Knowledge Discovery from Urban Trajectory Data: A Survey
Comprehending the mobility of entities in urban environments is crucial for developing and maintaining efficient and livable cities. This survey presents fundamental concepts, methods, and challenges related to knowledge discovery from urban trajectory data, including data acquisition, preprocessing, mining, and practical applications. Unlike previous studies, our survey offers a holistic and integrated view of these steps, ensuring that each stage is optimized to contribute to the overall performance of the mobility knowledge discovery process. Moreover, we identify operational and technical challenges, discussing potential future directions that can unlock new opportunities and advancements in this field.
Authors
- João B. Borges (ORCID: https://orcid.org/0000-0001-6497-1613)
- David F. Silva (ORCID: https://orcid.org/0000-0002-7681-7653)
- Lailla M. S. Bine (ORCID: https://orcid.org/0000-0002-4780-1783)
- Heitor S. Ramos (ORCID: https://orcid.org/0000-0003-4523-6466)
- Antônio A. F. Loureiro (ORCID: https://orcid.org/0000-0002-5250-1785)
- Aline Carneiro Viana (ORCID: https://orcid.org/0000-0002-1483-6269)
Institutions
- Institut national de recherche en sciences et technologies du numérique (FR)
- Institute of Computing Technology (CN)
- Faculdades Guarulhos (BR)
Publication Details
- Journal
- ACM Computing Surveys
- Published
- 2026-07-25
- DOI
- https://doi.org/10.1145/3833869
- Primary Topic
- Data Management and Algorithms
- Type
- article
- Field-Weighted Citation Impact
- 0.00