{"title":"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","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":"{\"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}","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}
Hierarchical adaptive cross‐coupled control of traffic signals and vehicle routes in large‐scale road network
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.
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.