A model for enriching trajectories with semantic geographical information

By Luis Otávio Campos Alvares, Vania Bogorny, Bart Kuijpers, J. Macêdo, Bart Moelans, Alejandro A. Vaisman. ACM SIGSPATIAL International Workshop on Advances in Geographic Information Systems, 2007

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A review of moving object trajectory clustering algorithms (2016, methodology): “Alvares et al. (2007b) also used stops and moves to extract moving patterns.”
A Survey on Trajectory Data Management, Analytics, and Learning (2020, methodology): “ further considered the constraint of road network topology, geometry information, and historical information to re-calibrate noisy data points. For trajectories of sparse data points, Alvares et al. [14] proposed that the data be enriched with additional points and semantic information about the ty”
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].”