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Dynamic estimation of freeway origin-destination demand and travel time using extended Kalman filtering algorithm

机译:扩展卡尔曼滤波算法动态估算高速公路始发地-目的地需求和行驶时间

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In the present research, a nonlinear Kalman filtering approach, i.e., extended Kalman filter (EKF) was proposed to solve dynamic OD flows and travel times on a freeway segment. The non-linearity results from the facts that the coefficient matrices in the measurement equation of the Kalman filtering framework are unknown in advance and needed to be obtained/updated in light of the most recent observations. The numerical results demonstrated the capability of the proposed EKF model in the dynamic estimation of freeway OD demands and travel times. More significantly, one can design beneficial traffic control and management strategies in accordance with the estimation results.
机译:在本研究中,提出了一种非线性卡尔曼滤波方法,即扩展卡尔曼滤波器(EKF),以解决高速公路段的动态OD流程和旅行时间。非线性结果来自克尔曼滤波框架的测量方程中的系数矩阵预先未知并且需要根据最近的观察结果获得/更新。数值结果证明了所提出的EKF模型在高速公路OD需求和旅行时间的动态估计中的能力。更重要的是,人们可以根据估计结果设计有益的交通管制和管理策略。

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