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Passively-generated data, such as global positioning system (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. However, most existing trip extraction methods rely on single-sourced data that are generated relying on single positioning technology (eg, GPS and cellular towers), and trip extraction methods for multi-sourced data are sparse. Generated using multiple technologies (eg, GPS, cellular network and WiFi), multi-sourced data contain high variances in their temporal and spatial properties. As multi-sourced data (eg, app-based data) are emerging and becoming popular, there is a critical need to develop trip extraction methods using such data. In this study, the authors propose a ‘Divide, Conquer and Integrate’(DCI) framework to extract trips from multi-sourced data. The authors evaluate the proposed framework by applying it to an app-based data, which is multi-sourced and has high variances in both location accuracy and sample frequency. The effectiveness of the framework is illustrated by the consistency between mobility patterns analyzed based on the extracted trips from the app-based data and those from external data sets.