PathletRL: Trajectory Pathlet Dictionary Construction using Reinforcement Learning

Gian Alix, Manos Papagelis

Abstract: 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.