Challenges of spatio-temporal trajectory datasets

ABSTRACT

The rapid evolution of wireless location technologies has significantly increased the availability of mobility data, particularly spatio-temporal trajectory data, detailing object movements and locations over time. This data holds diverse applications such as predicting travel patterns, optimizing routes, analysing social interactions, and managing urban resources. However, leveraging trajectory datasets presents challenges, including data heterogeneity, volume, velocity requiring real-time processing, privacy concerns with sensitive information, and regulatory compliance. Traditional systematic reviews of literature may not fully capture industry challenges, as professionals often do not publish research. To address this gap, industry expert input is vital. Collaborating with academia, we propose using focus group discussions to explore these challenges comprehensively. Focus groups offer a qualitative platform for professionals and academics to identify practical issues and gaps in the literature. This combined approach will inform technological advancements in creating and utilizing spatio-temporal datasets. Additionally, integrating insights from focus groups with a literature review ensures a thorough analysis of data challenges. This methodology aims to provide a holistic understanding and foster innovation in managing and exploiting trajectory data effectively.

KEYWORDS:

• Data ethics
• focus group study
• privacy concerns
• spatio-temporal datasets

Acknowledgments

The authors also would like to express their gratitude to the organising committee, and the panellists of the 1st International Summer School on Data Science for Mobility, including Nikos Pelekis, Chiara Renso, Beatrice Rapisarda, Konstantinos Tserpes, Yannis Theodoridis, Thierry Chevallier, Bettina Berendt, Luca Pappalardo, and Mahmoud Sakr, for letting us to submit the findings of the focus group study for publication.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Author Contributions

The data collection for this research was conducted by Muhammad Arslan during the summer school, showcasing his contribution to the study. Furthermore, Arslan took the lead in writing the paper. Prior to submission, the paper underwent a thorough review by Christophe Cruz, ensuring its quality and adherence to scholarly standards.

Additional information

Funding

The authors thank the French government for the plan France Relance funding, and the ANR Traces project (https://traces-anr-fns.imag.fr/) PRCI Franco-Suisse (funded by ANR and FNS), 2022-2025.