基于多源交通数据融合的实时城市交通状态估计融合结构模型

Pan Zhang, Lanlan Rui, Xue-song Qiu, Ruichang Shi
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引用次数: 3

摘要

为了满足实时城市交通状态估计对交通数据融合的要求,提出了一种新的融合结构模型。该融合模型包括空间融合和时间融合。首先采用功率平均算子作为空间融合方法。然后在分段线性回归(SLR)算法的基础上提出了一种基于时间相关的数据压缩(TCDC)算法。大量的仿真结果证明了TCDC算法的有效性和正确性,以及TCDC在整体性能上优于单反的优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A new fusion structure model for real-time urban traffic state estimation by multisource traffic data fusion
In order to meet the requirements of traffic data fusion for real-time urban traffic state estimation, a new kind of fusion structure model is proposed. This fusion model consists of both spatial fusion and temporal fusion. First we use the power average operator as spatial fusion method. Then we propose a temporal correlation based data compression (TCDC) algorithm, based on segment linear regression (SLR) algorithm. Extensive simulation results demonstrate the effectiveness and correctness of TCDC algorithm, as well as TCDC's advantage over SLR on overall performance.
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