一种众源车载GPS轨迹大数据自适应滤选方法

唐炉亮; 杨雪; 牛乐; 常乐; 李清泉

doi:10.11947/j.AGCS.2016.20160117

测绘学报 >

2016 , Vol. 45 >Issue 12: 1455 - 1463

DOI: https://doi.org/10.11947/j.AGCS.2016.20160117

地图学与地理信息

一种众源车载GPS轨迹大数据自适应滤选方法

唐炉亮 ,
杨雪 ,
牛乐 ,
常乐 ,
李清泉

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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

唐炉亮(1973-),男,博士,教授,研究方向为GIS-T、时空GIS、轨迹大数据挖掘等。E-mail:tll@whu.edu.cn

收稿日期: 2016-03-30

修回日期: 2016-10-27

网络出版日期: 2017-01-02

基金资助

国家自然科学基金（41671442；41571430；41271442）

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An Adaptive Filtering Method Based on Crowdsourced Big Trace Data

TANG Luliang ,
YANG Xue ,
NIU Le ,
CHANG Le ,
LI Qingquan

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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

Received date: 2016-03-30

Revised date: 2016-10-27

Online published: 2017-01-02

Supported by

The National Natural Science Foundation of China (Nos.41671442,41571430,41271442)

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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。

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

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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 . DOI: 10.11947/j.AGCS.2016.20160117

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.

Key words： crowdsourced trace; trajectories partition; similarity model; data filtering; big data

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