Trajectory Representation Learning

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Overview

It studies how to encode paths—such as vehicle movement, robot actions, and agent interaction traces—so useful structure survives compression and supports downstream tasks. Common approaches include self-supervised contrastive and masked-sequence pretraining, graph and hierarchical encoders, spatial tokenization, and continuous latent or generative models for waypoints, motion phases, and trajectory-level effects. These representations are tested on similarity search, classification, travel-time prediction, driving forecasts, and robot manipulation or navigation across tasks and embodiments. A central tension is balancing compact, reusable embeddings with enough temporal, geometric, and contextual detail for reliable long-horizon prediction and control in new scenes or at different execution rates.

Latest papers

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48 papers12 papers in the last four weeks, level with the four weeks before. 0.1% of all new papers.

Jul 3, 2026

Transition Information Density: Morphological Trajectories, Synesthetic Perception, and Structured Interpolation in Neural Training (or: The Synesthetic AI)

Standard machine learning training presents data as discrete endpoint pairs, omitting the structure of the space between them. This paper introduces Transition Information Density (TID) -- the information content recoverable from structured intermediate states between categorically distinct training endpoints -- and Positional Identity, the defined location of an intermediate state on the A-to-B continuum. Both constructs are grounded in three empirical contexts: grapheme-color synesthesia, the Synesthesia Grid (a boundary-contour morphing algorithm instantiating TID in visual morphological space), and a four-condition training experiment across four representational mediums. Probes trained on structured interpolation at defined Positional Identities (C3) exhibit substantially lower intrinsic dimensionality than volume-matched controls (C2) in Phonetic/Linguistic (C3: 3.33 vs. C2: 10.81) and Semantic Description (C3: 4.59 vs. C2: 8.67) mediums. Visual and cross-modal mediums do not show this effect, establishing a modality boundary condition. A fixed-N=50 comparison confirms that Positional Identity structure, not sample count, drives the effect. Resolution N scales monotonically with representational richness. Pooled TwoNN analysis reveals globally collapsed representations in visual space (0.075) and globally consistent representations in phonetic space (0.977). The paper contributes a formal definition of TID and Positional Identity, a nine-metric shape characterization framework, and a four-condition experimental design isolating trajectory structure, data volume, and Positional Identity as distinct factors.

Sam Mao

New York University | Interactive Media Arts

Neural Representation GeometryTrajectory Representation Learning
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Jun 19, 2026

BIT-Nav: Brain-Inspired Trajectory Memory for Embodied Navigation

Vision-Language Models (VLMs) for embodied navigation rely on selecting a fixed number of frames from a growing trajectory history. As episodes extend, this selection grows increasingly sparse, yet prior work shows no accuracy gain when scaling from 8 to 64 frames, suggesting the bottleneck is not frame quantity but the representation itself. Sparse frame selection cannot capture the structured behavioral signal that long-horizon reasoning requires: turning patterns, cumulative displacement, and path topology. We introduce BIT-Nav (Brain-Inspired Trajectory Memory for Navigation), a framework that augments frozen VLM navigation pipelines with a compact learned trajectory memory. Motivated by hippocampal path integration, where spatial experience is compressed into structured episodic traces rather than stored as raw sensory replay, BIT-Nav trains a Bi-GRU encoder over action and relative pose sequences via a multi-positive InfoNCE contrastive objective on trajectory prefixes sharing the same behavioral intent. The resulting embedding is projected into the VLM token space via a lightweight MLP and injected as a single memory token at each decision step, conditioning the model on structured motion history at constant token cost regardless of episode length

Rithvik Jonna, Aakash Gurram, Man Namgung +2

Department of Electrical and Computer Engineering Johns Hopkins University · Laboratory for Computational Sensing and Robotics Johns Hopkins University · DEVCOM Army Research Laboratory

Vision-Language ModelsEmbodied NavigationVision-Language NavigationTrajectory Representation Learning
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May 29, 2026

