交通燈外文翻譯 2

1、<p>　　當(dāng)今時(shí)代是一個(gè)自動(dòng)化時(shí)代，交通燈控制等很多行業(yè)的設(shè)備都與計(jì)算機(jī)密切相關(guān)。因此，一個(gè)好的交通燈控制系統(tǒng)，將給道路擁擠，違章控制等方面給予技術(shù)革新。隨著大規(guī)模集成電路及計(jì)算機(jī)技術(shù)的迅速發(fā)展，以及人工智能在控制技術(shù)方面的廣泛運(yùn)用，智能設(shè)備有了很大的發(fā)展，是現(xiàn)代科技發(fā)展的主流方向。本文介紹了一個(gè)智能交通的系統(tǒng)的設(shè)計(jì)。該智能交通燈控制系統(tǒng)可以實(shí)現(xiàn)的功能有：對(duì)某市區(qū)的四個(gè)主要交通路口進(jìn)行控制：個(gè)路口有固定的工作周期，并且在道路

2、擁擠時(shí)中控制中心能改變其周期：對(duì)路口違章的機(jī)動(dòng)車能夠即時(shí)拍照，并提取車牌號(hào)。在世界范圍內(nèi)，一個(gè)以微電子技術(shù)，計(jì)算機(jī)和通信技術(shù)為先導(dǎo)的，一信息技術(shù)和信息產(chǎn)業(yè)為中心的信息革命方興未艾。而計(jì)算機(jī)技術(shù)怎樣 與實(shí)際應(yīng)用更有效的結(jié)合并有效的發(fā)揮其作用是科學(xué)界最熱門的話題，也是當(dāng)今計(jì)算機(jī)應(yīng)用中空前活躍的領(lǐng)域。本文主要從單片機(jī)的應(yīng)用上來(lái)實(shí)現(xiàn)十字路口交通燈智能化的管理，用以控制過(guò)往車輛的正常運(yùn)作。</p><p>　　研究交通的目

3、的是為了優(yōu)化運(yùn)輸，人流以及貨流。由于道路使用者的不斷增加，現(xiàn)有資源和基礎(chǔ)設(shè)施有限，智能交通控制將成為一個(gè)非常重要的課題。但是，智能交通控制的應(yīng)用還存在局限性。例如避免交通擁堵被認(rèn)為是對(duì)環(huán)境和經(jīng)濟(jì)都有利的，但改善交通流也可能導(dǎo)致需求增加。交通仿真有幾個(gè)不同的模型。在研究中，我們著重于微觀模型，該模型能模仿單獨(dú)車輛的行為，從而模仿動(dòng)態(tài)的車輛組。</p><p>　　由于低效率的交通控制，汽車在城市交通中都經(jīng)歷過(guò)長(zhǎng)時(shí)間

4、的行進(jìn)。采用先進(jìn)的傳感器和智能優(yōu)化算法來(lái)優(yōu)化交通燈控制系統(tǒng)，將會(huì)是非常有益的。優(yōu)化交通燈開關(guān)，增加道路容量和流量，可以防止交通堵塞，交通信號(hào)燈控制是一個(gè)復(fù)雜的優(yōu)化問(wèn)題和幾種智能算法的融合，如模糊邏輯，進(jìn)化算法， 和聚類算法已經(jīng)在使用，試圖解決這一問(wèn)題，本文提出一種基于多代理聚類算法控制交通信號(hào)燈。</p><p>　　在我們的方法中，聚類算法與道路使用者的價(jià)值函數(shù)是用來(lái)確定每個(gè)交通燈的最優(yōu)決策的，這項(xiàng)決定是基于所

5、有道路使用者站在交通路口累積投票，通過(guò)估計(jì)每輛車的好處(或收益)來(lái)確定綠燈時(shí)間增益值與總時(shí)間是有差異的，它希望在它往返的時(shí)候等待，如果燈是紅色，或者燈是綠色。等待，直到車輛到達(dá)目的地，通過(guò)有聚類算法的基礎(chǔ)設(shè)施，最后經(jīng)過(guò)監(jiān)測(cè)車的監(jiān)測(cè)。</p><p>　　我們對(duì)自己的聚類算法模型和其它使用綠燈模擬器的系統(tǒng)做了比較。綠燈模擬器是一個(gè)交通模擬器，監(jiān)控交通流量統(tǒng)計(jì)，如平均等待時(shí)間，并測(cè)試不同的交通燈控制器。結(jié)果表明，在擁

6、擠的交通條件下，聚類控制器性能優(yōu)于其它所有測(cè)試的非自適應(yīng)控制器，我們也測(cè)試?yán)碚撋系钠骄却龝r(shí)間，用以選擇車輛通過(guò)市區(qū)的道路，并表明，道路使用者采用合作學(xué)習(xí)的方法可避免交通瓶頸。</p><p>　　本文安排如下：第2部分?jǐn)⑹鋈绾谓⒔煌Ｐ?，預(yù)測(cè)交通情況和控制交通。第3部分是就相關(guān)問(wèn)題得出結(jié)論。第4部分說(shuō)明了現(xiàn)在正在進(jìn)一步研究的事實(shí)，并介紹了我們的新思想。</p><p>　　The ti

7、mes 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 a

8、s being crowded,controlling against rules to give a technical improvement.With the fact that the large-scale integrated circuit and the computer art promptness develop,as well as artificial intelligence broad in the fiel

9、d of control technique applies,intelligence equipment </p><p>　　Transportation research has the goal to optimize transportation flow of people and goods.As the number of road users constantly increases, and

10、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. Avoid

11、ingtraffic jams for example is thought to be beneficial to both environment and economy, butimproved traffic-flow may also lead to an inc</p><p>　　There are several models for traffic simulation. In our rese

12、arch 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 Schrecke

13、nberg, 1992, Wahle and Schreckenberg, 2001].</p><p>　　Cars in urban traffic can experience long travel times due to inefficient traffic light con-trol. Optimal control of traffic lights using sophisticated s

14、ensors 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 i

15、s acomplex optimization problem and several intelligent algorithms, such as fuzzy logic, evo-lutionary algorithms, and reinforc</p><p>　　In our approach, reinforcement learning [Sutton and Barto, 1998, Kaelb

16、ling 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 junc

17、tion, 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 ligh</p>

18、<p>　　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 infrastructure

19、s and traffic patterns, 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