Open source user mobility and activity datasets: Taxonomy and applications

Abstract

The study of user mobility and activity (uMA) has numerous applications, including network resource planning, connected healthcare, localization, social media, and e-commerce. Current research in uMA heavily relies on open-source traces captured from pedestrian, vehicular, and application-based activities. These traces are rich in features and diverse, not only in the information they provide but also in their potential applications. However, this diversity presents two main challenges for researchers and practitioners who aim to utilize uMA datasets, classify existing datasets, or create new ones. Firstly, there is no readily available comprehensive classification of existing open-source uMA traces, making it typically labor-intensive and time-consuming to determine whether the identified datasets are suitable. Secondly, it is challenging to identify the key features and their specific use cases without conducting a detailed analysis of the traces.

This manuscript aims to address these challenges in three ways. First, we propose a taxonomy for classifying open-source mobility traces based on mobility mode, data source, collection technology, and application type. This taxonomy can be used to create tags for both existing and new datasets, making it easier to find problem-specific datasets compared to current search methods. Second, we demonstrate how existing datasets can be classified according to this taxonomy, providing examples of popular open-source uMA traces, along with information about their publishing source, licensing, and anonymization strategy. We also discuss how this taxonomy can guide the collection of new uMA datasets. Finally, we present three case studies using popular publicly available uMA datasets to illustrate how our taxonomy can be used to identify feature sets in the traces, helping to determine their applicability to specific use cases in networking, health, lifestyle, and location-based services.