The Terminal Representation in Reinforcement Learning

Representation learning is a powerful tool for spatio-temporal abstraction within reinforcement learning (RL). Two well established approaches are through the successor representation (SR) and the default representation (DR). The SR encodes states by the future trajectories they induce, capturing information flow decoupled from reward. The DR builds on this by weighting trajectories with reward, integrating credit-assignment structure into the representation. Eigenvectors of both representations have been used to support a range of downstream tasks -- including option discovery, reward shaping, transfer learning, and exploration. We introduce a structurally distinct formulation: the terminal representation (TR). The TR encodes reward-weighted trajectories similarly to the DR, but can be learned as a lower-dimensionality object, and can be used directly for the mentioned applications without eigenvector computations. Eigendecomposition also imposes the assumption of symmetric transition dynamics, which the TR can bypass. In this work we develop the theoretical foundations of the TR: its derivation, convergence of two learning algorithms, its use for zero-shot compositionality, and equivalences between alternative reward formulations. We further show the TR is embedded in the top DR eigenvector, allowing it to capture the same underlying knowledge without eigendecomposition. Additionally, we provide empirical evidence of the TR as a viable alternative to existing representations in subsidiary applications, while requiring less computational overhead to learn, store, and use.

Amir Esterhuysen, Anders Jonsson

Dept. Information and Communication Technologies Universitat Pompeu Fabra Barcelona, Spain

Representation Learning for RLTrajectory Representation LearningState Representation LearningSuccessor Representations
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May 19, 2026

TrajTok: Adaptive Spatial Tokenization for Trajectory Representation Learning

Learning generalizable trajectory representations from raw GPS traces remains difficult because the data is continuous, noisy, and irregularly sampled. Spatial tokenization is also challenging: fine grids yield sparse cells with weak embeddings, while coarse grids merge heterogeneous movement patterns into the same token. We present TrajTok, a trajectory encoder with a simple pretraining recipe for transferable trajectory embeddings. TrajTok first learns a multi-resolution hexagonal cell partition from the spatial distribution of GPS points, converting noisy GPS sequences into discrete cell tokens. To capture both geometry and kinematics, it uses a factorized transformer encoder with early per-modality self-attention blocks, cross-attention fusion layers, and spatiotemporal rotary position embeddings, ST-RoPE, to encode where and when each token occurs. TrajTok is pretrained with masked-token modeling that recovers both geometric structure and kinematic patterns from partial trajectory observations. On the Porto dataset, a frozen TrajTok encoder with lightweight task adapters achieves strong performance across trajectory similarity search, classification, estimated time of arrival, and full travel-time regression, outperforming multiple task-specific methods. The same frozen encoder supports both geometry-dominated and kinematics-dominated tasks, suggesting that TrajTok learns transferable trajectory structure rather than task-specific shortcuts. These results indicate that learned multi-resolution spatial tokenization combined with masked-token pretraining is a promising direction for general-purpose trajectory foundation models.

Zhen Xiong, Shang-Ling Hsu, Cyrus Shahabi

University of Southern California

Geospatial Representation LearningTrajectory Representation LearningSpatiotemporal Transformers
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May 14, 2026

Matrix-Space Reinforcement Learning for Reusing Local Transition Geometry

Compositional generalization in sequential decision-making requires identifying which parts of prior rollouts remain useful for new tasks. Existing methods reuse skills or predictive models, but often overlook rich local transition geometry and dynamics. We propose Matrix-Space Reinforcement Learning (MSRL), a geometric abstraction that represents trajectory segments through positive semidefinite matrix descriptors aggregating first- and second-order statistics of lifted one-step transitions. These descriptors expose shared hidden structure, support algebraic composition in an abstract matrix space, and reveal opportunities for transfer. We prove that the descriptor is well defined up to coordinate gauge, complete for the induced low-order additive signal class, additive under valid segment composition, and minimally sufficient among admissible additive descriptors. We further show that conditioning value functions on the trajectory-segment matrix yields a first-order smooth approximation of action values, enabling source-learned matrix-to-value mappings to bootstrap learning in new tasks. MSRL is plug-in compatible with standard model-free and model-based methods, while obstruction filtering rejects implausible compositions. Empirically, MSRL achieves the best average finite-budget target AUC of 0.73, outperforming MSRL from scratch (0.65), TD-MPC-PT+FT (0.63), and TD-MPC (0.57).

