Work

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

Year: 2021

Type: article

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.

Source: Transportation Research Part D Transport and Environment

DOI: 10.1016/j.trd.2021.102913

Authors: Pengxiang Zhao, He Haitao, Aoyong Li, Ali Mansourian

Institutions: Lund University, Loughborough University, ETH Zurich, Institute for Transport Planning and Systems

Language: English

Topic: Transportation and Mobility Innovations

Subfield: Automotive Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Sustainable cities and communities, Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $2,620