A clustering-based approach for discovering interesting places in trajectories
By Andrey Tietbohl Palma, Vania Bogorny, Bart Kuijpers, Luis Otávio Campos Alvares. ACM Symposium on Applied Computing, 2008
500 citations recorded by the source corpus.
Subfields: Geography, Planning and Development, Transportation, Signal Processing
View Paper: https://www.semanticscholar.org/paper/0a6c4a7271bac2b564368124cb45f45ef688d231
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In Geography, Planning and Development
In Transportation
In Signal Processing
Citations in the corpus
- A Survey on Trajectory Data Management, Analytics, and Learning (2020, methodology): “n categories of interests discovery using trajectory data for tourism are: 1) point of interest (POI) [71]; 2) region of interest (ROI) [90, 158]; and 3) interactive discovery [25, 171]. Palma et al. [131] discovered interesting locations from trajectories using a spatiotemporal clustering method, b”
- A review of moving object trajectory clustering algorithms (2016, methodology): “Palma et al. (2008) discovered interesting places from trajectories with density based clustering algorithms.”
- Identifying intercity freight trip ends of heavy trucks from GPS data (2021, methodology): “For selecting valid trip ends from among identified truck stops, three main methods have been applied in existing studies: (1) the geographic information method [23-26], (2) the spatial clustering method [24, 27-29], and (3) the dwell time method [12, 14, 30, 31].”