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基于现有的研究方法,提出了一种基于联合卡尔曼滤波器的交通信息融合算法。首先利用浮动车的覆盖率和浮动车的历史平均行程时间来修正浮动车的平均行程时间估计值,用于浮动车覆盖率不满足最小覆盖率时的路段平均行程时间估计;利用路段上浮动车的覆盖率来确定联合卡尔曼滤波器的融合系数;利用联合卡尔曼滤波器对固定型检测器的平均行程时间估计和浮动车的平均行程时间估计进行信息融合,得到路段的平均行程时间估计。该融合方法计算量小,融合时间快,有利于实际应用。试验结果表明,该方法提高了区间平均行程时间估计的精度。
Based on the existing research methods, a traffic information fusion algorithm based on joint Kalman filter is proposed. Firstly, the floating car’s coverage and the historical average travel time of the floating car are used to correct the estimated average travel time of the floating car, which is used to estimate the average travel time of the car when the coverage of the floating car does not meet the minimum coverage. Using the floating car The fusion coefficient of the joint Kalman filter is determined by the coverage ratio. The joint Kalman filter is used to fuse the estimated average travel time of the stationary detector and the estimated average travel time of the floating vehicle, and the estimated average travel time of the road is obtained. The fusion method has the advantages of small calculation amount and fast integration time, which is beneficial to practical application. The experimental results show that this method improves the accuracy of interval average travel time estimation.