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

Zhao, Pengxiang LU ; Haitao, He ; Li, Aoyong and Mansourian, Ali LU (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
; ; and
organization
publishing date
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}},
}