摘 要
随着城市化进程的加速，交通拥堵、能源消耗和环境污染等问题日益突出，智能交通系统（Intelligent Transportation System, ITS）作为解决这些问题的关键技术手段，逐渐成为研究热点。强化学习作为一种数据驱动的决策优化方法，能够有效应对复杂动态环境中的不确定性问题，因此在智能交通系统的优化中展现出巨大潜力。本研究旨在探索强化学习在智能交通系统中的应用策略，通过构建基于深度强化学习的优化框架，实现对交通信号控制、车辆路径规划及自动驾驶协同等关键环节的智能化管理。研究采用深度Q网络（DQN）及其改进算法，并结合多智能体强化学习技术，以模拟真实交通场景为实验平台，验证所提方法的有效性。结果表明，该方法能够在多种复杂交通条件下显著降低平均延误时间，提高道路通行能力和能源利用效率，同时减少碳排放量。
关键词：智能交通系统 强化学习 深度Q网络
Abstract
With the acceleration of urbanization, traffic congestion, energy consumption and environmental pollution have become increasingly prominent. Intelligent transportation system (Intelligent Transportation System, ITS), as a key technical means to solve these problems, has gradually become a research hotspot. As a data-driven decision optimization method, reinforcement learning can effectively deal with the uncertainties in complex dynamic environments, so it shows great potential in the optimization of intelligent transportation system. The purpose of this study is to explore the application strategy of reinforcement learning in intelligent transportation system, and realize the intelligent management of traffic signal control, vehicle path planning and autonomous driving collaboration by constructing an optimization fr amework based on deep reinforcement learning. The deep Q network (DQN) and its improved algorithm, combined with multi-agent reinforcement learning technology, are used to simulate real traffic scenes as the experimental platform to verify the effectiveness of the proposed method. The results show that the method can significantly reduce the average delay time, improve road capacity and energy efficiency while reducing carbon emissions.
Keyword:Intelligent Transportation System Reinforcement Learning Deep Q Network
目 录
1绪论 1
1.1智能交通系统的发展背景 1
1.2强化学习在ITS中的意义 1
1.3国内外研究现状分析 1
1.4本文研究方法与技术路线 2
2强化学习基础理论及其应用框架 2
2.1强化学习的基本概念与原理 2
2.2常见强化学习算法综述 3
2.3强化学习在智能交通中的适配性分析 3
2.4强化学习模型的构建方法 3
2.5应用框架设计与实现 4
3强化学习在交通信号控制中的优化策略 4
3.1交通信号控制问题概述 5
3.2强化学习在信号优化中的优势 5
3.3动态交通信号控制策略设计 5
3.4算法性能评估与实验验证 6
3.5实际应用场景与挑战 7
4强化学习在路径规划与车辆调度中的应用 7
4.1路径规划与车辆调度问题定义 7
4.2强化学习驱动的路径优化方法 8
4.3多目标调度策略的设计与实现 8
4.4数据驱动的仿真与结果分析 8
4.5面临的技术瓶颈与改进方向 9
结论 9
参考文献 11
致谢 12
