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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2021
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2110.06382 |
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| _version_ | 1866914912315375616 |
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| author | King, Sinjoni Mukhopadhyay Nawab, Faisal Obraczka, Katia |
| author_facet | King, Sinjoni Mukhopadhyay Nawab, Faisal Obraczka, Katia |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2110_06382 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | A Survey of Open Source User Activity Traces with Applications to User Mobility Characterization and Modeling King, Sinjoni Mukhopadhyay Nawab, Faisal Obraczka, Katia Computer Vision and Pattern Recognition Social and Information Networks 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. |
| title | A Survey of Open Source User Activity Traces with Applications to User Mobility Characterization and Modeling |
| topic | Computer Vision and Pattern Recognition Social and Information Networks |
| url | https://arxiv.org/abs/2110.06382 |