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

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

Zhao, Pengxiang / Haitao, He / Li, Aoyong / Mansourian, Ali

Highlights • A general framework is proposed to derive micro-mobility patterns. • Framework only uses easily accessible micro-mobility vehicle availability data. • Data processing is systematically analyzed and validated. • A case study is conducted using e-scooter sharing datasets from Zurich, Switzerland.

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 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.