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Impact of data processing on deriving micro-mobility patterns from vehicle availability data
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DOI：10.1016/j.trd.2021.102913.png)
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Abstract
En 中文
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 micromobility 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.
Keywords:
Micro-mobility
E-scooter sharing
Data processing
Data sampling
Spatio-temporal patterns
Vehicle availability data
GPS
Trip identification
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