Sentence examples for trajectory datasets from inspiring English sources

These approaches are validated on biological particle trajectory datasets from a wide range of experimental systems, demonstrating their broad applicability to research in cell biology.

In this paper, we propose solutions tackling the combined, map matched trajectory compression problem, the efficiency of which is demonstrated through an extensive experimental evaluation on offline and online trajectory data using synthetic and real trajectory datasets.

The KPC problem is important to domains such as transportation services interested in finding primary corridors for public transportation or greener travel (e.g., bicycling) by leveraging emerging GPS trajectory datasets.

In comparison with the existing state of the arts, our proposed approach not only achieves the advantage that the number of clusters can be automatically determined, but also the superior clustering performances on a range of temporal datasets, including synthetic dataset, time series benchmark, and real-world motion trajectory datasets.

Due to the stochastic nature of insect motion, researchers often need to analyze large trajectory datasets that capture their movement under diverse conditions to accurately interpret their behavior.

To the best of our knowledge only [20] uses data-driven spatial generalization to achieve anonymity for trajectory datasets; the only work applying spatial generalization is [31], but it uses a fixed grid hierarchy to discretize the spatial dimension.

The interaction rule is formulated as a multinomial logit model, which is calibrated by using a microscopic traffic trajectory dataset obtained from video footage.

Majorly, GPS data points are from GPS trajectory dataset collected in (Microsoft Research Asia) Geolife project.

With the particle filter Gaussian process dynamical model (PFGPDM), a high-dimensional target trajectory dataset of the observation space is projected to a low-dimensional latent space in a nonlinear probabilistic manner, which will then be used to classify object trajectories, predict the next motion state, and provide Gaussian process dynamical samples for the particle filter.

In contrast, the novelty of our approach lies in finding a suitable tessellation of the geographical area into sub-areas dependent on the input trajectory dataset and in taking into consideration from the start also the analytical properties to be preserved in the data for guaranteeing good performance in terms of clustering analysis.

Table 3 describes the sizes of the contents of the D-trajectory dataset derived from this GPS dataset.