---
# 这是YAML元数据块，帮助Hugging Face更好地展示您的数据
license: cc-by-4.0
language:
  - en
  - zh
tags:
  - transportation
  - spatiotemporal
  - time-series
  - travel-time-prediction
  - urban-computing
  - graph-neural-networks
pretty_name: "UrbanLPR Dataset"
---

# UrbanLPR-Dataset: A Large-Scale License Plate Recognition Dataset for Travel Time Prediction

This repository contains the **UrbanLPR Dataset**, a large-scale dataset of license plate recognition data collected in Dongguan, China, designed to support research in urban traffic analysis and travel time prediction.

## Paper
This dataset was created for our research paper, which has been accepted for publication in the journal **Measurement**.

*   **Title:** Urban Road Network Travel Time Prediction Method Based on "Node-Link-Network'' Spatiotemporal Reconstruction: A License Plate Data-Driven WGCN-BiLSTM Model
*   **Authors:** Weiwei Qi*, Bin Rao*, and Jiabing Wu (* co-first authors)
*   **Journal:** **Measurement** (Accepted for publication)
*   **Corresponding Author:** Jiabing Wu (jiabinwu@fosu.edu.cn)

## Dataset Description

This dataset contains vehicle passage records from License Plate Recognition (LPR) cameras deployed at major intersections in Dongguan, China, from **March 1, 2023, to March 20, 2023**. All data has been fully anonymized to protect privacy.

The dataset is ideal for research in:
*   Travel time prediction and estimation
*   Spatiotemporal data mining and forecasting
*   Graph-based traffic analysis
*   Path reconstruction in sparsely sensored networks

### File Structure

The dataset is provided as a `.zip` package containing the following structure:

```text
UrbanLPR-Dataset_v1.0/
├── 2023-03-01.parquet
├── 2023-03-02.parquet
│   ...
├── 2023-03-20.parquet
├── distance.csv
├── intersection_map.jpg
└── vehicle_type_mapping.csv
```


#### Main Data Files (`.parquet`)
Each `.parquet` file contains the anonymized traffic data for a single day. The schema is as follows:

| Column Name       | Data Type  | Description                                      |
|-------------------|------------|--------------------------------------------------|
| `vehicle_id`      | `string`   | Anonymized 64-character unique vehicle identifier. |
| `timestamp`       | `datetime` | The exact time a vehicle was detected.           |
| `intersection_id` | `integer`  | A unique ID for the intersection.                |
| `vehicle_type`    | `integer`  | A numeric ID for the vehicle type.               |

#### Auxiliary Files
*   **`distance.csv`**: A matrix containing the road network distance (in meters) between every pair of intersections.
*   **`intersection_map.jpg`**: A map of the study area, labeling each intersection with its `intersection_id`.
*   **`vehicle_type_mapping.csv`**: A table mapping the numeric `vehicle_type` ID to its Chinese and English names.

## How to Cite
If you use this dataset in your research, please cite our paper.

```bibtex
@article{qi2025urban,
  title={Urban road network travel time prediction method based on “node-link-network” spatiotemporal reconstruction: A license plate data-driven WGCN-BiLSTM model},
  author={Qi, Weiwei and Rao, Bin and Wu, Jiabing},
  journal={Measurement},
  pages={118339},
  year={2025},
  publisher={Elsevier}
}