Zuyuan Zhang, Carlee Joe-Wong, Tian Lan

The George Washington University · Carnegie Mellon University

Reinforcement LearningTrajectory Representation Learning
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May 7, 2026

TRAJGANR: Trajectory-Centric Urban Multimodal Learning via Geospatially Aligned Neural Representations

Multimodal self-supervised learning (MSSL) has emerged as a key paradigm for pretraining geospatial foundation models. However, existing geospatial MSSL methods are mainly designed for static pairs of modalities, such as satellite imagery, street-view imagery, and text, where learning is driven by aligning observations from the same or nearby locations. This assumption breaks down for human mobility trajectories, which represent continuous movement along paths rather than discrete observations at individual locations. Although trajectories are important for urban understanding through their ability to capture human activity across roads, neighborhoods, and places over time, they remain largely underexplored in current geospatial MSSL frameworks. We present TrajGANR, a novel trajectory-centric geospatial MSSL framework that aligns continuous movement patterns with static, location-based observations. TrajGANR learns a continuous neural representation of trajectories at arbitrary points along each path, which enables fine-grained alignment with nearby street-view images, even when they are not co-located with any trajectory waypoints. We leverage this capability to introduce an MSSL objective that jointly aligns three modalities: trajectories, street-view images, and their geographic locations. We evaluate TrajGANR on four urban mobility and road understanding tasks. Across these tasks, TrajGANR consistently outperforms existing geospatial MSSL frameworks and a trajectory-specific foundation model. Ablation studies further demonstrate that our proposed MSSL objective and the multimodal learning framework are the primary drivers of these improvements, highlighting the importance of fine-grained geospatial alignment over coarser aggregation, as well as geospatial multimodal learning.

Maria Despoina Siampou, Gengchen Mai, Ni Lao +4

Google Research, Mountain View, CA · Dept. of Computer Science, University of Southern California, Los Angeles, CA · SEAI Lab, Dept. of Geography and the Environment, The University of Texas at Austin, Austin, TX +1

Geospatial Foundation ModelsCross-Modal AlignmentMultimodal PretrainingTrajectory Representation Learning
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Apr 14, 2026

Improving Molecular Force Fields with Minimal Temporal Information

Accurate prediction of energy and forces for 3D molecular systems is one of fundamental challenges at the core of AI for Science applications. Many powerful and data-efficient neural networks predict molecular energies and forces from single atomic configurations. However, one crucial aspect of the data generation process is rarely considered while learning these models i.e. Molecular Dynamics (MD) simulation. MD simulations generate time-ordered trajectories of atomic positions that fluctuate in energy and explore regions of the potential energy surface (e.g., under standard NVE/NVT ensembles), rather than being constructed to steadily lower the potential energy toward a minimum as in geometry relaxations. This work explores a novel way to leverage MD data, when available, to improve the performance of such predictors. We introduce a novel training strategy called FRAMES, that use an auxiliary loss function for exploiting the temporal relationships within MD trajectories. Counter-intuitively, on two atomistic benchmarks and a synthetic system we observe that minimal temporal information, captured by pairs of just two consecutive frames, is often sufficient to obtain the best performance, while adding longer trajectory sequences can introduce redundancy and degrade performance. On the widely used MD17 and ISO17 benchmarks, FRAMES significantly outperforms its Equiformer baseline, achieving highly competitive results in both energy and force accuracy. Our work not only presents a novel training strategy which improves the accuracy of the model, but also provides evidence that for distilling physical priors of atomic systems, more temporal data is not always better.

