时长
1 months
期刊介绍: Computer-Aided Civil and Infrastructure Engineering stands as a scholarly, peer-reviewed archival journal, serving as a vital link between advancements in computer technology and civil and infrastructure engineering. The journal serves as a distinctive platform for the publication of original articles, spotlighting novel computational techniques and inventive applications of computers. Specifically, it concentrates on recent progress in computer and information technologies, fostering the development and application of emerging computing paradigms. Encompassing a broad scope, the journal addresses bridge, construction, environmental, highway, geotechnical, structural, transportation, and water resources engineering. It extends its reach to the management of infrastructure systems, covering domains such as highways, bridges, pavements, airports, and utilities. The journal delves into areas like artificial intelligence, cognitive modeling, concurrent engineering, database management, distributed computing, evolutionary computing, fuzzy logic, genetic algorithms, geometric modeling, internet-based technologies, knowledge discovery and engineering, machine learning, mobile computing, multimedia technologies, networking, neural network computing, optimization and search, parallel processing, robotics, smart structures, software engineering, virtual reality, and visualization techniques.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信