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A High-Efficiency Mobile Positioning System by Using Commercial Vehicle Operation Data Based on Cloud Computing Techniques

机译:基于云计算技术的商用车辆运行数据,一种高效的移动定位系统

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In recent year, consumption and logistics pattern have changed with the development of economy and gradual increase of national income. Commercial vehicle operation (CVO) has been an popular application of intelligent transportation systems. The location determination of on-board unit (OBU) in car is an important issue for CVO. Therefore, his study proposes a high-efficiency mobile positioning method to analyze the cellular network signals of CVO data. The method is also combined the cloud computing techniques to quickly determine the location of OBU for CVO. In experiments, this study compares the estimated positioning information with the real information obtained from global position system receiver. The results show that the average errors of location determination by using the proposed method and traditional cell ID based location method are 163.7 meters and 521.2 meters, respectively. Therefore, using the proposed method provides better accuracies of location determination than using cell ID based location method defined in the location service specification. This approach is feasible to determine the location of OBU for CVO improvement.
机译:近年来,消费和物流模式随着经济的发展和国民收入的逐步增加而变化。商用车运营(CVO)一直是智能交通系统的热门应用。汽车中板载单元(OBU)的位置确定是CVO的重要问题。因此,他的研究提出了一种高效的移动定位方法来分析CVO数据的蜂窝网络信号。该方法还结合了云计算技术以快速确定OBU对于CVO的位置。在实验中,该研究将估计的定位信息与从全球位置系统接收器获得的真实信息进行比较。结果表明,使用所提出的方法和基于传统的小区ID的位置法测定位置测定的平均误差分别为163.7米和521.2米。因此,使用所提出的方法提供的位置确定比使用位置服务规范中定义的基于小区ID的位置方法更好的位置确定。这种方法是可行的,可以确定OBU的CVO改善的位置。

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