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A Data Cleaning Method for Big Trace Data Using Movement Consistency.

Xue Yang1, Luliang Tang2, Xia Zhang3

  • 1State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China. yangxue@whu.edu.cn.

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This study introduces a novel data cleaning method for vehicle GPS traces, enhancing urban geographic information extraction. The approach uses movement consistency to improve the quality of spatial big data from GPS trajectories.

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Area of Science:

  • Geographic Information Science
  • Data Science
  • Transportation Engineering

Background:

  • Vehicle-generated spatiotemporal GPS traces represent a valuable big data source for urban analysis.
  • Challenges include processing large volumes and managing data quality issues that introduce uncertainty in human activity studies.

Purpose of the Study:

  • To propose a data cleaning method for spatial big data derived from vehicle GPS traces.
  • To address processing difficulties and uncertainties caused by low-quality GPS data.

Main Methods:

  • A novel data cleaning method based on movement consistency is developed.
  • Trajectories are segmented into sub-trajectories using movement characteristic points.
  • A movement consistency model, built with the random sample consensus algorithm, is applied to clean GPS data.

Main Results:

  • The proposed method effectively cleans spatial big data from vehicle GPS trajectories.
  • Extensive experiments demonstrate the method's effectiveness and efficiency.
  • The approach leverages the error distribution law and position accuracy of GPS data.

Conclusions:

  • The movement consistency-based data cleaning method significantly improves the quality of vehicle GPS trajectory data.
  • This enhances the reliability of urban geographic information extraction from spatial big data.
  • The method offers an efficient solution for managing and processing large-scale GPS datasets.