交通灯控制系统外文翻译

本科生毕业设计 论文 外文文献翻译 毕业设计题目 交通灯智能控制系统 学 院 信息科学与工程学院 专业班级 测控技术与仪器 0703 班 学生姓名 王欣 指导教师 桑海峰 2011 年年 3 月月 19 日日 外文原文外文原文 Intelligent Traffic Light Control Marco Wiering Jelle van Veenen Jilles Vreeken and Arne Koopman Intelligent Systems Group Institute of Information and Computing Sciences Utrecht University Padualaan 14 3508TB Utrecht The Netherlands email marco cs uu nl July 9 2004 Abstract Vehicular travel is increasing throughout the world particularly in large urban areas Therefore the need arises for simulating and optimizing traffic control algorithms to better accommodate this increasing demand In this paper we study the simulation and optimization of traffic light controllers in a city and present an adaptive optimization algorithm based on reinforcement learning We have implemented a traffic light simulator Green Light District that allows us to experiment with different infrastructures and to compare different traffic light controllers Experimental results indicate that our adaptive traffic light controllers outperform other fixed controllers on all studied infrastructures Keywords Intelligent Traffic Light Control Reinforcement Learning Multi Agent Systems MAS Smart Infrastructures Transportation Research 1 Introduction Transportation research has the goal to optimize transportation flow of people and goods As the number of road users constantly increases and resources provided by current infrastructures are limited intelligent control of traffic will become a very important issue in the future However some limitations to the usage of intelligent traffic control exist Avoiding traffic jams for example is thought to be beneficial to both environment and economy but improved traffic flow may also lead to an increase in demand Levinson 2003 There are several models for traffic simulation In our research we focus on microscopic models that model the behavior of individual vehicles and thereby can simulate dynamics of groups of vehicles Research has shown that such models yield realistic behavior Nagel and Schreckenberg 1992 Wahle and Schreckenberg 2001 Cars in urban traffic can experience long travel times due to inefficient traffic light control Optimal control of traffic lights using sophisticated sensors and intelligent optimization algorithms might therefore be very beneficial Optimization of traffic light switching increases road capacity and traffic flow and can prevent traffic congestions Traffic light control is a complex optimization problem and several intelligent algorithms such as fuzzy logic evolutionary algorithms and reinforcement learning RL have already been used in attempts to solve it In this paper we describe a model based multi agent reinforcement learning algorithm for controlling traffic lights In our approach reinforcement learning Sutton and Barto 1998 Kaelbling et al 1996 with road user based value functions Wiering 2000 is used to determine optimal decisions for each traffic light The decision is based on a cumulative vote of all road users standing for a traffic junction where each car votes using its estimated advantage or gain of setting its light to green The gain value is the difference between the total time it expects to wait during the rest of its trip if the light for which it is currently standing is red and if it is green The waiting time until cars arrive at their destination is estimated by monitoring cars flowing through the infrastructure and using reinforcement learning RL algorithms We compare the performance of our model based RL method to that of other controllers using the Green Light District simulator GLD GLD is a traffic simulator that allows us to design arbitrary infrastructures and traffic patterns monitor traffic flow statistics such as average waiting times and test different traffic light controllers The experimental results show that in crowded traffic the RL controllers outperform all other tested non adaptive controllers We also test the use of the learned average waiting times for choosing routes of cars through the city co learning and show that by using co learning road users can avoid bottlenecks This paper is organized as follows Section 2 describes how traffic can be modelled predicted and controlled In section 3 reinforcement learning is explained and