[2511.16105] Data-Efficient and Robust Trajectory Generation through Pathlet Dictionary Learning

Computer Science > Machine Learning arXiv:2511.16105 (cs) [Submitted on 20 Nov 2025 (v1), last revised 24 Mar 2026 (this version, v2)] Title:Data-Efficient and Robust Trajectory Generation through Pathlet Dictionary Learning Authors:Yuanbo Tang, Yan Tang, Zixuan Zhang, Zihui Zhao, Yang Li View a PDF of the paper titled Data-Efficient and Robust Trajectory Generation through Pathlet Dictionary Learning, by Yuanbo Tang and 4 other authors View PDF HTML (experimental) Abstract:Trajectory generation has recently drawn growing interest in privacy-preserving urban mobility studies and location-based service applications. Although many studies have used deep learning or generative AI methods to model trajectories and have achieved promising results, the robustness and interpretability of such models are largely unexplored. This limits the application of trajectory generation algorithms on noisy real-world data and their trustworthiness in downstream tasks. To address this issue, we exploit the regular structure in urban trajectories and propose a deep generative model based on the pathlet representation, which encode trajectories with binary vectors associated with a learned dictionary of trajectory segments. Specifically, we introduce a probabilistic graphical model to describe the trajectory generation process, which includes a Variational Autoencoder (VAE) component and a linear decoder component. During training, the model can simultaneously learn the latent embedding of path...