Unifying Environment Perception and Route Choice Modeling for Trajectory Representation Learning

2025-10-16

AI Summary

Existing trajectory representation learning methods treat trajectories as isolated spatiotemporal sequences, neglecting both external environmental influences and individual path decision-making mechanisms—leading to semantically impoverished and poorly interpretable representations. To address this, we propose the first unified framework jointly modeling environment awareness and explicit path selection. Our approach introduces a multi-granularity environmental semantic encoding module that hierarchically extracts road-network context from POI distributions, and a path selection encoder based on continuous decision sequences, which models trajectory segment transitions as dynamic, sequential decisions. Evaluated on three real-world datasets across five downstream tasks, our method consistently outperforms state-of-the-art baselines, achieving particularly notable gains in low-data regimes—demonstrating superior generalization capability and data efficiency.

Technology Category

• Search and Optimization: Learning to Search
• Planning, Routing, and Scheduling: Planning with Language Models
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

Trajectory Representation Learning (TRL) aims to encode raw trajectories into low-dimensional vectors, which can then be leveraged in various downstream tasks, including travel time estimation, location prediction, and trajectory similarity analysis. However, existing TRL methods suffer from a key oversight: treating trajectories as isolated spatio-temporal sequences, without considering the external environment and internal route choice behavior that govern their formation. To bridge this gap, we propose a novel framework that unifies comprehensive environment extbf{P}erception and explicit extbf{R}oute choice modeling for effective extbf{Traj}ectory representation learning, dubbed extbf{PRTraj}. Specifically, PRTraj first introduces an Environment Perception Module to enhance the road network by capturing multi-granularity environmental semantics from surrounding POI distributions. Building on this environment-aware backbone, a Route Choice Encoder then captures the route choice behavior inherent in each trajectory by modeling its constituent road segment transitions as a sequence of decisions. These route-choice-aware representations are finally aggregated to form the global trajectory embedding. Extensive experiments on 3 real-world datasets across 5 downstream tasks validate the effectiveness and generalizability of PRTraj. Moreover, PRTraj demonstrates strong data efficiency, maintaining robust performance under few-shot scenarios. Our code is available at: https://anonymous.4open.science/r/PRTraj.

Problem

• Existing trajectory learning methods ignore environmental influences on movement patterns
• Current approaches treat trajectories as isolated sequences without route choice behavior
• Lack of unified modeling for external environment and internal decision-making in trajectories

Innovation

• Unifies environment perception and route choice modeling
• Enhances road network with multi-granularity environmental semantics
• Models road transitions as sequential route choice decisions