some of its applications are shown Section 4 surveys several previous approaches to traffic light control and introduces our new algorithm Section 5 describes the simulator we used for our experiments and in section 6 our experiments and their results are given We conclude in section 7 2 Modelling and Controlling Traffic In this section we focus on the use of information technology in transportation A lot of ground can be gained in this area and Intelligent Transportation Systems ITS gained interest of several governments and commercial companies Ten T expert group on ITS 2002 White Paper 2001 EPA98 1998 ITS research includes in car safety systems simulating effects of infrastructural changes route planning optimization of transport and smart infrastructures Its main goals are improving safety minimizing travel time and increasing the capacity of infrastructures Such improvements are beneficial to health economy and the environment and this shows in the allocated budget for ITS In this paper we are mainly interested in the optimization of traffic flow thus effectively minimizing average traveling or waiting times for cars A common tool for analyzing traffic is the traffic simulator In this section we will first describe two techniques commonly used to model traffic We will then describe how models can be used to obtain real time traffic information or predict traffic conditions Afterwards we describe how information can be communicated as a means of controlling traffic and what the effect of this communication on traffic conditions will be Finally we describe research in which all cars are controlled using computers 2 1 Modelling Traffic Traffic dynamics bare resemblance with for example the dynamics of fluids and those of sand in a pipe Different approaches to modelling traffic flow can be used to explain phenomena specific to traffic like the spontaneous formation of traffic jams There are two common approaches for modelling traffic macroscopic and microscopic models 2 1 1 Macroscopic models Macroscopic traffic models are based on gas kinetic models and use equations relating traffic density to velocity Lighthill and Whitham 1955 Helbing et al 2002 These equations can be extended with terms for build up and relaxation of pressure to account for phenomena like stop and go traffic and spontaneous congestions Helbing et al 2002 Jin and Zhang 2003 Broucke and Varaiya 1996 Although macroscopic models can be tuned to simulate certain driver behaviors they do not offer a direct flexible way of modelling and optimizing them making them less suited for our research 2 1 2 Microscopic models In contrast to macroscopic models microscopic traffic models offer a way of simulating various driver behaviors A microscopic model consists of an infrastructure that is occupied by a set of vehicles Each vehicle interacts with its environment according to its own rules Depending on these rules different kinds of behavior emerge when groups of vehicles interact Cellular Automata One specific way of designing and simulating simple driving rules of cars on an infrastructure is by using cellular automata CA CA use discrete partially connected cells that can be in a specific state For example a road cell can contain a car or is empty Local transition rules determine the dynamics of the system and even simple rules can lead to chaotic dynamics Nagel and Schreckenberg 1992 describe a CA model for traffic simulation At each discrete time step vehicles increase their speed by a certain amount until they reach their maximum velocity In case of a slower moving vehicle ahead the speed will be decreased to avoid collision Some randomness is introduced by adding for each vehicle a small chance of slowing down Experiments showed realistic behavior of this CA model on a single road with emerging behaviors like the formation of start stop waves when traffic density increases Cognitive Multi Agent Systems A more advanced approach to traffic simulation and optimization is the Cognitive Multi Agent System approach CMAS in which agents interact and communicate with each other and the infrastructure A cognitive agent is an entity that autonomously tries to reach some goal state using minimal effort It receives information from the environment using its sensors believes