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.

车辆可用性数据正在成为微型移动研究和应用的潜在数据源。然而，目前还没有研究系统地评估或验证这种新兴移动数据的处理。为了填补这一空白，我们提出了一个普遍适用的数据处理框架并验证其相关算法。该框架利用微型移动车辆可用性数据来识别个人行程并通过评估一系列时间、空间和统计移动描述符来推导出聚合模式。通过将提议的框架与来自瑞士苏黎世的案例研究数据集相结合，系统和严格地研究了数据处理的影响。我们的结果表明，收集车辆可用性数据时使用的采样率对衍生的微移动模式具有重大而复杂的影响。这项研究呼吁更多地关注新兴移动数据处理的各种问题，以确保其对交通研究和实践的有效性。