Mobility data from smartphones, connected cars, and GPS devices are widely used for tasks such as transportation mode classification and suspicious movement detection. Time series research, a closely related field, focuses more on classification methods. Yet, Mobility Data analysis faces unique challenges like geographic transferability and limited public data due to privacy issues. My PhD work focuses on developing reusable, interpretable MD representations. I created Trajectory Interval Forest and later Geolet, a shapelet-based transformation to improve MD classification across geographic regions. Ongoing research explores improving geographic transferability and event-based trajectory clustering.

Maritime vessel monitoring is vital for ensuring navigational safety, protecting marine ecosystems, and enforcing regulations. We present a framework to support expert analysis and monitoring of vessel activities using Automatic Identification System (AIS) trajectory data. By extracting rotation- and scale-invariant shape signatures through a relative Hough transform, our system clusters and organizes subtrajectory patterns, enabling intuitive visual exploration. Experts interactively associate representative shapes with maritime events such as trawling or port visits, creating an event-to-shape map used for real-time detection in new trajectories. The framework's design allows efficient handling of geometric and motion dynamics, while facilitating the creation of labeled datasets to improve automated analysis. We demonstrate its effectiveness on a dataset of fishing vessels, highlighting its potential for scalable, human-in-the-loop maritime surveillance.

Abstract Instance-based models offer natural interpretability by making decisions based on concrete examples. However, their transparency is often hindered by the use of complex similarity measures, which are difficult to interpret, especially in high-dimensional datasets. To address this issue, this paper presents a meta-learning framework that enhances the interpretability of instance-based models by replacing traditional, complex pairwise distance functions with interpretable pairwise distance trees. These trees are designed to prioritize simplicity and transparency while preserving the model’s effectiveness. By offering a clear decision-making process, the framework makes the instance selection more understandable. Also, the framework mitigates the computational burden of instance-based models, which typically require calculating all pairwise distances. Leveraging the generalization capabilities of pairwise distance trees and employing sampling strategies to select representative subsets, the method significantly reduces computational complexity. Our experiments demonstrate that the proposed approach improves computational efficiency with only a modest trade-off in accuracy while substantially enhancing the interpretability of the learned distance measure.

Although Mobility Data Analysis (MDA) has been explored for a long time, it still lags behind advancements in other fields. A common issue in MDA is the lack of methods’ standardization and reusability. On the other hand, for instance, in time series analysis, the existing methods are typically general-purpose, and it is possible to apply them across diverse datasets and applications without extensive customization. Still, in MDA, most contributions are ad-hoc and designed to address specific research questions, which limits their generalizability and reusability. Recently, some researchers explored the application of shapelet transform to trajectory data, i.e., extracting discriminatory sub-trajectories from training data to be used as classification features. Unlike current MDA methods, this line of research eliminates the need for feature engineering, greatly improving its ability to generalize. While shapelets on mobility data have shown state-of-the-art performance on public classification datasets, it is still not clear why they work. Are these subtrajectories merely proxies for geographic location, or do they also capture motion dynamics? We empirically show that shapelet-based approaches are a viable alternative to classical methods and flexible enough to solve MDA tasks related solely to trajectory shape, solely to movement dynamics, and those related to both. Additionally, we investigate the problem of Geographic Transferability, showing that such approaches offer a promising starting point for tackling this challenge.

GPS devices generate spatio-temporal trajectories for different types of moving objects. Scientists can exploit them to analyze migration patterns, manage city traffic, monitor the spread of diseases, etc. Many current state-of-the-art models that use this data type require a not negligible running time to be trained. To overcome this issue, we propose the Trajectory Interval Forest (TIF) classifier, an efficient model with high throughput. TIF works by calculating various mobility-related statistics over a set of randomly selected intervals. These statistics are used to create a tabular representation of the data, which can be used as input for any classical classifier. Our results show that TIF is comparable to or better than state-of-art in terms of accuracy and is orders of magnitude faster.

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