交通燈控制系統(tǒng)外文翻譯-交通線路-在線瀏覽
2025-03-24 00:17本頁(yè)面
【正文】 conditions will be. Finally, we describe research in which all cars are controlled using puters. 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 mon approaches for modelling traffic。 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 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 startstop waves when traffic density increases. Cognitive MultiAgent Systems. A more advanced approach to traffic simulation and optimization is the Cognitive MultiAgent System approach (CMAS), in which agents interact and municate with each other and the infrastruc ture. 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 (., by using learning capabilities) its way of
畢業(yè)設(shè)計(jì)相關(guān)推薦
鄂ICP備17016276號(hào)-1