Impact of data processing on deriving micro-mobility patterns from vehicle availability data
(2021) In Transportation Research Part D: Transport and Environment 97.- Abstract
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... (More)
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.
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- author
- Zhao, Pengxiang LU ; Haitao, He ; Li, Aoyong and Mansourian, Ali LU
- organization
- publishing date
- 2021-08
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Data processing, Data sampling, E-scooter sharing, GPS, Micro-mobility, Spatio-temporal patterns, Trip identification, Vehicle availability data
- in
- Transportation Research Part D: Transport and Environment
- volume
- 97
- article number
- 102913
- publisher
- Elsevier
- external identifiers
-
- scopus:85108428903
- ISSN
- 1361-9209
- DOI
- 10.1016/j.trd.2021.102913
- language
- English
- LU publication?
- yes
- id
- 70aa21bf-4a66-4d88-b9a1-7db8db8d0659
- date added to LUP
- 2021-07-02 22:41:12
- date last changed
- 2022-07-08 13:21:57
@article{70aa21bf-4a66-4d88-b9a1-7db8db8d0659, abstract = {{<p>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.</p>}}, author = {{Zhao, Pengxiang and Haitao, He and Li, Aoyong and Mansourian, Ali}}, issn = {{1361-9209}}, keywords = {{Data processing; Data sampling; E-scooter sharing; GPS; Micro-mobility; Spatio-temporal patterns; Trip identification; Vehicle availability data}}, language = {{eng}}, publisher = {{Elsevier}}, series = {{Transportation Research Part D: Transport and Environment}}, title = {{Impact of data processing on deriving micro-mobility patterns from vehicle availability data}}, url = {{http://dx.doi.org/10.1016/j.trd.2021.102913}}, doi = {{10.1016/j.trd.2021.102913}}, volume = {{97}}, year = {{2021}}, }