智能交通灯控制研究文献及翻译

1、1智能交通灯控制研究1 绪论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

2、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

3、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。2 交通控制模型在这一节中，我们注重信息技术在交通运输方面的应用在这个领域可以获得很多利益， 几个国家政府和商业公司从智能运输系统( its )中获得了利益。模型建立和交通控 its 的研究包括汽车安全系统，仿真基建变化效果，路径

4、规划，优化交通，智能基础设施其主要目标是：提高安全性，减少行车时间，提高基础设施的能力。在本文中，我们的主要兴趣在优化交通流，从而有效地减少平均旅行(或等待)时间一个分析交通的普通工具是交通模拟器。本节我们将首先描述两种常用的模式交通的方法。然后我们会描述模型是怎样获得实时交通信息或预测交通条件的。2.1 交通模型动态交通的相同之处是显而易见的，例如动态的液体，管道里的沙子，不同近似方法得到的交通流量模型，可以用来解释具体交通的现象，像自发形成的交通拥堵 有两种常见的办法模拟交2通：宏观模型和微观模型。2.1.1 宏观模型宏观交通模型都是基于气体动力学的模型，并利用方程与交通密度来得到速度。这

5、些方程可以通过内建的和减小压力来说明定期而不断被迫停止的交通和自发的交通堵塞。虽然宏观模型可调谐模拟某些司机的行为，但它们并不能直接的，灵活的建立模型和优化它们，使它们适合于我们的研究。2.1.2 微观模型对比宏观模型，微观的交通模型提供了模拟各种驾驶行为的方法。微观模型是由一个被一组车辆占领的基础设施组成，每辆车按照自己的规则与各自的环境相适应，根据这些规则，群体车辆相互作用的时候，就出现了不同的行为。（1）小单元的自动控制： 一个具体的设计方法及汽车驾驶规则仿真(简单的)，是利用一种叫做细胞自动控制器(ca)来完成的。在一个特定的条件下，ca 使用离散部分连通各单元。举例来说，一条路单元可

6、以包含一个车，或者是空的。本地的交通规则决定了系统的动力，甚至简单的规则可以导致混沌。纳哥尔和史瑞克描述一个 ca 交通仿真模型，在每一个离散时间步，车辆按一定数额的速度增加， 直到他们达到最高速度。如果是慢上坡，速度会有所下降，以避免相撞 一些随机性介绍如下：每部车辆的一个小机会趋缓 实验表明，当交通密度增大，这一 ca 模型现实的行为与开动-停止的形成相似。（2）multi-agent 系统介绍：一种更高级的进行交通模拟和优化的方法就是智能多级 agent 系统方法论。其中代理商之间的互动与沟通，是彼此相处的基础。智能代理是一个实体，有些国家用最小的努力自主试图达到这个目标。它利用其传感器

7、从环境获得资料，并用这些理念和投入选择一个行动，因为每个agent 是一个单一实体，它可以用优化(例如，用学习能力等)的方式选择行为。此外，利用异种多 agent 系统，不同的代理商，可以有不同的传感器，目标，行为和学习能力，从而使我们的实验是非常广泛的(微观)的交通模式。dia 根据一项实际驱动的研究，建立了司机反映的信息模型。人们对影响选择路线和出发时间的因素进行了研究，结果用来模拟驾驶员对不同地方信息的反映。利用这部分人口，来对不同的地区的调查进行模拟 研究似乎很有希望，但没有结果。 2.2 交通预测 在一个路口，交通量预测的最简单方式是测定一段时间，假设条件，将同为下一期。一种方法是预

