Learning Trajectories as Words: A Probabilistic Generative Model for Destination Prediction

Destination prediction is crucial for many location based services such as sightseeing places recommendation and targeted advertisements push. Most existing techniques utilize the historical trajectories to predict destinations, but they fail to well describe the spatio-temporal characteristics of trajectories and suffer the trajectory sparsity problem, i.e., the available historical trajectories are hard to cover all probable trajectories. The temporal sensitivity of historical trajectories highlights the sparsity problem even more. In this paper, we address this problem by building a probabilistic generative model to capture the spatio-temporal features of trajectories. We develop an extended Latent Dirichlet Allocation (LDA) model to characterize the generative mechanism of track points in each trajectory. In this model, trajectory, track point of trajectory and destination are regarded as document, word and response respectively. To address the trajectory sparsity problem, each trajectory is expressed by the distribution of trajectory patterns which are the topics discovered from historical trajectories. Then, the most likely destination is predicted through the trajectory patterns. The experiments performed on a real-world taxi trajectory dataset from Guangzhou confirm the advantage of the probabilistic generative model in destination prediction, achieving remarkable accuracy and strong interpretability.