[Paper Title]

📄 Paper: [Full Citation — Authors, Year, Journal, DOI]
🗂️ Status: [ ] Under Review  |  [ ] Accepted  |  [x] Published
👤 Contact: [Student Name] · [PSU Email] · [XB's email as PI]

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Overview

This repository contains the code and data resources accompanying the paper:

[Paper Title]
[Author 1], [Author 2], Xianbiao Hu
[Journal Name], [Year]. DOI: https://doi.org/xxx

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Repository Structure

.
├── data/
│   ├── raw/            # Raw data (or external link — see data/README.md)
│   ├── processed/      # Preprocessed inputs ready for model/analysis
│   └── README.md       # Data description, source, and download instructions
│
├── src/                # Core source code (importable modules)
│   ├── model/
│   ├── utils/
│   └── __init__.py
│
├── experiments/        # Entry-point scripts to reproduce paper results
│   ├── train.py
│   ├── evaluate.py
│   └── configs/        # YAML/JSON config files (hyperparameters, paths)
│
├── notebooks/          # Exploratory analysis and result visualization
│   └── demo.ipynb
│
├── results/
│   └── figures/        # Key figures from the paper
│
├── requirements.txt    # Python dependencies
├── LICENSE
└── README.md

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Getting Started

1. Clone the repository

git clone https://github.com/[student-username]/[repo-name].git
cd [repo-name]

2. Set up the environment

# Using pip
pip install -r requirements.txt

# Or using conda
conda env create -f environment.yml
conda activate [env-name]

Tested on Python [X.X], [OS]. Key dependencies: [e.g., PyTorch 2.x, NumPy, Pandas].

3. Download the data

Data is hosted on Zenodo: https://doi.org/10.5281/zenodo.XXXXXXX

# Place downloaded files in:
data/raw/

See data/README.md for detailed data description and format.

4. Run the experiments

# Reproduce main results (Table X in the paper)
python experiments/evaluate.py --config experiments/configs/main.yaml

# Train from scratch
python experiments/train.py --config experiments/configs/train.yaml

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Data

Item	Description	Format	Size	Link
[Dataset name]	[Brief description]	CSV / JSON / PCD	[X MB]	[Zenodo DOI]

Full data documentation → data/README.md

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Results

Metric	Value
[e.g., RMSE]	[X.XX]
[e.g., MAE]	[X.XX]

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Cite This Paper

If you use this code or dataset, please cite:

@article{[citekey][year],
  author    = {[Author1] and [Author2] and Hu, Xianbiao},
  title     = {[Paper Title]},
  journal   = {[Journal Name]},
  year      = {[Year]},
  volume    = {[Vol]},
  pages     = {[Pages]},
  doi       = {[DOI]}
}

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Related Resources

• 🏠 Smart Mobility Lab: sites.psu.edu/xbhu
• 📖 Research Atlas: atlas.mobilitypsu.com
• 📦 Dataset (Zenodo): [DOI link]
• 📊 Slides: [Link if available]

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License

Code: MIT License
Data: CC BY 4.0 — See data/README.md