8、测当神经网络被用来预测一个红绿灯的队列长度，多层感知是训练有素的预测排队长度和下一时间步长，长期预测可由迭代步法预估。因此，最后的网络在预测 10 个步长时是相当准确的。但目前尚未被纳入到一个控制器。交通预测模型，已被应用到实际生活情况。该模型是一个多 agent 系统(mas)，操纵代理商占据了一个类似实际问题的模拟设施，每个代理人有两层控制。一对(简单)驾驶的决定 ，一个战术决定像路线选择。真正的世界形势用已经安装的探测装置探测并建立了模型从这些装置，可以获得与这段道路相关的汽车的进出数量，利用这一信息，就可以得到这个路口车辆相见的概率。由3于这个实例资料的速度比实时仿真快，交通流量的实际

9、预测就可以做得更好安装在德国杜伊斯堡的系统利用现有的交通控制中心和生产实时数据上网获得信息。另一个系统是安装在高速公路系统中的北莱茵-威斯特法，用来自约 2500 电感线圈所得到的信息预测沿线 6000 公里的公路的交通情况。2.3 控制交通的交通通信条件一旦 (预测)资料准确，有几种途径传达给道路使用者，司机可以用动态信息，通过道路标志，广播，甚至在车载导航系统。几项研究已经显示出提供相关资料的效果 levinson 采用了微观经济模型，考虑到一个行程的成本，并提高了系统的可靠性，从而使塞车可以更好地避免。实验表明，通知过的司机降低平均旅行时间的百分比均高于不知情的司机。旅行时间能否预测交通

10、状况是最优控制的关键。举例来说，如果我们都知道，有些道路会变得拥挤不堪了一段时间之后，在当前条件下，这种信息可以传送给道路使用者，可以绕过这条道路，因此允许整个系统得以缓解紧迫的情况.此外，如果我们能比较准确地预测不同的驾驶策略的后果，比较预测结果，就能迅速做出最优化(或至少是最优的预测区间)的决定。减少最多的是在交通拥挤的时候，在获悉发生突发性的塞车问题(例如意外)，有经验的司机通过路线切换减少旅自己的旅行时间。但由于这种路线替代，是交通变得更加拥挤，有可能增加不知情的司机的旅行时间。emmerink 等人介绍了把一些人带去阿姆斯特丹做试验的调查结果。结果表 70%的司机有透过电台或可变信息

11、标志找到适合自己的路线双方媒体都在用类似的方式。和有其它旅行目的的人相比，游客基本不会受到信息的影响。商务司机表示，他们愿意付款，以得到车辆信息。模拟器可以用来测试控制策略，然后落实在真实的生活环境中。阿卡凡等人做了一个关于大型公路项目的不同的策略。例如交通访问控制，路线选择，行车控制模拟器提供一个测试不同配置的车辆探测器的方法，这表明相互作用控制系统实际上可能恶化交通状况。一体化战略中，需要仔细分析各部分的影响，证明模拟器是一个有用的工具。2.4 车辆控制以避免干扰，这种分析这是一个众所周知的事实是，交通流量将大幅增加，如果所有司机将driveat 相同的（最高）的速度。另一个事实是，这将是

12、绝对不可能的事情如果你让driversdecide 。在本节中，我们首先查看如何车辆可以学习合作。然后，我们 describean 雄心勃勃的研究计划，目的是控制所有车辆由机载计算机。莫和兰利（1998）用强化学习的分布式交通结论 trol 。他们的做法，使车的学习里选择策略从经验中 witha 交通模拟器。实验研究表明，据悉，战略，让司机 moreclosely 符合他们理想的速度比手工制作的控制器和人数减少 oflane 的变化。他们的做法，像我们这样，侧重于分布式汽车为基础的控制器， whichmakes 很容易采取一些具体的愿望/目标的司机，到如预期的速度(ordestination.

