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通过对浮动车定位数据情况的分析,可知大部分速度估计算法仅适用于采样时间间隔不大于行程时间的情况,为在相同浮动车比例以及采样时间间隔的条件下,提高数据利用率,以提高速度估计结果的路网覆盖率,提出两种速度估计算法:车辆跟踪法、速度-距离积分法,并给出路段区间平均速度自适应估计模型。使用真实交通流OD数据进行仿真,结果表明在相同的浮动车定位数据情况下,使用自适应估计模型可比速度-时间积分法获得更高的路网覆盖率,且所得的速度估计结果的误差与速度-时间积分法处于同一水平。
Through the analysis of the situation of the floating car locating data, it can be seen that most of the speed estimation algorithms are only applicable to the case that the sampling time interval is not greater than the travel time. In order to improve data utilization under the same floating car ratio and sampling time interval, Speed estimation results of the road network coverage, proposed two kinds of speed estimation algorithm: vehicle tracking method, speed - distance integral method, and gives the interval section average speed adaptive estimation model. The simulation results using the real traffic flow OD data show that using the adaptive estimation model can get a higher coverage of the road network than the speed-time integration method under the same situation of the floating car positioning data, and the error of the obtained speed estimation result and Speed - time integral method at the same level.