当今时代是一个自动化时代，交通灯控制等很多行业的设备都与计算机密切相关。因此，一个好的交通灯控制系统，将给道路拥挤，违章控制等方面给予技术革新。随着大规模集成电路及计算机技术的迅速发展，以及人工智能在控制技术方面的广泛运用，智能设备有了很大的发展，是现代科技发展的主流方向。本文介绍了一个智能交通的系统的设计。该智能交通灯控制系统可以实现的功能有：对某市区的四个主要交通路口进行控制：个路口有固定的工作周期，并且在道路拥挤时中控制中心能改变其周期：对路口违章的机动车能够即时拍照，并提取车牌号。在世界范围内，一个以微电子技术，计算机和通信技术为先导的，一信息技术和信息产业为中心的信息革命方兴未艾。而计算机技术怎样 与实际应用更有效的结合并有效的发挥其作用是科学界最热门的话题，也是当今计算机应用中空前活跃的领域。本文主要从单片机的应用上来实现十字路口交通灯智能化的管理，用以控制过往车辆的正常运作。
研究交通的目的是为了优化运输，人流以及货流。由于道路使用者的不断增加，现有资源和基础设施有限，智能交通控制将成为一个非常重要的课题。但是，智能交通控制的应用还存在局限性。例如避免交通拥堵被认为是对环境和经济都有利的，但改善交通流也可能导致需求增加。交通仿真有几个不同的模型。在研究中，我们着重于微观模型，该模型能模仿单独车辆的行为，从而模仿动态的车辆组。
由于低效率的交通控制，汽车在城市交通中都经历过长时间的行进。采用先进的传感器和智能优化算法来优化交通灯控制系统，将会是非常有益的。优化交通灯开关，增加道路容量和流量，可以防止交通堵塞，交通信号灯控制是一个复杂的优化问题和几种智能算法的融合，如模糊逻辑，进化算法， 和聚类算法已经在使用，试图解决这一问题，本文提出一种基于多代理聚类算法控制交通信号灯。
在我们的方法中，聚类算法与道路使用者的价值函数是用来确定每个交通灯的最优决策的，这项决定是基于所有道路使用者站在交通路口累积投票，通过估计每辆车的好处(或收益)来确定绿灯时间增益值与总时间是有差异的，它希望在它往返的时候等待，如果灯是红色，或者灯是绿色。等待，直到车辆到达目的地，通过有聚类算法的基础设施，最后经过监测车的监测。
我们对自己的聚类算法模型和其它使用绿灯模拟器的系统做了比较。绿灯模拟器是一个交通模拟器，监控交通流量统计，如平均等待时间，并测试不同的交通灯控制器。结果表明，在拥挤的交通条件下，聚类控制器性能优于其它所有测试的非自适应控制器，我们也测试理论上的平均等待时间，用以选择车辆通过市区的道路，并表明，道路使用者采用合作学习的方法可避免交通瓶颈。
本文安排如下：第2部分叙述如何建立交通模型，预测交通情况和控制交通。第3部分是就相关问题得出结论。第4部分说明了现在正在进一步研究的事实，并介绍了我们的新思想。
The times is a automation times nowadays,traffic light waits for much the industey equipment to go hand in hand with the computer under the control of.Therefore,a good traffic light controls system,will give road aspect such as being crowded,controlling against rules to give a technical improvement.With the fact that the large-scale integrated circuit and the computer art promptne develop,as well as artificial intelligence broad in the field of control technique applies,intelligence equipment has had very big development,the main current being that modern science and technology develops direction.The main body of a book is designed having introduccd a intelligence traffic light systematically.The function being intelligence traffic light navar’s turn to be able to come true has：The croing carries out supervisory control on four main traffic of some downtown area;Every croing has the fixed duty period,charges centrefor being able to change it’s period and in depending on a road when being crowded;The motro vehicle breaking rules and regulations to the croing is able to take a photo immediately,abstracts and the vehicle shop sign.Within world range ,one uses the microelectronics technology,the computer and the technology communicating by letter are a guide’s,centering on IT and IT industry information revolution is in the ascendant.But,how,computer art applies more effective union and there is an effect’s brought it’s effect into play with reality is the most popular topic of scientific community,is also that computer applications is hit by the unparalleled active field nowadays.The main body of a book is applied up mainly from slicing machine’s only realizing intellectualized administration of croroads traffic light,use operation in controlling the vehicular traffic regularity.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 infras-tructures are limited, intelligent control of traffic will become a very important iue in thefuture.However, some limitations to the usage of intelligent tra?c control exist.Avoidingtraffic jams for example is thought to be beneficial to both environment and economy, butimproved 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 microscopicmodels that model the behavior of individual vehicles, and thereby can simulate dynam-ics 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 con-trol.Optimal control of traffic lights using sophisticated sensors and intelligent optimizationalgorithms might therefore bevery beneficial.Optimization of traffic light switching increasesroad capacity and traffic flow, and can prevent tra?c congestions.Traffic light control is acomplex optimization problem and several intelligent algorithms, such as fuzzy logic, evo-lutionary algorithms, and reinforcement learning (RL) have already been used in attemptsto solve it.In this paper we describe a model-based, multi-agent reinforcement learningalgorithm 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 decisionsfor each traffic light.The decision is based on a cumulative vote of all road users standingfor a traffic junction, where each car votes using its estimated advantage (or gain) of settingits light to green.The gain-value is the difference between the total time it expects to waitduring 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 flowingthrough the infrastructure and using reinforcement learning (RL) algorithms.We compare the performance of our model-based RL method to that of other controllersusing the Green Light District simulator (GLD).GLD is a traffic simulator that allows usto design arbitrary infrastructures and traffic patterns, monitor traffic flow statistics such asaverage waiting times, and test different traffic light controllers.The experimental resulthow that in crowded traffic, the RL controllers outperform all other tested non-adaptivecontrollers.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 avoidbottlenecks.
