An Adaptive Filtering Method Based on Crowdsourced Big Trace Data

doi:10.11947/j.AGCS.2016.20160117

Abstract

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

CLC Number:

P228

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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