Ali Mollahosseini, Mohammed Haroon Dupty, Wee Sun Lee

School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran · School of Computing, National University of Singapore, Singapore

Machine Learning Interatomic PotentialsTrajectory Representation Learning
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Mar 25, 2026

Hierarchical Spatial-Temporal Graph-Enhanced Model for Map-Matching

The integration of GNSS data into portable devices has led to the generation of vast amounts of trajectory data, which is crucial for applications such as map-matching. To tackle the limitations of rule-based methods, recent works in deep learning for trajectory-related tasks occur. However, existing models remain challenging due to issues such as the difficulty of large-scale data labeling, ineffective modeling of spatial-temporal relationships, and discrepancies between training and test data distributions. To tackle these challenges, we propose HSTGMatch, a novel model designed to enhance map-matching performance. Our approach involves a two-stage process: hierarchical self-supervised learning and spatial-temporal supervised learning. We introduce a hierarchical trajectory representation, leveraging both grid cells and geographic tuples to capture moving patterns effectively. The model constructs an Adaptive Trajectory Adjacency Graph to dynamically capture spatial relationships, optimizing GATs for improved efficiency. Furthermore, we incorporate a Spatial-Temporal Factor to extract relevant features and employ a decay coefficient to address variations in trajectory length. Our extensive experiments demonstrate the model's superior performance, module effectiveness, and robustness, providing a promising solution for overcoming the existing limitations in map-matching applications. The source code of HSTGMatch is publicly available on GitHub at https://github.com/Nerooo-g/HSTGMatch.

Anjun Gao, Zhenglin Wan, Pingfu Chao +1

School of Computer Science and Technology, Soochow University, Suzhou, China · Metasequoia Intelligence, Shenzhen, China · Department of Astronomy, The Ohio State University, Columbus, OH 43202 USA

Spatiotemporal GNNsTrajectory Representation Learning
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Mar 16, 2026

What Matters for Scalable and Robust Learning in End-to-End Driving Planners?

End-to-end autonomous driving has gained significant attention for its potential to learn robust behavior in interactive scenarios and scale with data. Popular architectures often build on separate modules for perception and planning connected through latent representations, such as bird's eye view feature grids, to maintain end-to-end differentiability. This paradigm emerged mostly on open-loop datasets, with evaluation focusing not only on driving performance, but also intermediate perception tasks. Unfortunately, architectural advances that excel in open-loop often fail to translate to scalable learning of robust closed-loop driving. In this paper, we systematically re-examine the impact of common architectural patterns on closed-loop performance: (1) high-resolution perceptual representations, (2) disentangled trajectory representations, and (3) generative planning. Crucially, our analysis evaluates the combined impact of these patterns, revealing both unexpected limitations as well as underexplored synergies. Building on these insights, we introduce BevAD, a novel lightweight and highly scalable end-to-end driving architecture. BevAD achieves 72.7% success rate on the Bench2Drive benchmark and demonstrates strong data-scaling behavior using pure imitation learning. Our code and models are publicly available here: https://dmholtz.github.io/bevad/

David Holtz, Niklas Hanselmann, Simon Doll +2

Mercedes-Benz AG · Max-Planck-Institute for Informatics, SIC

End-to-End Autonomous DrivingAutonomous Driving PlanningTrajectory Representation Learning
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Mar 12, 2026

Temporal Straightening for Latent Planning

Learning good representations is essential for latent planning with world models. While pretrained visual encoders produce strong semantic visual features, they are not tailored to planning and contain information irrelevant -- or even detrimental -- to planning. Inspired by the perceptual straightening hypothesis in human visual processing, we introduce temporal straightening to improve representation learning for latent planning. Using a curvature regularizer that encourages locally straightened latent trajectories, we jointly learn an encoder and a predictor of a Joint-Embedding Predictive Architecture (JEPA) world model. We show that reducing curvature this way makes the Euclidean distance in latent space a better proxy for the geodesic distance and improves the conditioning of the planning objective. We demonstrate empirically that temporal straightening makes gradient-based planning more stable and yields significantly higher success rates across a suite of goal-reaching tasks. Our code is available at https://agenticlearning.ai/temporal-straightening.

