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--- |
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license: cc-by-4.0 |
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language: |
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- en |
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- zh |
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tags: |
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- transportation |
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- spatiotemporal |
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- time-series |
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- travel-time-prediction |
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- urban-computing |
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- graph-neural-networks |
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pretty_name: "UrbanLPR Dataset" |
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--- |
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# UrbanLPR-Dataset: A Large-Scale License Plate Recognition Dataset for Travel Time Prediction |
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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. |
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## Paper |
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This dataset was created for our research paper, which has been accepted for publication in the journal **Measurement**. |
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* **Title:** Urban Road Network Travel Time Prediction Method Based on "Node-Link-Network'' Spatiotemporal Reconstruction: A License Plate Data-Driven WGCN-BiLSTM Model |
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* **Authors:** Weiwei Qi*, Bin Rao*, and Jiabing Wu (* co-first authors) |
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* **Journal:** **Measurement** (Accepted for publication) |
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* **Corresponding Author:** Jiabing Wu ([email protected]) |
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## Dataset Description |
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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. |
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The dataset is ideal for research in: |
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* Travel time prediction and estimation |
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* Spatiotemporal data mining and forecasting |
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* Graph-based traffic analysis |
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* Path reconstruction in sparsely sensored networks |
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### File Structure |
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The dataset is provided as a `.zip` package containing the following structure: |
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```text |
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UrbanLPR-Dataset_v1.0/ |
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├── 2023-03-01.parquet |
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├── 2023-03-02.parquet |
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│ ... |
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├── 2023-03-20.parquet |
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├── distance.csv |
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├── intersection_map.jpg |
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└── vehicle_type_mapping.csv |
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``` |
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#### Main Data Files (`.parquet`) |
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Each `.parquet` file contains the anonymized traffic data for a single day. The schema is as follows: |
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| Column Name | Data Type | Description | |
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|-------------------|------------|--------------------------------------------------| |
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| `vehicle_id` | `string` | Anonymized 64-character unique vehicle identifier. | |
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| `timestamp` | `datetime` | The exact time a vehicle was detected. | |
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| `intersection_id` | `integer` | A unique ID for the intersection. | |
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| `vehicle_type` | `integer` | A numeric ID for the vehicle type. | |
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#### Auxiliary Files |
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* **`distance.csv`**: A matrix containing the road network distance (in meters) between every pair of intersections. |
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* **`intersection_map.jpg`**: A map of the study area, labeling each intersection with its `intersection_id`. |
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* **`vehicle_type_mapping.csv`**: A table mapping the numeric `vehicle_type` ID to its Chinese and English names. |
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## How to Cite |
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If you use this dataset in your research, please cite our paper. |
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```bibtex |
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@article{qi2025urban, |
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title={Urban road network travel time prediction method based on “node-link-network” spatiotemporal reconstruction: A license plate data-driven WGCN-BiLSTM model}, |
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author={Qi, Weiwei and Rao, Bin and Wu, Jiabing}, |
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journal={Measurement}, |
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pages={118339}, |
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year={2025}, |
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publisher={Elsevier} |
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} |
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