This repository offers a curated collection of deep learning and foundation models for trajectory data management and mining. It serves as a comprehensive resource hub for researchers and practitioners, providing access to papers, code, datasets, and tools, thereby accelerating advancements in trajectory computing applications.

How It Works

The project systematically categorizes and lists state-of-the-art deep learning methods and Large Language Models (LLMs) applied to trajectory data. It covers both trajectory data management (e.g., pre-processing, storage, indexing, similarity, clustering, visualization) and trajectory data mining (e.g., forecasting, recommendation, classification, travel time estimation, anomaly detection, mobility generation), highlighting novel approaches and recent LLM integrations.

Quick Start & Requirements

This repository is a curated list of resources and does not provide direct installation or execution commands. It links to external datasets and tools such as SUMO, SafeGraph, PyMove, and Scikit-mobility, which may have their own setup requirements.

Highlighted Details

• Features a comprehensive survey paper, "Deep Learning for Trajectory Data Management and Mining: A Survey and Beyond."
• Organizes resources across detailed taxonomies for both trajectory data management and mining tasks.
• Includes recent advancements and applications of Large Language Models (LLMs) in trajectory computing.
• Provides an extensive summary of relevant datasets and tools for trajectory analysis.

Maintenance & Community

The repository is maintained by Citymind LAB at HKUST(GZ) and is committed to ongoing updates. It encourages community contributions via pull requests for missing resources or corrections. A news item indicates the launch of the DL4Traj project on March 19, 2024. No specific community channels like Discord or Slack are listed.

Licensing & Compatibility

No specific license information is provided for the repository itself. The listed resources and papers are subject to their respective licenses.

Limitations & Caveats

As a curated list, the repository does not offer direct software functionality or installation instructions. It relies on external resources, and a planned Google Cloud Drive for datasets is noted as "Coming soon." No explicit license details are available for the repository's content.