摘要翻译：
目前已有几种基于元胞自动机的公路交通模型。最简单的是基本元胞自动机规则184。我们将此模型扩展到城市交通，在交叉路口耦合元胞自动机，仅使用规则184、252和136。模型的简单性提供了对城市交通及其相变的主要性质的清晰理解。利用该模型对两种交通信号灯协调方法进行了比较：一种是根据预期流量优化相位的绿波法，另一种是适应当前交通状况的自组织法。自组织方法比绿波方法有很大的改进。对于低密度，自组织方法促进了在四个方向上自由流动的排的形成和协调，即以最大速度和没有停止。对于中等密度，该方法允许不断使用交叉路口，利用其最大通量能力。对于高密度，该方法防止了交通堵塞，并促进了“自由空间”的形成和协调，这些“自由空间”流向与交通相反的方向。
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英文标题：
《Modeling self-organizing traffic lights with elementary cellular
  automata》
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作者：
Carlos Gershenson and David A. Rosenblueth
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最新提交年份：
2009
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分类信息：

一级分类：Physics        物理学
二级分类：Cellular Automata and Lattice Gases        元胞自动机与格子气体
分类描述：Computational methods, time series analysis, signal processing, wavelets, lattice gases
计算方法，时间序列分析，信号处理，小波，格子气体
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一级分类：Physics        物理学
二级分类：Statistical Mechanics        统计力学
分类描述：Phase transitions, thermodynamics, field theory, non-equilibrium phenomena, renormalization group and scaling, integrable models, turbulence
相变，热力学，场论，非平衡现象，重整化群和标度，可积模型，湍流
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一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Physics        物理学
二级分类：Adaptation and Self-Organizing Systems        自适应和自组织系统
分类描述：Adaptation, self-organizing systems, statistical physics, fluctuating systems, stochastic processes, interacting particle systems, machine learning
自适应，自组织系统，统计物理，波动系统，随机过程，相互作用粒子系统，机器学习
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英文摘要：
  There have been several highway traffic models proposed based on cellular automata. The simplest one is elementary cellular automaton rule 184. We extend this model to city traffic with cellular automata coupled at intersections using only rules 184, 252, and 136. The simplicity of the model offers a clear understanding of the main properties of city traffic and its phase transitions.   We use the proposed model to compare two methods for coordinating traffic lights: a green-wave method that tries to optimize phases according to expected flows and a self-organizing method that adapts to the current traffic conditions. The self-organizing method delivers considerable improvements over the green-wave method. For low densities, the self-organizing method promotes the formation and coordination of platoons that flow freely in four directions, i.e. with a maximum velocity and no stops. For medium densities, the method allows a constant usage of the intersections, exploiting their maximum flux capacity. For high densities, the method prevents gridlocks and promotes the formation and coordination of "free-spaces" that flow in the opposite direction of traffic.
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PDF链接：
https://arxiv.org/pdf/0907.1925