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KDD25: The source code of our paper "UoMo: A Universal Model of Mobile Traffic Forecasting for Wireless Network Optimization"

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# *KDD'25: UoMo: A Universal Model of Mobile Traffic Forecasting for Wireless Network Optimization*

<a href="#">Haoye Chai1</a>,
 <a href="#">Shiyuan Zhang1</a>,
 <a href="#">Xiaoqian Qi1</a>,
 <a href="#">Baohua Qiu2</a>,
 <a href="#">Yong Li1*</a>

1 Tsinghua University &nbsp; &nbsp;
 2 China Mobile

This is the official implementation of our foundation model for mobile traffic data, accepted by the KDD 2025 ADS track.

---

# Overall framework

Our model adopts a three-stage paradigm consisting of <code>tokenization</code>, <code>pre-training</code> and <code>fine-tuning</code>. The tokenization stage transforms the data into an <code>T, H, W</code> representation. The pre-training stage learns the fundamental features of the data, while the fine-tuning stage incorporates the number of users and the distribution of POIs as conditional inputs.

# Provided Datasets

We provide three datasets used in the original model training: **TrafficNJ**, **TrafficSD**, and **TrafficNC**, along with corresponding **user count** and **POI data**.

## 1. Data Usage

- The datasets are located in the `dataset64time` folder.
- Please extract each `.rar` file to obtain the data in `.json` format.

## 2. Data Format

Each dataset is stored as a dictionary containing the following four keys:

- `train`: shape `N₁ × 1 × 64 × 4 × 4`  
  - `64`: temporal length  
  - `4 × 4`: spatial patches of the geographical area

- `test`: shape `N₂ × 1 × 64 × 4 × 4`  
  - `64`: temporal length  
  - `4 × 4`: spatial patches of the geographical area

- `val`: shape `N₃ × 1 × 64 × 4 × 4`  
  - `64`: temporal length  
  - `4 × 4`: spatial patches of the geographical area

- `timestamp`: shape `N × 1 × 64 × 2`  
  - `64`: temporal length  
  - `2`: timestamp corresponding to each time step, formatted as `[time, day]`

## 3. Dataset Description

- **TrafficNJ**  
  - 15-minute granularity  
  - timestamp dimension space: `[96, 7]`

- **TrafficNC**  
  - 30-minute granularity  
  - timestamp dimension space: `[48, 7]`

- **TrafficSD**  
  - 1-hour granularity  
  - timestamp dimension: `[24, 7]`

# Getting Started

## 1. Environment

- Python >= 3.7  
- PyTorch >= 2.0.0  
- CUDA >= 11.7

Use the following command to install all required Python modules and packages:

```bash
pip install -r requirements.txt
 ```

## 2. Running the Code

- Extract the `.rar` files in the `dataset64time` folder.

- Run the model using one of the following options:

- **Run pre-training and fine-tuning together**:

```bash
        python run.py
        ```

- **Run pre-training and fine-tuning separately**:

- Pre-training:

```bash
            python main.py
            ```

- Fine-tuning:

```bash
            python main_alignment.py
            ```

## 3. Pre-trained Models and Generated Data Directory

- The pre-trained models and generated data are saved in the following directory:

```
    experiments/Len64_{Dataset_name}_Pretrain
    ```

## 4. Fine-tuned Models and Generated Data Directory

- The fine-tuned models and generated data are saved in the following directory:

```
    experiments/Len64_data_{Dataset_name}_Finetuning
    ```

## 5. Experimental Results

In the original paper, we conducted experiments by mixing multiple city-level datasets as input to UoMo. The resulting performance is shown as follows.

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