Ying Wang, Oumayma Bounou, Gaoyue Zhou +4

New York University · Brown University · University of Toronto

Latent-Space PlanningWorld Model-Based PlanningTrajectory Representation LearningJoint-Embedding Predictive Architecture
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Mar 9, 2026

3PoinTr: From Human Videos to Robot Policies with 3D Point-Track Plans

Human videos could reduce the reliance on expensive robot demonstrations, but extracting task-relevant features remains challenging. We introduce 3PoinTr, a method that provides robot policies with a global 3D point-track plan predicted from human videos. Given an initial point cloud, 3PoinTr predicts dense 3D point tracks spanning the full task and compresses them into a small set of tokens that remain fixed throughout execution. At each policy query, the policy combines these plan tokens with the current observation to predict robot actions. This formulation enables the policy to extract useful motion cues from human videos. We find this formulation transfers exceptionally well, even when human demonstration motions are infeasible for robots. In controlled simulation and real-world evaluations, 3PoinTr achieves an average success rate 25.0 percentage points higher than the strongest behavior-cloning and video-pretraining baselines on real-world tasks, and an improvement of 29.6 percentage points in simulation. Ablations establish the benefits of our method, including predicting non-embodiment 3D tracks once as a global plan, Perceiver-IO compression, and a residual cross-attention mechanism. Our point-track prediction network also outperforms a strong 3D-flow baseline by retaining supervision for temporarily occluded points. Project page: https://adamhung60.github.io/3PoinTr/

Adam Hung, Bardienus Pieter Duisterhof, Jeffrey Ichnowski

Carnegie Mellon University

Human-to-Robot Motion ImitationRobot Policy LearningRobot Learning from VideoRobot Imitation LearningCross-Embodiment Robot Learning3D Point Tracking+1
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Mar 9, 2026

AutoTraces: Autoregressive Trajectory Forecasting via Multimodal Large Language Models

We present AutoTraces, an autoregressive vision-language-trajectory model for robot trajectory forecasting in humam-populated environments, which harnesses the inherent reasoning capabilities of large language models (LLMs) to model complex human behaviors. In contrast to prior works that rely solely on textual representations, our key innovation lies in a novel trajectory tokenization scheme, which represents waypoints with point tokens as categorical and positional markers while encoding waypoint numerical values as corresponding point embeddings, seamlessly integrated into the LLM's space through a lightweight encoder-decoder architecture. This design preserves the LLM's native autoregressive generation mechanism while extending it to physical coordinate spaces, facilitates modeling of long-term interactions in trajectory data. We further introduce an automated chain-of-thought (CoT) generation mechanism that leverages a multimodal LLM to infer spatio-temporal relationships from visual observations and trajectory data, eliminating reliance on manual annotation. Through a two-stage training strategy, our AutoTraces achieves SOTA forecasting accuracy, particularly in long-horizon prediction, while exhibiting strong cross-scene generalization and supporting flexible-length forecasting.

Teng Wang, Yanting Lu, Ruize Wang

Robot Motion PredictionTrajectory PredictionMultimodal Trajectory PredictionHuman Trajectory PredictionTrajectory Representation Learning
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Mar 8, 2026

Context-Enriched Natural Language Descriptions of Vessel Trajectories

We address the problem of transforming raw vessel trajectory data collected from AIS into structured and semantically enriched representations interpretable by humans and directly usable by machine reasoning systems. We propose a context-aware trajectory abstraction framework that segments noisy AIS sequences into distinct trips each consisting of clean, mobility-annotated episodes. Each episode is further enriched with multi-source contextual information, such as nearby geographic entities, offshore navigation features, and weather conditions. Crucially, such representations can support generation of controlled natural language descriptions using LLMs. We empirically examine the quality of such descriptions generated using several LLMs over AIS data along with open contextual features. By increasing semantic density and reducing spatiotemporal complexity, this abstraction can facilitate downstream analytics and enable integration with LLMs for higher-level maritime reasoning tasks.

