TrajMamba: An Efficient and Semantic-rich Vehicle Trajectory Pre-training Model

TL;DR

TrajMamba is a novel pre-training model that efficiently captures semantic travel patterns from vehicle trajectories by jointly modeling GPS and road data, incorporating travel purposes, and reducing trajectory redundancy.

Contribution

It introduces a Traj-Mamba Encoder, a Travel Purpose-aware Pre-training, and a Knowledge Distillation scheme, advancing trajectory learning with semantic richness and computational efficiency.

Findings

Outperforms baselines in accuracy and efficiency

Effectively integrates travel purposes into embeddings

Reduces trajectory redundancy with learnable masking

Abstract

Vehicle GPS trajectories record how vehicles move over time, storing valuable travel semantics, including movement patterns and travel purposes. Learning travel semantics effectively and efficiently is crucial for real-world applications of trajectory data, which is hindered by two major challenges. First, travel purposes are tied to the functions of the roads and points-of-interest (POIs) involved in a trip. Such information is encoded in textual addresses and descriptions and introduces heavy computational burden to modeling. Second, real-world trajectories often contain redundant points, which harm both computational efficiency and trajectory embedding quality. To address these challenges, we propose TrajMamba, a novel approach for efficient and semantically rich vehicle trajectory learning. TrajMamba introduces a Traj-Mamba Encoder that captures movement patterns by jointly modeling both GPS and road perspectives of trajectories, enabling robust representations of continuous travel behaviors. It also incorporates a Travel Purpose-aware Pre-training procedure to integrate travel purposes into the learned embeddings without introducing extra overhead to embedding calculation. To reduce redundancy in trajectories, TrajMamba features a Knowledge Distillation Pre-training scheme to identify key trajectory points through a learnable mask generator and obtain effective compressed trajectory embeddings. Extensive experiments on two real-world datasets and three downstream tasks show that TrajMamba outperforms state-of-the-art baselines in both efficiency and accuracy.