---
title: "DGCPath: Distribution-Aware Generative Contrastive Framework for Self-supervised Path Representation Learning"
authors:
  - "Sean Bin Yang"
  - "Hao Miao"
  - "Zongyi Xu"
  - "Jilin Hu"
  - "Xiangmeng Wang"
  - "Hua Lu"
  - "Bin Yang"
  - "Christian S. Jensen"
doi: "10.24963/ijcai.2026/352"
url: "https://ijcai.org/proceedings/2026/352"
pdf: "https://ijcai.org/proceedings/2026/0352.pdf"
venue: "Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence"
year: 2026
pages: "3169-3177"
track: "Main Track"
keywords:
  - "Data Mining: Mining spatial and/or temporal data"
---

# DGCPath: Distribution-Aware Generative Contrastive Framework for Self-supervised Path Representation Learning

Sean Bin Yang, Hao Miao, Zongyi Xu, Jilin Hu, Xiangmeng Wang, Hua Lu, Bin Yang, Christian S. Jensen

*Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence*, pages 3169-3177. DOI: [10.24963/ijcai.2026/352](https://doi.org/10.24963/ijcai.2026/352)

## Abstract

Due to proliferation of vehicle trajectory data from advanced sensing technologies, path representation learning has become a pivotal task in intelligent transportation systems. Although existing self-supervised approaches work to some extent, their dependence on deterministic contrastive learning paradigms and handcrafted view augmentation strategies inherently restricts cross-scenario generalization capabilities. To address these limitations, we present DGCPath – an innovative Distribution-aware Generative Contrastive learning framework for Path representation. This architecture establishes a synergistic connection between generative modeling and distributional contrastive learning, enabling the acquisition of robust and transferable feature embeddings. Specifically, our framework incorporates: (1) a diffusion-based view generator that autonomously produces semantically coherent yet diversified trajectory views from Gaussian noise distributions; (2) a variational contrastive mechanism enforcing latent feature alignment at the distribution level, transcending conventional instance-wise consistency; and (3) a novel generative cross-supervision module that reinforces view-level consistency through cross-view reconstruction learning. Comprehensive evaluations on three real-world trajectory datasets demonstrate DGCPath's superior performance over state-of-the-art baselines in two distinct downstream tasks, validating its enhanced generalization capacity and representation effectiveness.