Pengxiang Zhao
Researcher
Impact of data processing on deriving micro-mobility patterns from vehicle availability data
Author
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