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Error Measures for Trajectories Estimations with Geo-tagged Mobility Sample Data.

Mohsen Parsafard1, Guangqing Chi2, Xiaobo Qu3

  • 1Department of Civil and Environmental Engineering, University of South Florida, Tampa, FL 33620, USA.

IEEE Transactions on Intelligent Transportation Systems : a Publication of the IEEE Intelligent Transportation Systems Council
|July 24, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces new measures to assess the accuracy of travel trajectories estimated from sparse geo-tagged mobility data. These methods help identify reliable human mobility data for analysis.

Keywords:
Geo-tagged dataactivity rangecellphonesocial mediatime geographytrajectory estimation

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Area of Science:

  • Geographic Information Science
  • Mobility Data Analysis
  • Human Geography

Background:

  • Geo-tagged mobility data (e.g., cell phone, social media) offer insights into travel trajectories but suffer from low sampling rates.
  • Sparse data requires interpolation, introducing uncertainty in estimating complete space-time paths.
  • Accurate assessment of trajectory estimation error is crucial for reliable human mobility studies.

Purpose of the Study:

  • To propose novel time geography-based measures for quantifying trajectory estimation accuracy from sparsely sampled mobility data.
  • To develop methods for evaluating the reliability of individual trajectories for travel mobility analysis.
  • To establish criteria for distinguishing between useful, low-error data and noisy, high-error data.

Main Methods:

  • Introduction of activity bandwidth and normalized activity bandwidth measures to quantify absolute and relative error ranges.
  • Theoretical analysis demonstrating that error measures decrease with increased sample rates and activity ranges.
  • Development of a lookup table-based interpolation method to improve computational efficiency.

Main Results:

  • The proposed measures effectively quantify estimation errors for sparsely sampled geo-tagged mobility data.
  • Theoretical analysis confirms the inverse relationship between sample rates/activity ranges and estimation errors.
  • Application to New York City (tweets) and Shenzhen (cell phone) data demonstrated faster error estimation compared to benchmark methods.

Conclusions:

  • The developed measures provide a robust framework for assessing the quality of geo-tagged mobility data for human mobility research.
  • The proposed methods enable efficient evaluation of large-scale datasets, revealing data quality distributions.
  • These findings are vital for understanding the suitability and limitations of digital trace data in mobility studies.