| 一种众源车载GPS轨迹大数据自适应滤选方法 |
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| 引用本文： | 唐炉亮,杨雪,牛乐,常乐,李清泉.一种众源车载GPS轨迹大数据自适应滤选方法[J].测绘学报,2016,45(12):1455-1463. |
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| 作者姓名： | 唐炉亮 杨雪 牛乐 常乐 李清泉 |
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| 作者单位： | 1. 武汉大学测绘遥感信息工程国家重点实验室, 湖北 武汉 430079;2. 深圳大学 土木工程学院 空间信息智能感知与服务深圳市重点实验室, 广东 深圳 518060 |
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| 基金项目： | 国家自然科学基金(41671442 |
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| 摘 要： | 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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| 关 键 词： | 众源轨迹数据 轨迹分割 相似度模型 数据滤选 大数据 |
| 收稿时间： | 2016-03-30 |
| 修稿时间： | 2016-10-27 |
An Adaptive Filtering Method Based on Crowdsourced Big Trace Data |
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| TANG Luliang,YANG Xue,NIU Le,CHANG Le,LI Qingquan.An Adaptive Filtering Method Based on Crowdsourced Big Trace Data[J].Acta Geodaetica et Cartographica Sinica,2016,45(12):1455-1463. |
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| Authors: | TANG Luliang YANG Xue NIU Le CHANG Le LI Qingquan |
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| Institution: | 1. State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China;2. Shenzhen Key Laboratory of Spatial Smart Sensing and Services, College of Civil Engineering, Shenzhen University, Shenzhen 518060, China |
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| Abstract: | 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. |
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| Keywords: | crowdsourced trace trajectories partition similarity model data filtering big data |
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