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Pengxiang Zhao

Researcher

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Impact of data processing on deriving micro-mobility patterns from vehicle availability data

Author

  • Pengxiang Zhao
  • He Haitao
  • Aoyong Li
  • Ali Mansourian

Summary, in English

Vehicle availability data is emerging as a potential data source for micro-mobility research and applications. However, there is not yet research that systematically evaluates or validates the processing of this emerging mobility data. To fill this gap, we propose a generally applicable data processing framework and validate its related algorithms. The framework exploits micro-mobility vehicle availability data to identify individual trips and derive aggregate patterns by evaluating a range of temporal, spatial, and statistical mobility descriptors. The impact of data processing is systematically and rigorously investigated by applying the proposed framework with a case study dataset from Zurich, Switzerland. Our results demonstrate that the sampling rate used when collecting vehicle availability data has a significant and intricate impact on the derived micro-mobility patterns. This research calls for more attention to investigate various issues with emerging mobility data processing to ensure its validity for transportation research and practices.

Department/s

  • Dept of Physical Geography and Ecosystem Science
  • Centre for Geographical Information Systems (GIS Centre)

Publishing year

2021-08

Language

English

Publication/Series

Transportation Research Part D: Transport and Environment

Volume

97

Document type

Journal article

Publisher

Elsevier

Topic

  • Earth and Related Environmental Sciences
  • Engineering and Technology

Keywords

  • Data processing
  • Data sampling
  • E-scooter sharing
  • GPS
  • Micro-mobility
  • Spatio-temporal patterns
  • Trip identification
  • Vehicle availability data

Status

Published

ISBN/ISSN/Other

  • ISSN: 1361-9209