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.
Impact of data processing on deriving micro-mobility patterns from vehicle availability data
2021-01-01
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Analysis of Mobile Phone Data for Deriving City Mobility Patterns
Springer Verlag | 2017
|Deriving Traffic Flow Patterns from Historical Data
ASCE | 2012
|Deriving Traffic Flow Patterns from Historical Data
British Library Online Contents | 2012
|Deriving Traffic Flow Patterns from Historical Data
Online Contents | 2012
|Deriving traffic signal timing plans from connected vehicle trajectory data
Europäisches Patentamt | 2024
|