13、in)加州的合作伙伴和先进的过境公路（路径）计划，太子港 tomated 公路系统。2.4.1 交通灯控制交通灯优化是一个复杂的问题，即使使是单路口，有可能没有明显的最优解。与多个路口，个问题变得更加复杂，作为国家的一轻的影响，对许多其他的灯光。另外的并发症的事实是，流量不断变化，视乎有关的时间一天，一天的一周内，并在一年的时间。道路和事故的进一步影响4复杂性和性能。 在实践中最交通灯控制固定周期控制器。一个周期的结论 figurations 是指在所有的交通得到了绿灯，在一些点。分裂的时间决定了多久，灯光应该留在每一个国家。繁忙的道路上可以得到优惠通过调整分裂的时间。周期时间是时间，一个完整

14、的周期。在拥挤的交通，较长的周期，导致更好的表现。设置一个周期定义的起始时间一个周期相对其他交通灯。设置可作调整，让几个灯的合作， 并举例来说，创造绿色的波浪。固定控制器有加以调整的具体情况有良好表现。经常一表的时间特定的设置是用来让一盏灯，适应周期性活动一样，尖峰时间的交通。设置控制参数的固定控制器是一个大量工作， 控制器有定期予以更新，由于变化的交通情况。独特的事件不能处理不好，因为它们需要大量的手动更改该系统。固定控制器可以回应交通的开始，一个周期只有当交通是目前，但这种汽车驱动控制器，还需要大量的微调。 大多数的研究在交通灯控制的重点是适应期或秩序控制周期。我们的做法，我们不使用周期，

15、但让的决定取决于对实际的交通情况，周围的交界处，从而导致更准确的控制。当然，我们的做法，要求有关的实际交通情况，可获得使用邸？ erent 感应器或通讯系统。我们将首先描述相关工作 intelligent 红绿灯控制， andthen describeourcar基于强化学习。3 结论投资是值得的。我们所描述的交通显示可以为蓝本，并显示实际使用某些型号。我们解释，强化学习，并表明其使用作为优化算法的各项管制的问题。然后，我们描述了问题的交通灯控制和几个智能交通灯控制器，然后才显示如何汽车为基础的强化学习，可用于交通灯控制问题。在于我们的做法，我们让车，估计其增益设置他们的灯，绿色，并让所有的车

16、投票产生的交通灯的决定。合作学习是一种特殊的特点，我们的赛车基于强化学习算法，让司机选择最短的路线与最低预计轮候时间。我们执行的三个系列的实验中，使用了绿灯区的 c 模拟器，我们如何描述这个模拟器的工程，那个实验表现在三个方面，erent 基础设施。第一次试验，其中使用一个大型的网格，表明强化学习的是电子交通。excient 在控制流量，并认为使用的合作学习，进一步提高性能。第二次实验结果表明， 用合作学习的车辆避免前往人烟稠密的路口。这样，车辆不必等待，并积极减少的压力，拥挤的十字路口。第三次实验结果表明， 算法研究部主管认为，对更加复杂和城市基础设施一样，再次超出定额控制器通过减少等候时间

17、，与 25 以上。第三次实验还表明，在有些情况下的一种简化版本的强化学习算法执行以及完整的版本，并且合作，学习并不总是提高性能。4 进一步研究虽然强化学习算法在这里提出了一些优于固定算法的方法，有几项改进，举例来说，我们使用它来沟通道路的行车线，使绿色浪潮成为可能，让估计的等候时间依赖于交通量在未来的道路里。学习驾驶的政策，可能会提高。目前的执行情况患有饱和度和振荡。因为所有的司机对路线选择最优里，这里有可能成为拥挤。只有当的表现这样一个里跌幅正因为如此拥挤，司机会选择另一个里。那么贪心形式的合作学习可能会阻止这种效果。虽然桶算法的工程，以及为固定的算法，它没有工作，以及同研究部主管算法。我们

18、要研究这个更小心，因为水桶 26 算法，当设计的好，可能有助于在创建绿色浪潮在非常拥挤的交通条件。 模拟器可能加以完善，以及，以便让比较与其他的研究。 优化可以包括更复杂的动力学为车辆及其他道路使用者，作为以及作为实施固5定周期的交通灯控制器。 intelligent traffic light control and researchabstract: the system has introduced that new traffic centering on 89c51 monolithic machine controls systematic design mainly. this

