Unification of probabilistic graph model and deep reinforcement learning (UPGMDRL) for multi-intersection traffic signal control

IF 7.2 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Knowledge-Based Systems Pub Date : 2024-10-20 DOI:10.1016/j.knosys.2024.112663
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Abstract

Traffic signals play a pivotal role in modern life by preventing collisions, regulating traffic flow, and ensuring a predictable and efficient transportation system. Adaptive traffic light signal control (ATSC) is a promising paradigm for mitigating traffic congestion in Intelligent Transportation Systems (ITS). Among various AI-based approaches, Deep Reinforcement Learning (DRL) has gained widespread application, demonstrating superior performance. This paper aims to develop a latent space reinforcement learning method for intelligent traffic control, with a focus on making explainable decisions. According to the latent model and hidden Markov mixed model, this method integrated both to develop an ATSC framework for traffic networks with multiple intersections. Given the challenges posed by high-dimensional data and a limited understanding of the task, traditional decision-making methods often struggle with understanding the environment. This paper aims to provide semantic information and an enhanced understanding of the environment by offering interpretable states. The latent model is employed to extract task-relevant information from underlying representations within a framework that unifies representation learning and DRL. The experimental results demonstrate how our approach effectively and efficiently balances traffic flow, leading to improved traffic management.
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统一概率图模型和深度强化学习(UPGMDRL)用于多交叉口交通信号控制
交通信号在现代生活中发挥着举足轻重的作用,它可以防止碰撞、调节交通流量,并确保交通系统的可预测性和高效性。自适应交通信号灯控制(ATSC)是智能交通系统(ITS)中缓解交通拥堵的一种前景广阔的范例。在各种基于人工智能的方法中,深度强化学习(DRL)已获得广泛应用,并展现出卓越的性能。本文旨在为智能交通控制开发一种潜空间强化学习方法,重点在于做出可解释的决策。根据潜模型和隐马尔可夫混合模型,该方法将二者结合起来,为具有多个交叉口的交通网络开发了一个 ATSC 框架。鉴于高维数据带来的挑战和对任务的有限理解,传统决策方法往往在理解环境方面举步维艰。本文旨在通过提供可解释的状态来提供语义信息并增强对环境的理解。在表征学习和 DRL 相结合的框架内,采用潜模型从底层表征中提取与任务相关的信息。实验结果表明了我们的方法如何有效、高效地平衡交通流量,从而改善交通管理。
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来源期刊
Knowledge-Based Systems
Knowledge-Based Systems 工程技术-计算机:人工智能
CiteScore
14.80
自引率
12.50%
发文量
1245
审稿时长
7.8 months
期刊介绍: Knowledge-Based Systems, an international and interdisciplinary journal in artificial intelligence, publishes original, innovative, and creative research results in the field. It focuses on knowledge-based and other artificial intelligence techniques-based systems. The journal aims to support human prediction and decision-making through data science and computation techniques, provide a balanced coverage of theory and practical study, and encourage the development and implementation of knowledge-based intelligence models, methods, systems, and software tools. Applications in business, government, education, engineering, and healthcare are emphasized.
期刊最新文献
DeSGOA: double exponential smoothing gazelle optimization algorithm-based deep learning model for blind source separation Contrastive learning for fair graph representations via counterfactual graph augmentation Unification of probabilistic graph model and deep reinforcement learning (UPGMDRL) for multi-intersection traffic signal control SPIRF-CTA: Selection of parameter importance levels for reasonable forgetting in continuous task adaptation HeteroKGRep: Heterogeneous Knowledge Graph based Drug Repositioning
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