Unifying Environment Perception and Route Choice Modeling for Trajectory Representation Learning

• URL: http://arxiv.org/abs/2510.14819v1
• Date: Thu, 16 Oct 2025 15:55:28 GMT
• Title: Unifying Environment Perception and Route Choice Modeling for Trajectory Representation Learning
• Authors: Ji Cao, Yu Wang, Tongya Zheng, Zujie Ren, Canghong Jin, Gang Chen, Mingli Song,
• Abstract summary: Tray Learning (TRL) aims to encode raw trajectories into low-dimensional vectors, which can be leveraged in various downstream tasks, including travel time estimation, location prediction, and trajectory similarity analysis.<n>We propose a framework that unifies comprehensive environment textbfPertemporal explicit textRoute choice modeling for effective textbfPRTrajectory representation learning, dubbed textbfPRTraj.
• Score: 47.00223863430964
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 \textbf{P}erception and explicit \textbf{R}oute choice modeling for effective \textbf{Traj}ectory representation learning, dubbed \textbf{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.

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