Trans-Space: Space Computing Based Spatiotemporal Resources Optimization for Signalized Intersection with Transfer Learning

IF 2.5 4区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Xin Qu, Qingyuan Ji, Wei Tan, Shumin Yu, Chao Li, Daoxun Li
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引用次数: 0

Abstract

The optimization of spatial and temporal resources at signalized intersections is a critical aspect of intelligent transportation systems. Traditional traffic signal control methods, which usually rely on fixed signal timings and lane assignments, are suboptimal in addressing changing traffic conditions. Additionally, understanding traffic flow in a large scale is often challenging due to the lack of traffic flow monitoring infrastructure. This paper introduces Trans-Space, a novel framework that leverages transfer learning and space computing for managing spatiotemporal traffic resources at signalized intersections. Trans-Space consists of two core modules: (space computing for optimized traffic system (SCOTS) and traffic optimization with spatial–temporal control agents (TOSCA). SCOTS configures satellite constellations for high-resolution Earth observation images and utilizes space computing to extract real-time traffic flow parameters. TOSCA employs hierarchical reinforcement learning agents to optimize lane directions and signal timings based on the data provided by SCOTS. TOSCA incorporates knowledge transfer that adapts traffic management strategies from source to target intersections. Through extensive simulations, Trans-Space demonstrated superior performance over traditional and state-of-the-art models in traffic flow metrics. The paper concludes with a discussion on the prospects of space computing in traffic management and potential directions for future research.

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跨空间:基于空间计算的信号交叉口时空资源优化与迁移学习
信号交叉口的时空资源优化是智能交通系统的一个重要方面。传统的交通信号控制方法通常依赖于固定的信号配时和车道分配,在应对不断变化的交通状况时,效果并不理想。此外,由于缺乏交通流量监控基础设施,大规模了解交通流量往往具有挑战性。本文介绍了一种利用迁移学习和空间计算来管理信号交叉口时空交通资源的新框架Trans-Space。Trans-Space包括两个核心模块:基于优化交通系统的空间计算(SCOTS)和基于时空控制代理的交通优化(TOSCA)。SCOTS配置卫星星座用于高分辨率地球观测图像,并利用空间计算提取实时交通流量参数。TOSCA采用分层强化学习代理,根据SCOTS提供的数据优化车道方向和信号定时。TOSCA结合了知识转移,适应从源到目标十字路口的交通管理策略。通过广泛的模拟,Trans-Space在交通流量指标方面表现出优于传统和最先进模型的性能。最后,对空间计算在交通管理中的应用前景和未来的研究方向进行了展望。
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来源期刊
IET Intelligent Transport Systems
IET Intelligent Transport Systems 工程技术-运输科技
CiteScore
6.50
自引率
7.40%
发文量
159
审稿时长
3 months
期刊介绍: IET Intelligent Transport Systems is an interdisciplinary journal devoted to research into the practical applications of ITS and infrastructures. The scope of the journal includes the following: Sustainable traffic solutions Deployments with enabling technologies Pervasive monitoring Applications; demonstrations and evaluation Economic and behavioural analyses of ITS services and scenario Data Integration and analytics Information collection and processing; image processing applications in ITS ITS aspects of electric vehicles Autonomous vehicles; connected vehicle systems; In-vehicle ITS, safety and vulnerable road user aspects Mobility as a service systems Traffic management and control Public transport systems technologies Fleet and public transport logistics Emergency and incident management Demand management and electronic payment systems Traffic related air pollution management Policy and institutional issues Interoperability, standards and architectures Funding scenarios Enforcement Human machine interaction Education, training and outreach Current Special Issue Call for papers: Intelligent Transportation Systems in Smart Cities for Sustainable Environment - https://digital-library.theiet.org/files/IET_ITS_CFP_ITSSCSE.pdf Sustainably Intelligent Mobility (SIM) - https://digital-library.theiet.org/files/IET_ITS_CFP_SIM.pdf Traffic Theory and Modelling in the Era of Artificial Intelligence and Big Data (in collaboration with World Congress for Transport Research, WCTR 2019) - https://digital-library.theiet.org/files/IET_ITS_CFP_WCTR.pdf
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