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README.md

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-license: cc-by-4.0
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+---
+# 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:
+
+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{Qi2023UrbanLPR,
+  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},
+  year    = {2023},
+  note    = {Accepted for publication}
+}
+
+