19、system uses the hand control, timed control, wireless remote control and real-time control. the real-time control is in the transportation control one kind more novel also an effective method, in this method application optimum control theory control thought, the tendency, real-time controls the cur

20、rent green light time, maximum limit enhanced the transportation efficiency in under the guarantee traffic safety premise.key words: 89c51, timed control, real-time control, remote control1 introductiontransportation research has the goal to optimize transportation flow of people and goods.as the nu

21、mber of road users constantly increases, and resources provided by current infras-tructures are limited, intelligent control of traffic will become a very important issue in thefuture. however, some limitations to the usage of intelligent tra?c control exist. avoidingtraffic jams for example is thou

22、ght 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

23、 dynam-ics of groups of vehicles. research has shown that such models yield realistic behaviornagel 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 sophist

24、icated 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

25、 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 6learningalgorithm for controlling traffic lights.in our approach, reinforcement learning sutton and barto, 199

26、8, kaelbling et al., 1996with 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 setti

27、ngits 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 flowingthroug

28、h 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,

29、 monitor traffic flow statistics such asaverage waiting times, and test different traffic light controllers. the experimental resultsshow 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 cho

30、osing routes ofcars through the city (co-learning), and show that by using co-learning road users can avoidbottlenecks.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 itsapplic

31、ations are shown. section 4 surveys several previous approaches to tra?c light control,and introduces our new algorithm. 2 modelling and controlling trafficin this section, we focus on the use of information technology in transportation. a lot ofground can be gained in this area, and intelligent tra

32、nsportation systems (its) gained in-terest 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 in

33、frastructures. its main goals are:improving safety, minimizing travel time, and increasing the capacity of infrastructures. suchimprovements are beneficial to health, economy, and the environment, and this shows in theallocated budget for its.in this paper we are mainly interested in the optimizatio

34、n of traffic flow, thus effectively minimizing average traveling (or waiting) times for cars. a common tool for analyzing trafficis the traffic simulator. in this section we will first describe two techniques commonly usedto model traffic. we will then describe how models can be used to obtain real-

35、time traffic information or predicttraffic conditions. afterwards we describe how information can becommunicated as a means of controlling traffic, and what the e?ect of this communication ontraffic conditions will be. finally, we describe research in which all cars are controlled usingcomputers.2.1

36、 modelling traffictraffic dynamics bare resemblance with, for example, the dynamics of fluidsand those of sandin a pipe. different approaches to modelling traffic flow can be used to explain phenomenaspecific to traffic, 7like the spontaneous formation of traffic jams. there are two commonapproaches

37、 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 canbe extended with terms for build

38、-up and relaxation of pressure to account for phenomena likestop-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 certaindriver behaviors, they do not offer a direct, flexible, way of

39、modelling and optimizing them,making them less suited for our research.2.1.2 microscopic models.incontrast tomacroscopic models, microscopic traffic modelsoffer away of simulating variousdriver behaviors. a microscopic model consists of an infrastructure that is occupied by a setof vehicles. each ve

40、hicle interacts with its environment according to its own rules. dependingon these rules, different kinds of behavior emerge when groups of vehicles interact.cellular automata. one specific way of designing and simulating (simple) driving rulesof cars on an infrastructure, is by using cellular autom

41、ata (ca). ca use discrete partiallyconnected cells that can be in a specific state. for example, a road-cell can contain a caror is empty. local transition rules determine the dynamics of the system and even simplerules can lead to chaotic dynamics. nagel and schreckenberg (1992) describe a ca model

42、for traffic simulation.at each discrete time-step, vehicles increase their speed by a certainamount 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 foreach vehicle a small chance

43、 of slowing down. experiments showed realistic behavior of thisca model on a single road with emerging behaviors like the formation of start-stop waveswhen traffic density increases.cognitive multi-agent systems. a more advanced approach to simulation andoptimization isthecognitive multi-agent syste

