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Impact of data processing on deriving micro-mobility patterns from vehicle availability data
journal contribution
posted on 2021-06-30, 08:33 authored by Pengxiang Zhao, Haitao HeHaitao He, Aoyong Li, Ali MansourianVehicle 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.
Funding
This research has been supported by the QR Strategic Priorities Fund provided by Research England.
History
School
- Architecture, Building and Civil Engineering
Published in
Transportation Research Part D: Transport and EnvironmentVolume
97Publisher
Elsevier BVVersion
- AM (Accepted Manuscript)
Rights holder
© ElsevierPublisher statement
This paper was accepted for publication in the journal Transportation Research Part D: Transport and Environment and the definitive published version is available at https://doi.org/10.1016/j.trd.2021.102913.Publication date
2021-06-23Copyright date
2021ISSN
1361-9209Publisher version
Language
- en
Depositor
Dr Haitao He. Deposit date: 30 June 2021Article number
102913Usage metrics
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