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本文以城市出租车为浮动车数据采集源,介绍了基于GPS数据的实时路段速度估计的基本方法。针对目标路段GPS数据样本量不足的情况,考虑邻近区域的路段速度、上周同日速度、前一时刻速度等与目标路段当前时刻速度等密切相关的变量,建立多元线性回归方程,利用卡尔曼滤波融合预测值和测量值,从而提高路段行驶速度的估计精度。选择广州市东风路作为测试实例,融合值比测量值误差降低9%,绝对相对误差变动系数减少4%,表明结合卡尔曼滤波技术的城市路段速度估计精度和稳定性均得到提高。
In this paper, city taxi as a floating car data acquisition source, introduced based on GPS data of real-time road speed estimation method. Aiming at the lack of sample GPS data in the target section, a multivariate linear regression equation is established considering variables such as the link speed in the adjacent region, the same day last week speed, the previous time speed and the current time speed of the target section, and the Kalman filter The forecast value and the measurement value are merged, so as to improve the estimation accuracy of the travel speed of the road section. Choosing Dongfeng Road in Guangzhou as a test case, the error of fusion value is reduced by 9% and the absolute relative error variation coefficient is reduced by 4%, which shows that both the accuracy and the stability of urban road speed estimation based on Kalman filter technology are improved.