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Travel-mode classification based on GPS-trajectory data and geographic information using an XGBoost classifier

IOP conference series. Earth and environmental science, 2022-03, Vol.1004 (1), p.12012 [Peer Reviewed Journal]

Published under licence by IOP Publishing Ltd ;Published under licence by IOP Publishing Ltd. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1755-1307 ;EISSN: 1755-1315 ;DOI: 10.1088/1755-1315/1004/1/012012

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  • Title:
    Travel-mode classification based on GPS-trajectory data and geographic information using an XGBoost classifier
  • Author: Jin, Huiling ; Wu, Hangbin ; Xu, Zeran ; Huang, Wei ; Liu, Chun
  • Subjects: Classification ; Classifiers ; Datasets ; Global positioning systems ; GPS ; Resampling ; Segmentation ; Transportation planning ; Travel ; Travel modes ; Workflow
  • Is Part Of: IOP conference series. Earth and environmental science, 2022-03, Vol.1004 (1), p.12012
  • Description: Abstract Massive Global Positioning System (GPS) trajectory datasets are being produced owing to the advances in mobile sensors, the Internet, and GPS devices. Accurately inferring travel modes from GPS trajectory data can be helpful in transportation planning and modeling, infrastructure design, etc. However, adverse factors such as data noise, differences in sampling rate, and inadequate features have a negative impact on the results of travel mode classification. In this paper, to address such issues, we first propose a preprocessing workflow, which includes data cleaning, segmentation, and resampling, to preprocess raw trajectories. Then, we add new features related to the road and bus stop information for travel mode classification using an XGBoost (eXtreme Gradient Boosting) classifier, along with various basic features of the trajectories. We conducted a set of experiments on the GeoLife dataset using a group of state-of-the-art methods. The results showed that the proposed methods can improve the classification accuracy by using all the classifiers we compared and the classification accuracy using the XGBoost classifier can reach a maximum of 90.41%.
  • Publisher: Bristol: IOP Publishing
  • Language: English
  • Identifier: ISSN: 1755-1307
    EISSN: 1755-1315
    DOI: 10.1088/1755-1315/1004/1/012012
  • Source: Open Access: IOP Publishing Free Content
    IOPscience (Open Access)
    Alma/SFX Local Collection
    ProQuest Central
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