Abstract. Mobility research is mainly concerned with understanding mobility on a higher level, including environmental factors, e.g., measuring the time out of home or tracking revisited places. This requires preprocessing the raw data obtained from GPS sensors, like clustering significant locations and distinguishing these from periods on the go. We introduce a new stop and trip detection algorithm to transform a list of position records into intervals of dwelling and transit. The system is based on geometrical analyses of the signal noise: Imperfect GPS data tends to scatter around an actual dwell position in a star-like pattern, and this imperfection is what we leverage for our classification. The system contains four independent classification methods, comparing different aspects of the geometrical properties of a given trajectory. If available, accelerometer readings can be used to improve the system’s accuracy further. To evaluate the classifier’s performance, we recorded a large dataset containing gold-standard labels and compared the classification results of our system with the results of Scikit Mobility and Moving Pandas. Our Stop Go Classifier outperforms the traditional distance/time-threshold-based systems. The described system is available as free software.
Abstract. Identifying stops and trips from raw GPS traces is a fundamental preprocessing step for most mobility research applications. Thus, ensuring the excellent accuracy of such systems is of high interest to researchers designing such analysis pipelines. While there are plenty of GPS datasets available, these usually do not provide annotations and thus cannot be used for benchmarking stop/trip classifiers easily. This manuscript introduces a GPS & accelerometer dataset, including accurate stop/trip annotations. It contains 122,808 GPS samples as one continuous trajectory, spanning over 126 days. The recorded time frame includes working days, vacations, travelling, everyday life and all regular modes of transportation. During recording, a detailed mobility diary was conducted to capture each dwelling period’s exact beginning and end. The position and diary data combined contain 78,900 labelled stops and 43,908 labelled trips. This serves as ground truth for stop/trip classification algorithms to test existing tools or develop new analysis methods. The introduced dataset is freely available under a CC-By Attribution 4.0 International license, the annotation tool under the BSD 3-Clause license.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.