Sophisticated location and tracking technologies have led to the generation of vast amounts of trajectory data. Of interest is constructing a small set of basic building blocks that can represent a wide range of trajectories, known as a trajectory pathlet dictionary. This dictionary can be useful in various tasks and applications, such as trajectory compression, travel time estimation, route planning, and navigation services. Existing methods for constructing a pathlet dictionary use a top-down approach, which generates a large set of candidate pathlets and selects the most popular ones to form the dictionary. However, this approach is memory-intensive and leads to redundant storage due to the assumption that pathlets can overlap. To address these limitations, we propose a bottom-up approach for constructing a pathlet dictionary that significantly reduces memory storage needs of baseline methods by multiple orders of magnitude (by up to ~24K× better). The key idea is to initialize unit-length pathlets and iteratively merge them, while maximizing utility. The utility is defined using newly introduced metrics of trajectory loss and representability. A deep reinforcement learning method is proposed, PathletRL, that uses Deep Q Networks (Dqn) to approximate the utility function. Experiments show that our method outperforms the current state-of-the-art, both on synthetic and real-world data. Our method can reduce the size of the constructed dictionary by up to 65.8% compared to other methods. It is also shown that only half of the pathlets in the dictionary is needed to reconstruct 85% of the original trajectory data.

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.