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首页> 外文会议>Asia Simulation Conference/International Conference on System Simulation and Scientific Computing >A New Dynamic OD Estimation Method in ITS Based on Fuzzy DTA Model and Kalman Filtering
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A New Dynamic OD Estimation Method in ITS Based on Fuzzy DTA Model and Kalman Filtering

机译：基于模糊DTA模型和卡尔曼滤波的新动态OD估计方法

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摘要

In this paper, dynamic OD matrix estimation and prediction from real-time link counts based on fuzzy dynamic traffic assignment (FDTA) and Kalman Filtering was proposed. At first, the state-space model of dynamic OD matrix estimation and prediction was established. To obtain the key assignment matrix in measurement equation of the state-space model, fuzzy dynamic traffic assignment (FDTA) was applied based on C-LOGIT model and fuzzy shortest path (FSP) algorithm in which fuzzy path travel time (FPTT) was constructed from real-time link counts. Then Kalman Filtering algorithm iterated with FDTA was used for dynamic OD estimation and prediction. Simulation test shows that compared with K.Ashok's Kalman Filter model, the mean estimation error and the mean square error of FDTA-based Kalman Filter model are improved greatly, which exactly means more precision and better dynamic characteristics of dynamic OD estimation and prediction could be obtained.
机译：在本文中，提出了基于模糊动态流量分配（FDTA）和卡尔曼滤波的实时链路计数的动态OD矩阵估计和预测。首先，建立了动态​​OD矩阵估计和预测的状态空间模型。为了获得状态空间模型的测量方程中的关键分配矩阵，基于C-Logit模型和模糊的最短路径（FSP）算法来应用模糊动态流量分配（FDTA），在该模糊路径（FPTT）构建从实时链接计数。然后，使用FDTA迭代的卡尔曼滤波算法用于动态OD估计和预测。仿真试验表明，与K.ashok的卡尔曼滤波器模型相比，平均估计误差和基于FDTA的卡尔曼滤波器模型的平均误差大大提高，这完全是指动态OD估计和预测的更精确和更好的动态特性获得。

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