Kostas Patroumpas, Alexandros Troupiotis-Kapeliaris, Giannis Spiliopoulos +4

Archimedes/Athena RC, Greece · University of the Aegean, Greece · Athena RC, Greece +1

Maritime TransportationTrajectory Representation LearningText Generation
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Mar 4, 2026

Structural Action Transformer for 3D Dexterous Manipulation

Achieving human-level dexterity in robots via imitation learning from heterogeneous datasets is hindered by the challenge of cross-embodiment skill transfer, particularly for high-DoF robotic hands. Existing methods, often relying on 2D observations and temporal-centric action representation, struggle to capture 3D spatial relations and fail to handle embodiment heterogeneity. This paper proposes the Structural Action Transformer (SAT), a new 3D dexterous manipulation policy that challenges this paradigm by introducing a structural-centric perspective. We reframe each action chunk not as a temporal sequence, but as a variable-length, unordered sequence of joint-wise trajectories. This structural formulation allows a Transformer to natively handle heterogeneous embodiments, treating the joint count as a variable sequence length. To encode structural priors and resolve ambiguity, we introduce an Embodied Joint Codebook that embeds each joint's functional role and kinematic properties. Our model learns to generate these trajectories from 3D point clouds via a continuous-time flow matching objective. We validate our approach by pre-training on large-scale heterogeneous datasets and fine-tuning on simulation and real-world dexterous manipulation tasks. Our method consistently outperforms all baselines, demonstrating superior sample efficiency and effective cross-embodiment skill transfer. This structural-centric representation offers a new path toward scaling policies for high-DoF, heterogeneous manipulators.

Xiaohan Lei, Min Wang, Bohong Weng +2

MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition, University of Science and Technology of China · Institute of Artificial Intelligence, Hefei Comprehensive National Science Center

Robotic ManipulationRobot Imitation LearningCross-Embodiment Robot LearningTrajectory Representation Learning
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Mar 2, 2026

TopoCurate:Modeling Interaction Topology for Tool-Use Agent Training

Training tool-use agents typically relies on outcome-based filtering: Supervised Fine-Tuning (SFT) on successful trajectories and Reinforcement Learning (RL) on pass-rate-selected tasks. However, this paradigm ignores interaction dynamics: successful trajectories may lack error recovery or exhibit redundancy, while pass rates fail to distinguish structurally informative tasks from trivial ones. We propose TopoCurate, an interaction-aware framework that projects multi-trial rollouts from the same task into a unified semantic quotient topology. By merging equivalent action-observation states, this projection transforms scattered linear trajectories into a structured manifold that explicitly captures how tool invocations and environmental responses drive the divergence between effective strategies and failure modes. Leveraging this representation, we introduce a dual-selection mechanism: for SFT, we prioritize trajectories demonstrating reflective recovery, semantic efficiency, and strategic diversity to mitigate covariate shift and mode collapse; for RL, we select tasks with high error branch ratios and strategic heterogeneity, maximizing gradient Signal-to-Noise Ratio to address vanishing signals in sparse-reward settings. Evaluations on BFCLv3 and Tau2 Bench show that TopoCurate achieves consistent gains of 4.2% (SFT) and 6.9% (RL) over state-of-the-art baselines. We will release the code and data soon for further investigations.

Jinluan Yang, Yuxin Liu, Zhengyu Chen +7

Zhejiang University, Hangzhou, China · Meituan, Beijing, China.

LLM Agent TrainingRL for Tool UseTrajectory Representation Learning
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Mar 2, 2026

