论文标题
对交通信号控制的批判性审查以及强化学习和模型预测性控制方法的新颖统一观点，用于自适应交通信号控制
A Critical Review of Traffic Signal Control and A Novel Unified View of Reinforcement Learning and Model Predictive Control Approaches for Adaptive Traffic Signal Control
论文作者
论文摘要
近年来，自适应交通信号控制（ATSC）方法的实质增长，这些方法可以提高运输网络效率，尤其是在利用基于人工智能的优化和控制算法（例如增强学习以及常规模型预测控制）的分支机构中。但是，缺乏跨域分析和应用方法在ATSC研究中的有效性的比较限制了我们对现有挑战和研究方向的理解。本章提出了一种新颖的现代ATSC统一观点，以确定共同基础以及现有方法的差异和缺点，以促进交叉利用并推进最先进的最终目标。统一的视图应用了马尔可夫决策过程的数学语言，描述了来自世界（问题）和解决方案建模的角度的控制器设计过程。统一的观点还分析了现有研究中通常忽略的系统问题，并提出了解决这些问题的未来潜在方向。
Recent years have witnessed substantial growth in adaptive traffic signal control (ATSC) methodologies that improve transportation network efficiency, especially in branches leveraging artificial intelligence based optimization and control algorithms such as reinforcement learning as well as conventional model predictive control. However, lack of cross-domain analysis and comparison of the effectiveness of applied methods in ATSC research limits our understanding of existing challenges and research directions. This chapter proposes a novel unified view of modern ATSCs to identify common ground as well as differences and shortcomings of existing methodologies with the ultimate goal to facilitate cross-fertilization and advance the state-of-the-art. The unified view applies the mathematical language of the Markov decision process, describes the process of controller design from both the world (problem) and solution modeling perspectives. The unified view also analyses systematic issues commonly ignored in existing studies and suggests future potential directions to resolve these issues.