certain things about its environment and uses these beliefs and inputs to select an action Because each agent is a single entity it can optimize e g by using learning capabilities its way of selecting actions Furthermore using heterogeneous multi agent systems different agents can have different sensors goals behaviors and learning capabilities thus allowing us to experiment with a very wide range of microscopic traffic models Dia 2002 used a CMAS based on a study of real drivers to model the drivers response to travel information In a survey taken at a congested corridor factors influencing the choice of route and departure time were studied The results were used to model a driver population where drivers respond to presented travel information differently Using this population the effect of different information systems on the area where the survey was taken could be simulated The research seems promising though no results were presented A traffic prediction model that has been applied to a real life situation is described in Wahle and Schreckenberg 2001 The model is a multi agent system MAS where driving agents occupy a simulated infrastructure similar to a real one Each agent has two layers of control one for the simple driving decision and one for tactical decisions like route choice The real world situation was modelled by using detection devices already installed From these devices information about the number of cars entering and leaving a stretch of road are obtained Using this information the number of vehicles that take a certain turn at each junction can be inferred By instantiating this information in a faster than real time simulator predictions on actual traffic can be made A system installed in Duisburg uses information from the existing traffic control center and produces real time information on the Internet Another system was installed on the freeway system of North Rhine Westphalia using data from about 2 500 inductive loops to predict traffic on 6000 km of roads 中文译文中文译文 智能交通灯控制智能交通灯控制 马克 威宁 简丽 范 威 吉尔 威瑞肯 安瑞 库普曼 智能系统小组 乌得勒支大学信息与计算科学研究所 荷兰乌得勒支 Padualaan14 号 邮箱 marco cs uu nl 2004 年 7 月 9 日 摘要摘要 世界各地的车辆运行逐渐增多 尤其是在一个大的本地区域 因此就需要 有关交通控制的模拟与优化算法 来更好的地适应日益增长的需求 在文中 我们学习了在城市中的模拟与优化的交通灯控制器 以及目前基于强化学习的 自适应优化算法 我们已经实行了一个交通等模拟器 绿灯区 这允许我们用 不同的基础设施和不同的交通控制器去实验 实验结果表明 在所有基础设施 的研究领域内 我们的自适应交通灯控制器优于其他固定的控制器 关键字关键字 智能交通灯控制 强化学习 多代理系统 MAS 智能基础设施 运输研究 1 介绍介绍 运输研究的目的是优化人流和物流 随着道路使用者的数量不断上涨 当 前基础设施所提供的资源受到限制 在未来 交通智能控制将会成为一个非常 重要的问题 然而 一些交通智能控制使用受限问题的存在 避免交通堵塞 例如 被认为是对环境和经济有益的 但是增加的交通流也可能导致资源需求 的增加 莱文森 2003 这有几个交通仿真模型 在我们的研究中 我们专注于那些具有个体车辆 行为的微观模型 从而更好的模拟群体车辆的动力学 研究表明 这种模型的 出现具有现实意义 Nagel and Schreckenberg 1992 Wahle and Schreckenberg 2001 汽车在城市交通中经历了漫长的运行时间 要归因于低效的交通灯控制 因此 使用成熟传感器和智能优化算法的交通灯优化控制可能是有益的 优化 的交通灯切换增加了道路的容量和人流 能阻止交通堵塞 交通灯控制是一个 复杂的优化问题和几个智能算法 例如模糊逻辑 遗传算法和强化学习 RL 已被应用去试图解决问题 在本文中 我们描述了一种对交通灯控制 基于模 型的 多代理的强化学习算法 我们的方法 强化学习 Sutton and Barto 1998 Kaelbling 1996 和基于道 路使用者的价值功能 威宁 2000 被用来决定每个交通灯的优化选择 这个决 定是基于道路使用者站了一个交叉路口的累积投票 在那里每辆汽车使用其估 计选票的优势 或增益 设置它的光的绿色 在其余路程 它的所有等待时间里 如果信号灯现在是红色的或者绿色的 那么增益的值是不同的 汽车直到到达 目的地后的等待时间 是通过监测汽车流过基础设施和应用强化学习 RL 算法 而估算出来的 本文写作安排如下 第二部分描述了交通是如何被建立 预测和控制的 在第三部分解释了什么是强化学习和一些它的应用 第四部分调查了几个以前 交通控制的方法 介绍了我们的新算法 第五部分描述了我们实验中所使用的 仿真器 以及第六部分给出我们的实验和实验结果 在第七部分我们得出结论 2 建立和控制交通建立和控制交通 在这一部分 我们专注于在交通运输方面所使用的信息技术 在这个区域 增加了大量的土地 并且一些政府和商业公司在交通智能系统 ITS 方面获得 了利润 Ten T expert group on ITS 2002 白皮书 2001 EPA98 1998 交通智能系统 ITS 研究包括车内安全系统 基础设施改变所引起的仿真 效果 路途规划 优化运输和智能的基础设施 其主要目标是 提高安全性 减少运行时间 增加基础设施的能力 这种改进有益健康 经济 环境 这表 现在交通智能系统的分配预算方面 在本文中 我们主要对车流的优化感兴趣 从而有效减少平均运行 或者等待 的 车辆次数 一种常见的分析交通的工具就是交通仿真器 在这部分中 我们将首 先描述两种常用于交通模型的技术 然后我们将描述模型是如何用来获取实时 交通信息或者预测交通情况的 后来 我们描述信息是如何作为一种控制交通的 手段来进行沟通的 在这样的交通条件下 沟通产生了什么样的影响 最后 我 们描述了所有的汽车都使用计算机进行控制的研究 2 1 建立交通建立交通 与交通动力学仅有的相似之处是 例如 流体力学和管内的沙子 建立车 流模型的不同方法是用来解释交通的特殊现象的 就像自发形成的交通堵塞状 况 有两种普遍的方法去建立交通 宏观和微观模型 2 1 12 1 1 宏观模型宏观模型 宏观交通模型是基于gas kinetic模型的 利用了关于交通密度和速度的方 程式 Lighthill and Whitham 1955 Helbing et al 2002 这些方程式可以延长 积累和放松压力 归因于类似的停停走走的交通和自发的拥堵的现象 Helbing et al 2002 Jin and Zhang 2003 Broucke and Varaiya 1996 尽管 宏观模型可以来模拟一些特定的可调驱动行为 但是他们不能提供一个直接的 灵活的建立和优化交通的方法 这使他们不太适合我们的研究 2 1 22 1 2 微观模型微观模型 与宏观模型相对比的 微观交通模型提供了一种仿真各种各样司机行为的 方法 一个微观模型由一组车辆占据的基础设施组成 每辆车都根据自己的规 则 和周围的环境产生作用 根据这些规则 当很多车辆互相作用时 不同种 类的行为就会出现 元胞自动机元胞自动机 一个在基础设施上的具体设计和仿真 简单的 汽车驾驶规 则 利用了元胞自动机 CA 元胞自动机运用离散的部分连接细胞 那些细 胞就能处于一种