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license: odbl
language:
- en
tags:
- urban computing
- spatial-temporal data mining
- trajectory analysis
size_categories:
- 1M<n<10M
viewer: false
---
<p align="left">
<img src="Logo.png" width="300"/>
<p>
## WorldTrace Dataset
### 🗺️ Overview
WorldTrace is a large-scale, high-quality, globally covering GPS trajectory dataset.
Trajectory data provides an important data source for understanding human mobility patterns and transforming urban intelligence. However, existing trajectory modeling methods have limitations in terms of task specificity, regional dependency, and data sensitivity. The construction of the WorldTrace dataset aims to address these challenges by providing unprecedented geographical diversity and data scale, thereby promoting the development of region-agnostic universal trajectory models. This dataset contains 2.45 million trajectories collected from 70 countries worldwide, totaling 8.8 billion raw GPS points, making it the most geographically comprehensive and diverse trajectory dataset currently available.
### 📊 Key Statistics
| Trajectory Information | Statistical Values |
| --------------------------- | -------------------------------- |
| Number of Trajectories | 2.45 million |
| Number of Trajectory Points | 880 million (after processing) |
| Geographic Coverage | 70 countries |
| Time Span | August 2021 - December 2023 |
| Sampling Interval | 1 second (after standardization) |
| Average Trajectory Duration | ~6 minutes |
| Average Trajectory Distance | 5.73 kilometers |
| Average Trajectory Speed | 48.0 km/h |
| Average Trajectory Points | ~358 points |
### 📁 File Structure and Data Format
We provide two types of files: processed trajectory data (Trajectory.zip) and metadata (Meta.zip). Each record in the dataset typically contains the following fields:
📄 Trajectory: **csv** files
- `time`: Timestamp in ISO 8601 format
- `latitude`: Latitude coordinates under WGS84 standard
- `longitude`: Longitude coordinates under WGS84 standard
- `altitude`: Altitude of trajectory points (optional)
- `osm_way_id`: Way ID in OpenStreetMap
- `matched_latitude`: Matched latitude coordinates
- `matched_longitude`: Matched longitude coordinates
- `matched_distance`: Distance between trajectory point and matched coordinates
- `matched_type`: Type of matched coordinates
📝 Meta: **json** files
- `Filename`: Original trajectory file name
- `Uploaded`: Upload time of trajectory file
- `Points`: Number of data points in trajectory file
- `Start coordinate`: Starting coordinates of trajectory (longitude, latitude)
- `End coordinate`: Ending coordinates of trajectory (longitude, latitude)
- `Owner`: Uploader of trajectory data
- `Description`: Description information of trajectory
- `Tags`: Tags related to trajectory
- `Visibility`: Visibility description of trajectory
- `Distance`: Total distance of trajectory
- `Time`: Total duration of trajectory
- `geometry`: Geometric representation of trajectory, typically an array of [longitude, latitude]
### ⏬ Download
#### Using Command Line
```bash
hf download OpenTrace/WorldTrace --repo-type dataset --local-dir <Your_dir>
```
Only download Trajectory:
```bash
hf download OpenTrace/WorldTrace --repo-type dataset --local-dir <Your_dir> --include "Trajectory.zip"
```
For other download methods, please refer to https://huggingface.co/docs/huggingface_hub/en/guides/download and https://huggingface.co/docs/huggingface_hub/en/guides/cli
### ⚖️ Privacy and License
**Privacy:** To protect user privacy and comply with international data protection regulations, all data collection strictly adheres to privacy regulations and ethical guidelines. Trajectory data has been anonymized, and any personally identifiable information has been excluded.
**License:** The original data of this dataset follows the OpenStreetMap Open Database License ([ODbL](https://opendatacommons.org/licenses/odbl/)) version 1.0. Any derivative datasets we release will also follow the same license terms.
If you use the WorldTrace dataset in your research, please cite our paper:
```ini
@article{unitraj2025,
title={UniTraj: Learning a Universal Trajectory Foundation Model from Billion-Scale Worldwide Traces},
author={Zhu, Yuanshao and Yu, James Jianqiao and Zhao, Xiangyu and Zhou, Xun and Han, Liang and Wei, Xuetao and Liang, Yuxuan},
journal={Advances in Neural Information Processing Systems},
volume={38},
year={2025}
}
``` |