Dealing with noise in crowdsourced GPS human trajectory logging data

Kiki Adhinugraha, Wenny Rahayu, Takahiro Hara, David Taniar

Research output: Contribution to journalArticleResearchpeer-review

3 Citations (Scopus)

Abstract

As a crowdsourcing map platform, OpenStreetMap (OSM) relies on public contributions to enhance its dataset where the contributors can create, modify or remove features from the maps or share their trajectory trips in the repository. The majority of the data provided in a crowdsourcing platform are manually created and reviewed to suit real-world conditions, hence human perception is the key indicator to consider the correctness of the data. One of the data that is provided by crowdsourcing platform is public trajectory. Public trajectory data contains details of historical trips obtained from contributors' GPS logger devices that are embedded in mobile devices, wearable devices, satnavs, or vehicle GPS trackers to record the user's trajectory path. While public trajectory data can be used as an alternate data source for human movement analysis, this crowdsourced dataset is also prone to noise and inaccuracy which makes the preprocessing step an important phase prior of any processing step. In this article, we discuss the characteristics and the most common noise from crowdsourcing GPS trajectories and utilize a non-map-matching approach convex hull-based reduction method to minimize spike noise, followed by granularity reduction to reduce the number of trajectory points while maintaining the nature of the trajectories.

Original languageEnglish
Article numbere6139
Number of pages18
JournalConcurrency Computation: Practice and Experience
Volume33
Issue number19
DOIs
Publication statusPublished - 10 Oct 2021

Keywords

  • noise classification
  • noise reduction
  • trajectory granularity
  • trajectory noise

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