交通燈控制系統(tǒng)外文翻譯-交通線路-全文預(yù)覽
【正文】 增加基礎(chǔ)設(shè)施的能力 。 2 建立和控制交通 在這一部分，我們專注于在交通運(yùn)輸方面所使用的信息技術(shù)。在第三部分解釋了什么是強(qiáng)化學(xué)習(xí)和一些它的應(yīng)用。 在其余路程，它的所有等待時(shí)間里，如果信號(hào)燈現(xiàn)在是紅色的或者綠色的，那么增益的值是不同的。 交通燈控制是一個(gè)復(fù)雜的優(yōu)化問(wèn)題和幾個(gè)智能算法，例如模糊邏輯、遺傳算 法和強(qiáng)化學(xué)習(xí)（ RL）已被應(yīng)用去試圖解決問(wèn)題。 研究表明， 這種模型的出 現(xiàn)具有現(xiàn)實(shí)意義 [Nagel and Schreckenberg， 1992， Wahle and Schreckenberg， 2021]。避免交通堵塞，例如，被認(rèn)為是對(duì)環(huán)境和經(jīng)濟(jì)有益的，但是增加的交通流也可能導(dǎo)致資源需求的增加。實(shí)驗(yàn)結(jié)果表明，在所有基礎(chǔ)設(shè)施的研究領(lǐng)域內(nèi)，我們的自適應(yīng)交通燈控制器優(yōu)于其他固定的控制器。 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 realtime simulator, predictions on actual traffic can be made. A system installed in Duisburg uses information from the existing traffic control center and produces realtime information on the Inter. Another system was installed on the freeway system of North RhineWestphalia, using data from about inductive loops to predict traffic on 6000 km of roads. 中文譯 文 智能交通燈控制 馬克 威寧，簡(jiǎn)麗 范 威 ，吉爾 威瑞肯，安瑞 庫(kù)普曼 智能系統(tǒng)小組 烏得勒支大學(xué) 信息與計(jì)算科學(xué)研究所 荷蘭烏得勒支 Padualaan14 號(hào) 郵箱： 2021 年 7 月 9 日 摘要 世界各地的車(chē)輛運(yùn)行逐漸增多，尤其是在一個(gè)大的本地區(qū)域。 macroscopic and microscopic models. Macroscopic models. Macroscopic traffic models are based on gaskiic models and use equations relating traffic density to velocity [Lighthill and Whitham, 1955, Helbing et al., 2021]. These equations can be extended with terms for buildup and relaxation of pressure to account for phenomena like stopandgo traffic and spontaneous congestions [Helbing et al., 2021, Jin and Zhang, 2021, 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. 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 roadcell 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 timestep, vehicles increase their speed by a certain amount until they reach their maximum velocity. In case of a slower moving v
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