44、mapproach (cmas), in whichagents interactand communicate with each other and the infrastructure. a cognitive agent is an entity thatautonomously tries to reach some goal state using minimal effort. it receives informationfrom the environment using its sensors, believes certain things about its envir

45、onment, anduses these beliefs and inputs to select an action. because each agent is a single entity, itcan optimize (e.g., by using learning capabilities) its way of selecting actions. furthermore,using heterogeneous multi-agent systems, di?erent agents can have different sensors, goals,behaviors, a

46、nd learning capabilities, thus allowing us to experiment with a very wide rangeof (microscopic) traffic models.dia (2002) used a cmas based on a study of real drivers to model the drivers response totravel information. in a survey taken at a congested corridor, factors influencing the choice ofroute

47、 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 8information systems on the area where the survey was taken could besimulated. the research seems prom

48、ising, though no results were presented.2.2 predicting trafficthe ability to predict traffic conditions is important for optimal control. for example, if wewould know that some road will become congested after some time under current conditions,this information could be transmitted to road users tha

49、t can circumvent this road, therebyallowing the whole system to relieve from congestion. furthermore, if we can accuratelypredict the consequences of different driving strateges, an optimal (or at least optimal forthe predicted interval) decision can be made by comparing the predicted results.the si

50、mplest form of traffic prediction at a junction is by measuring tra?c over a cer-tain time, and assuming that conditions will be the same for the next period. one ap-proach to predicting is presented in ledoux, 1996, where neural networks are used to per-form long-term prediction of the queue length

51、 at a traffic light. a multi-layer perceptronrumelhart et al., 1986 is trained to predict the queue length for the next time-step, andlong-term predictions can be made by iterating the one-step predictor. the resulting networkis quite accurate when predicting ten steps ahead, but has not yet been in

52、tegrated into acontroller.a tra?c prediction model that has been applied to a real-life situation, is described inwahle and schreckenberg, 2001. the model is a multi-agent system (mas) where drivingagents occupy a simulated infrastructure similar to a real one. each agent has two layers ofcontrol; o

53、ne 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. fromthese devices, information about the number of cars entering and leaving a stretch of roadare obtained. using this informatio

54、n, the number of vehicles that take a certain turn ateach junction can be inferred. by instantiating this information in a faster than real-timesimulator, predictions on actual tra?c can be made. a system installed in duisburg usesinformation from the existing traffic control center and produces rea

55、l-time information on theinternet. another system was installed on the freeway system of north rhine-westphalia,using data from about 2.500 inductive loops to predict tra?c on 6000 km of roads.2.3 controlling traffic by communicating traaffic conditionsonce accurate (predictive) information is avail

56、able, there are several ways of communicatingit to road users. drivers could be presented with information through dynamic road signs,radio, or even on-board navigation systems. several studies have shown the e?ects of the. several studies have shown the e?ects of theavailability of relevant informa

57、tion.levinson(2003)usesamicro-economicmodeltoconsiderthecostofatrip ,andincreasessystemreliability,sincecongestioscanbebetteravoided.thisresultsinsupplycurveshift.experiments show that increasing the percentage of informed drivers reduces the averagetravel time for both informed and uninformed drive

58、rs. the travel time reduction is largest intravel time for both informed and uninformed drivers. the travel time reduction is largest incrowdedtraffic. inthecase ofunexpectedcongestions (forexample duetoaccidents) informedtravellers reduce their travel time by switching routes, but as a result of th

59、is the alternativeroutes become more crowded, 9possibly increasing travel times for uninformed drivers.emmerink et al. (1996) present the results of a survey taken amongst drivers in am-sterdam.thisresultsinsupplycurveshift.experiments show that increasing the percentage of informed drivers reduces

60、the averagetravel time for both informed and uninformed drivers. the travel time reduction is largest intravel time for both informed and uninformed drivers. the travel time reduction is largest incrowdedtraffic. inthecase ofunexpectedcongestions (forexample duetoaccidents) informedtravellers reduce