Vehicles’GPS traces collected by crowds have being as a new kind of big data and are widely applied to mine urban geographic information with low-cost,quick-update and rich-informative.However, the growing volume of vehicles’GPS traces has caused difficulties in data processing and their low quality adds uncertainty when information mining.Thus,it is a hot topic to extract high-quality GPS data from the crowdsourced traces based on the expected accuracy.In this paper,we propose an efficient partition-and-filter model to filter trajectories with expected accuracy according to the spatial feature of high-precision GPS data and the error rule of GPS data.First,the proposed partition-and-filter model to partition a trajectory into sub-trajectories based on the constrained distance and angle,which are chosen as the basic unit for the next processing step.Secondly,the proposed method collects high-quality GPS data from each sub-trajectory according to the similarity between GPS tracking points and the reference baselines constructed using random sample consensus algorithm.Experimental results demonstrate that the proposed method can effectively pick up high quality GPS data from crowdsourced trace data sets with the expected accuracy.%基于同步高低精度GPS轨迹数据的空间特征和GPS误差分布原理，提出了一种众源GPS车载轨迹大数据自适应分割-滤选模型。该模型首先通过角度、距离约束将完整的车载 GPS 轨迹数据进行分割，以轨迹分割段作为基本滤选单元；然后通过对比轨迹分割段内 GPS 轨迹向量与其参考基线间的相似度，按照相似度与GPS定位精度之间的量化关系指导滤选。试验结果表明，该方法可以实现车载轨迹大数据按信息提取精度需求的滤选。
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