Extracting Trips from Multi-Sourced Data for Mobility Pattern Analysis: An App-Based Data Example

Abstract

Passively-generated data, such as GPS data and cellular data, bring tremendous opportunities for human mobility analysis and transportation applications. Since their primary purposes are often non-transportation related, the passively-generated data need to be processed to extract trips. Most existing trip extraction methods rely on data that are generated via a single positioning technology such as GPS or triangulation through cellular towers (thereby called single-sourced data), and methods to extract trips from data generated via multiple positioning technologies (or, multisourced data) are absent. And yet, multi-sourced data are now increasingly common. Generated using multiple technologies (e.g., GPS, cellular network- and WiFi-based), multi-sourced data contain high variances in their temporal and spatial properties. In this study, we propose a ``Divide, Conquer and Integrate'' (DCI) framework to extract trips from multi-sourced data. We evaluate the proposed framework by applying it to an app-based data, which is multisourced and has high variances in both location accuracy and observation interval (i.e. time interval between two consecutive observations). The effectiveness of the framework is illustrated by consistent mobility patterns obtained from the app-based data and an externally collected household travel survey data for the same region and the same period.

Jinzhou Cao(曹劲舟)

Assistant Professor

My research interests Urban big data mining, Geo-AI and Urban Analytics.