FlashST: A Simple and Universal Prompt-Tuning Framework for Traffic Prediction

A pytorch implementation for the paper: FlashST: A Simple and Universal Prompt-Tuning Framework for Traffic Prediction

Zhonghang Li, Lianghao Xia, Yong Xu, Chao Huang* (*Correspondence)

Data Intelligence Lab@University of Hong Kong, South China University of Technology, PAZHOU LAB

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Introduction

In this work, we introduce a simple and universal spatio-temporal prompt-tuning framework, which addresses the significant challenge posed by distribution shift in this field. To achieve this objective, we present FlashST, a framework that adapts pretrained models to the specific characteristics of diverse downstream datasets, thereby improving generalization across various prediction scenarios. We begin by utilizing a lightweight spatio-temporal prompt network for in-context learning, capturing spatio-temporal invariant knowledge and facilitating effective adaptation to diverse scenarios. Additionally, we incorporate a distribution mapping mechanism to align the data distributions of pre-training and downstream data, facilitating effective knowledge transfer in spatio-temporal forecasting. Empirical evaluations demonstrate the effectiveness of our FlashST across different spatio-temporal prediction tasks.

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Getting Started

Table of Contents:

• 1. Code Structure
• 2. Environment
• 3. Run the codes

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1. Code Structure [Back to Top]

• conf: This folder includes parameter settings for FlashST (config.conf) as well as all other baseline models.
• data: The documentation encompasses all the datasets utilized in our work, alongside prefabricated files and the corresponding file generation codes necessary for certain baselines.
• lib: Including a series of initialization methods for data processing, as follows:
  • data_process.py: Load, split, generate data, normalization method, slicing, etc.
  • logger.py: For output printing.
  • metrics.py: Method for calculating evaluation indicators.
  • predifineGraph.py: Predefined graph generation method.
  • TrainInits.py: Training initialization, including settings of optimizer, device, random seed, etc.
• model: Includes the implementation of FlashST and all baseline models, along with the necessary code to support the framework's execution. The args.py script is utilized to generate the required prefabricated data and parameter configurations for different baselines. Additionally, the SAVE folder serves as the storage location for saving the pre-trained models.
• SAVE: This folder serves as the storage location for saving the trained models, including pretrain, eval and ori.

│  README.md
│  requirements.txt
│
├─conf
│  ├─AGCRN
│  ├─ASTGCN
│  ├─FlashST
│  │  │  config.conf
│  │  │  Params_pretrain.py
│  ├─GWN
│  ├─MSDR
│  ├─MTGNN
│  ├─PDFormer
│  ├─ST-WA
│  ├─STFGNN
│  ├─STGCN
│  ├─STSGCN
│  └─TGCN
│
├─data
│  ├─CA_District5
│  ├─chengdu_didi
│  ├─NYC_BIKE
│  ├─PEMS03
│  ├─PEMS04
│  ├─PEMS07
│  ├─PEMS07M
│  ├─PEMS08
│  ├─PDFormer
│  ├─STFGNN
│  └─STGODE
│
├─lib
│  │  data_process.py
│  │  logger.py
│  │  metrics.py
│  │  predifineGraph.py
│  │  TrainInits.py
│
├─model
│  │  FlashST.py
│  │  PromptNet.py
│  │  Run.py
│  │  Trainer.py
│  │
│  ├─AGCRN
│  ├─ASTGCN
│  ├─DMSTGCN
│  ├─GWN
│  ├─MSDR
│  ├─MTGNN
│  ├─PDFormer
│  ├─STFGNN
│  ├─STGCN
│  ├─STGODE
│  ├─STSGCN
│  ├─ST_WA
│  └─TGCN
│
└─SAVE
    └─pretrain
        ├─GWN
        │      GWN_P8437.pth
        │
        ├─MTGNN
        │      MTGNN_P8437.pth
        │
        ├─PDFormer
        │      PDFormer_P8437.pth
        │
        └─STGCN
                STGCN_P8437.pth
            

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2.Environment [Back to Top]

The code can be run in the following environments, other version of required packages may also work.

• python==3.9.12
• numpy==1.23.1
• pytorch==1.9.0
• cudatoolkit==11.1.1

Or you can install the required environment, which can be done by running the following commands:

# cteate new environmrnt
conda create -n FlashST python=3.9.12

# activate environmrnt
conda activate FlashST

# Torch with CUDA 11.1
pip install torch==1.9.1+cu111 torchvision==0.10.1+cu111 torchaudio==0.9.1 -f https://download.pytorch.org/whl/torch_stable.html

# Install required libraries
pip install -r requirements.txt

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3. Run the codes [Back to Top]

• First, download "data" folder from hugging face (data.zip) or wisemodel (data.zip), put it in the FlashST-main directory, unzip and then enter "model" folder:

cd model

• To test different models in various modes, you can execute the Run.py code. There are some examples:

# Evaluate the performance of MTGNN enhanced by FlashST on the PEMS07M dataset
python Run.py -dataset_test PEMS07M -mode eval -model MTGNN

# Evaluate the performance of STGCN enhanced by FlashST on the CA_District5 dataset
python Run.py -dataset_test CA_District5 -mode eval -model STGCN

# Evaluate the original performance of STGCN on the chengdu_didi dataset
python Run.py -dataset_test chengdu_didi -mode ori -model STGCN

# Pretrain from scratch with MTGNN model, checkpoint will be saved in FlashST-main/SAVE/pretrain/MTGNN(model name)/xxx.pth
python Run.py -mode pretrain -model MTGNN

• Parameter setting instructions. The parameter settings consist of two parts: the pre-training model and the baseline model. To avoid any confusion arising from potential overlapping parameter names, we employ a hyphen (-) to specify the parameters of FlashST and use a double hyphen (--) to specify the parameters of the baseline model. Here is an example:

# Set first_layer_embedding_size and out_layer_dim to 32 in STFGNN
python Run.py -model STFGNN -mode ori -dataset_test PEMS08 --first_layer_embedding_size 32 --out_layer_dim 32

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Citation

If you find FlashST useful in your research or applications, please kindly cite:

@misc{li2024flashst,
      title={FlashST: A Simple and Universal Prompt-Tuning Framework for Traffic Prediction}, 
      author={Zhonghang Li and Lianghao Xia and Yong Xu and Chao Huang},
      year={2024},
      eprint={2405.17898},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

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Acknowledgements

We developed our code framework drawing inspiration from AGCRN and GPT-ST. Furthermore, the implementation of the baselines primarily relies on a combination of the code released by the original author and the code from LibCity. We extend our heartfelt gratitude for their remarkable contribution.