One Fits All: General Mobility Trajectory Modeling via Masked Conditional Diffusion

2025-01-23

AI Summary

Existing trajectory models are predominantly designed for single tasks, rendering them inadequate for unified handling of heterogeneous tasks such as generation, imputation, and prediction. To address this limitation, we propose the first general-purpose multi-task trajectory modeling framework. Our method introduces three key innovations: (1) a novel masked conditional diffusion mechanism that supports flexible input/output formats and diverse task-specific conditioning; (2) a context-aware trajectory embedding fusion architecture that jointly models spatiotemporal dynamics and user preferences; and (3) classifier-free guidance to enhance generation fidelity. Evaluated on mainstream trajectory benchmarks, our framework consistently outperforms state-of-the-art methods—achieving over 13% improvement in generation performance—and is the first to enable efficient, single-model adaptation across multiple trajectory tasks without architectural modification.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Trajectory data play a crucial role in many applications, ranging from network optimization to urban planning. Existing studies on trajectory data are task-specific, and their applicability is limited to the specific tasks on which they have been trained, such as generation, recovery, or prediction. However, the potential of a unified model has not yet been fully explored in trajectory modeling. Although various trajectory tasks differ in inputs, outputs, objectives, and conditions, they share common mobility patterns. Based on these common patterns, we can construct a general framework that enables a single model to address different tasks. However, building a trajectory task-general framework faces two critical challenges: 1) the diversity in the formats of different tasks and 2) the complexity of the conditions imposed on different tasks. In this work, we propose a general trajectory modeling framework via masked conditional diffusion (named GenMove). Specifically, we utilize mask conditions to unify diverse formats. To adapt to complex conditions associated with different tasks, we utilize historical trajectory data to obtain contextual trajectory embeddings, which include rich contexts such as spatiotemporal characteristics and user preferences. Integrating the contextual trajectory embedding into diffusion models through a classifier-free guidance approach allows the model to flexibly adjust its outputs based on different conditions. Extensive experiments on mainstream tasks demonstrate that our model significantly outperforms state-of-the-art baselines, with the highest performance improvement exceeding 13% in generation tasks.

Problem

• Trajectory Data
• Multi-task Modeling
• Adaptability

Innovation

• GenMove
• Masked Conditional Diffusion
• Multi-task Adaptability