Trajectory prediction estimates an entity's future path from its historical movements, enabling applications in autonomous navigation, robotics, and mobility analytics. This highlight paper presents TrajLearn, a deep generative framework that models higher-order mobility flows using a hexagonal spatial representation. TrajLearn combines a Transformer-based architecture with a constrained beam search to explore multiple plausible paths while preserving spatial continuity. Experiments on real-world datasets show up to 40% accuracy improvement over state-of-the-art models. We also propose a hierarchical mixed-resolution mapping algorithm that adaptively applies finer granularity to high-activity regions while using coarser resolution elsewhere, optimizing storage and computation. TrajLearn provides a scalable and reproducible foundation for accurate trajectory prediction in dynamic spatial environments.

Research on trajectory data mining relies on appropriate datasets, including Gps-based geolocations, check-in data to points of interest (Pois), and synthetic datasets. Even though some data are accessible, the majority of mobility datasets are typically discovered through ad-hoc searches and lack comprehensive documentation of their generation process or source to reproduce curated or customized versions of them. At the same time, there has been a growing interest in a new type of mobility data, describing trajectories as sequences of higher-order geometric elements like hexagons that offer several benefits: (i) reduced sparsity and analysis at different granularity levels, (ii) compatibility with popular machine learning architectures, (iii) improved generalization and reduced overfitting, and (iv) efficient visualization. To this end, we present Point2Hex, a method and tool for generating higher-order mobility flow datasets from raw trajectory data. We used Point2Hex to create higherorder versions of seven popular mobility datasets typically employed in trajectory-related technical problems and downstream tasks, such as trajectory prediction, classification, clustering, imputation, and anomaly detection, to name a few. To promote reuse and encourage reproducibility, we provide the source code and documentation of Point2Hex, as well as the generated higher-order mobility flow datasets in publicly accessible repositories.

The relationship between human mobility and the spread of an infectious disease has been well documented. At the same time, availability of mobility data is growing due to advancements in digital contact tracing mobile applications and GPS-enabled devices. Motivated by these observations, we have designed and developed STRIPE (Safe Trips during Epidemics), a mobility-based recommendation system that can provide safer trip recommendations to individuals. The recommendation model considers the risk of infection of alternative trips between an origin and destination. It also considers the risk of infection of specific points of interests (POIs) that occur at the microscale. In this paper, we present a high-level architecture of the system, its main features and system use cases. The broader impact of our research is that by helping individuals making informed decisions, we promote more responsible behaviors in the community as a whole that could effectively alleviate the impact of the epidemic.