# A Survey of Open Source User Activity Traces with Applications to User Mobility Characterization and Modeling

arXiv: https://arxiv.org/abs/2110.06382 · HTML: https://mlsanity.com/paper/2110.06382/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 142 references that are in the map.
- First posted on arXiv 12 Oct 2021.

## Builds on (most cited first, 7 of 7)

- [Understanding Human Mobility from Twitter](https://mlsanity.com/paper/1412.2154/) (2014)
- [Human Mobility in Opportunistic Networks: Characteristics, Models and Prediction Methods](https://mlsanity.com/paper/2008.05611/) (2020)
- [User Mobility Evaluation for 5G Small Cell Networks Based on Individual Mobility Model](https://mlsanity.com/paper/1512.03149/) (2015)
- [Mobility Changes in Response to COVID-19](https://mlsanity.com/paper/2003.14228/) (2020)
- [A survey on Human Mobility and its applications](https://mlsanity.com/paper/1307.0814/) (2013)
- [Cost-optimal caching for D2D networks with user mobility: Modeling, analysis, and computational approaches](https://mlsanity.com/paper/1705.10728/) (2017)
- [Identifying latent shared mobility preference segments in low-income communities: ride-hailing, fixed-route bus, and mobility-on-demand transit](https://mlsanity.com/paper/2107.04412/) (2021)

## Built on by (most cited first, 1 of 1)

- [Berlin V2X: A Machine Learning Dataset from Multiple Vehicles and Radio Access Technologies](https://mlsanity.com/paper/2212.10343/) (2022)

## Abstract (arXiv)

The current state-of-the-art in user mobility research has extensively relied on open-source mobility traces captured from pedestrian and vehicular activity through a variety of communication technologies as users engage in a wide-range of applications, including connected healthcare, localization, social media, e-commerce, etc. Most of these traces are feature-rich and diverse, not only in the information they provide, but also in how they can be used and leveraged. This diversity poses two main challenges for researchers and practitioners who wish to make use of available mobility datasets. First, it is quite difficult to get a bird's eye view of the available traces without spending considerable time looking them up. Second, once they have found the traces, they still need to figure out whether the traces are adequate to their needs. The purpose of this survey is three-fold. It proposes a taxonomy to classify open-source mobility traces including their mobility mode, data source and collection technology. It then uses the proposed taxonomy to classify existing open-source mobility traces and finally, highlights three case studies using popular publicly available datasets to showcase how our taxonomy can tease out feature sets in traces to help determine their applicability to specific use-cases.