Observing and mitigating errors in passively collected mobile device data for travel behavior modeling

Keywords: mobile device data, human mobility, transportation geography, data science, data quality
Abstract Type: Paper Abstract

Abstract

GPS trajectory data passively collected by mobile devices has been widely adopted in multiple
fields for its advantages in revealing travel behaviors and has greatly facilitate human-centered
geospatial research. Data quality assessments and preprocessing have always been indispensable
steps for exploring travel behaviors embedded in the data. However, the impact of trip-level
errors have not been studied to the same degree in previous studies. In this research, we examine a
newly emerged type of error present at trip-level in mobile device datasets that violates the
spatio-temporal consistency of such data by including trips on road segments where and when
there should be no trips. We designed a distributed-computing workflow to quantify the errors by
comparing the number of trips on closed road segments during road closures with time periods
before and after the closures. Using two real-world cases from 2023, we examined multiple
datasets acquired from major vendors in the US, where several of the datasets contained a
significant number of trip-level errors. These findings point to the errors being present in recent
datasets that have not otherwise been processed for data quality and can significantly impact
analyses by data users. Data users should consider conducting trip-level error data quality checks
as part of their preprocessing steps.