Towards Robot Skill Learning and Adaptation with Gaussian Processes

General robot skill adaptation requires expressive representations robust to varying task configurations. While recent learning-based skill adaptation methods refined via Reinforcement Learning (RL), have shown success, existing skill models often lack sufficient representational capacity for anything beyond minor environmental changes. In contrast, Gaussian Process (GP)-based skill modelling provides an expressive representation with useful analytical properties; however, adaptation of GP-based skills remains underexplored. This paper proposes a novel, robust skill adaptation framework that utilises GPs with sparse via-points for compact and expressive modelling. The model considers the trajectory's poses and leverages its first and second analytical derivatives to preserve the skill's kinematic profile. We present three adaptation methods to cater for the variability between initial and observed configurations. Firstly, an optimisation agent that adjusts the path's via-points while preserving the demonstration velocity. Second, a behaviour cloning agent trained to replicate output trajectories from the optimisation agent. Lastly, an RL agent that has learnt to modify via-points whilst maintaining the kinematic profile and enabling online capabilities. Evaluated across three tasks (drawer opening, cube-pushing and bar manipulation) in both simulation and hardware, our proposed methods outperform every benchmark in success rates. Furthermore, the results demonstrate that the GP-based representation enables all three methods to attain high cosine similarity and low velocity magnitude errors, indicating strong preservation of the kinematic profile. Overall, our formulation provides a compact representation capable of adapting to large deviations from a single demonstrated skill.

A K M Nadimul Haque, Fouad Sukkar, Sheila Sutjipto +3

Robot Skill LearningRobot LearningRobotic ManipulationTrajectory Representation Learning
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Oct 16, 2025

Capturing Context-Aware Route Choice Semantics for Trajectory Representation Learning

Trajectory representation learning (TRL) aims to encode raw trajectory data into low-dimensional embeddings for downstream tasks such as travel time estimation, mobility prediction, and trajectory similarity analysis. From a behavioral perspective, a trajectory reflects a sequence of route choices within an urban environment. However, most existing TRL methods ignore this underlying decision-making process and instead treat trajectories as static, passive spatiotemporal sequences, thereby limiting the semantic richness of the learned representations. To bridge this gap, we propose CORE, a TRL framework that integrates context-aware route choice semantics into trajectory embeddings. CORE first incorporates a multi-granular Environment Perception Module, which leverages large language models (LLMs) to distill environmental semantics from point of interest (POI) distributions, thereby constructing a context-enriched road network. Building upon this backbone, CORE employs a Route Choice Encoder with a mixture-of-experts (MoE) architecture, which captures route choice patterns by jointly leveraging the context-enriched road network and navigational factors. Finally, a Transformer encoder aggregates the route-choice-aware representations into a global trajectory embedding. Extensive experiments on 4 real-world datasets across 6 downstream tasks demonstrate that CORE consistently outperforms 15 state-of-the-art TRL methods, achieving an average improvement of 9.20% over the best-performing baseline. Our code is available at https://github.com/caoji2001/CORE.

Ji Cao, Yu Wang, Tongya Zheng +6

College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China · Zhejiang Lab, Hangzhou 311121, China · Zhejiang Provincial Engineering Research Center for Real-Time SmartTech in Urban Security Governance, Hangzhou City University, Hangzhou 310015, China +2

Geospatial Representation LearningTrajectory Representation Learning
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Jun 17, 2025

HiT-JEPA: A Hierarchical Self-supervised Trajectory Embedding Framework for Similarity Computation

The representation of urban trajectory data plays a critical role in effectively analyzing spatial movement patterns. Despite considerable progress, the challenge of designing trajectory representations that can capture diverse and complementary information remains an open research problem. Existing methods struggle in incorporating trajectory fine-grained details and high-level summary in a single model, limiting their ability to attend to both long-term dependencies while preserving local nuances. To address this, we propose HiT-JEPA (Hierarchical Interactions of Trajectory Semantics via a Joint Embedding Predictive Architecture), a unified framework for learning multi-scale urban trajectory representations across semantic abstraction levels. HiT-JEPA adopts a three-layer hierarchy that progressively captures point-level fine-grained details, intermediate patterns, and high-level trajectory abstractions, enabling the model to integrate both local dynamics and global semantics in one coherent structure. Extensive experiments on multiple real-world datasets for trajectory similarity computation show that HiT-JEPA's hierarchical design yields richer, multi-scale representations. Code is available at: https://anonymous.4open.science/r/HiT-JEPA.

Lihuan Li, Hao Xue, Shuang Ao +2

University of New South Wales Sydney, Australia · Hong Kong University of Science and Technology (Guangzhou) Guangzhou, China

Intelligent Transportation SystemsHierarchical Representation LearningTrajectory Representation Learning
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