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Fuzzy Cellular Automata Model for Signalized Intersections

机译：信号交叉口的模糊元胞自动机模型

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摘要

At signalized intersections, the decision-making process of each individual driver is a very complex process that involves many factors. In this article, a fuzzy cellular automata (FCA) model, which incorporates traditional cellular automata (CA) and fuzzy logic (FL), is developed to simulate the decision-making process and estimate the effect of driving behavior on traffic performance. Different from existing models and applications, the proposed FCA model utilizes fuzzy interface systems (FISs) and membership functions to simulate the cognition system of individual drivers. Four FISs are defined for each decision-making process: car-following, lane-changing, amber-running, and right-turn filtering. A field observation study is conducted to calibrate membership functions of input factors, model parameters, and to validate the proposed FCA model. Simulation experiments of a two-lane system show that the proposed FCA model is able to replicate decision-making processes and estimate the effect on overall traffic performance.
机译：在信号交叉口，每个驾驶员的决策过程是一个非常复杂的过程，涉及许多因素。在本文中，开发了一种融合了传统细胞自动机（CA）和模糊逻辑（FL）的模糊细胞自动机（FCA）模型，以模拟决策过程并估算驾驶行为对交通性能的影响。与现有模型和应用程序不同，本文提出的FCA模型利用模糊接口系统（FIS）和隶属函数来模拟单个驾驶员的认知系统。为每个决策过程定义了四个FIS：跟车，变道，琥珀行驶和右转过滤。进行了现场观察研究，以校准输入因子，模型参数的隶属函数，并验证所提出的FCA模型。两车道系统的仿真实验表明，所提出的FCA模型能够复制决策过程并估计对整体交通性能的影响。

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