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An approach for real-time urban traffic state estimation by fusing multisource traffic data

机译:一种融合多源交通数据的实时城市交通状态估计方法

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Data fusion is an important tool for estimating urban traffic state when various traffic data are available. In order to get more accurate and comprehensive traffic state, this paper proposes an improved reliability revaluated Dempster- Shafer fusion algorithm (RRDSF) and a framework of real-time traffic state estimation system for fusing multi-source data, tests on the accuracy by real-world traffic data. The framework of real-time traffic state estimation system proposed in this paper shows the feasibility of developing advanced data fusion system for real-time traffic state estimation. The results report in this paper demonstrate that the proposed model can fuse data from loop detectors and probe vehicles to more accurately obtain traffic state estimation than using either of them alone and encourage us to do further work.
机译:当可获得各种交通数据时,数据融合是估算城市交通状态的重要工具。为了获得更准确,更全面的交通状态,提出了一种改进的可靠性重估的Dempster-Shafer融合算法(RRDSF)和用于融合多源数据的实时交通状态估计系统的框架,通过真实性对准确性进行测试世界交通数据。本文提出的实时交通状态估计系统框架表明了开发高级数据融合系统进行实时交通状态估计的可行性。本文的结果报告表明,该模型可以融合来自环路检测器和探测车辆的数据,从而比单独使用其中任何一个更准确地获得交通状态估计,并鼓励我们做进一步的工作。

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