Nowadays, the intelligent transportation concept has become one of the most important research fields. All of us depend on mobility, even when we talk about people, provide services, or move goods. Researchers have tried to create and test different transportation models that can optimize traffic flow through road networks and, implicitly, reduce travel times. To validate these new models, the necessity of having a calibration process defined has emerged. Calibration is mandatory in the modeling process because it ensures the achievement of a model closer to the real system. The purpose of this paper is to propose a new multidisciplinary approach combining microscopic traffic modeling theory with intelligent control systems concepts like fuzzy inference in the traffic model calibration. The chosen Takagi–Sugeno fuzzy inference system proves its adaptive capacity for real-time systems. This concept will be applied to the specific microscopic car-following model parameters in combination with a Kalman filter. The results will demonstrate how the microscopic traffic model parameters can adapt based on real data to prove the model validity.

中文翻译：

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卡尔曼滤波与Takagi–Sugeno模糊推理系统相结合的混合模型在线校正模型

如今，智能交通概念已成为最重要的研究领域之一。即使当我们谈论人，提供服务或转移商品时，我们所有人都依赖于流动性。研究人员已经尝试创建和测试不同的运输模型，这些模型可以优化通过道路网络的交通流量，并隐式地减少出行时间。为了验证这些新模型，已经出现了定义校准过程的必要性。在建模过程中，校准是必不可少的，因为它可以确保实现更接近实际系统的模型。本文的目的是提出一种新的多学科方法，将微观交通建模理论与智能控制系统概念（如交通模型校准中的模糊推理）相结合。选择的Takagi–Sugeno模糊推理系统证明了其对实时系统的自适应能力。结合卡尔曼滤波器，该概念将应用于特定的微观汽车跟随模型参数。结果将证明微观交通模型参数如何根据实际数据进行调整以证明模型的有效性。