L2MM: Learning to Map Matching with Deep Models for Low-Quality GPS Trajectory Data

Abstract

Map matching is a fundamental research topic with the objective of aligning GPS trajectories to paths on the road network. However, existing models fail to achieve satisfactory performance for low-quality (i.e., noisy, low-frequency, and non-uniform) trajectory data. To this end, we propose a general and robust deep learning-based model, L2MM , to tackle these issues at all. First, high-quality representations of low-quality trajectories are learned by two representation enhancement methods, i.e., enhancement with high-frequency trajectories and enhancement with the data distribution . The former employs high-frequency trajectories to enhance the expressive capability of representations, while the latter regularizes the representation distribution over the latent space to improve the generalization ability of representations. Secondly, to embrace more heuristic clues, typical mobility patterns are recognized in the latent space and further incorporated into the map matching task. Finally, based on the available representations and patterns, a mapping from trajectories to corresponding paths is constructed through a joint optimization method. Extensive experiments are conducted based on a range of datasets, which demonstrate the superiority of L2MM and validate the significance of high-quality representations as well as mobility patterns.