OpenCity: Open Spatio-Temporal Foundation Models for Traffic Prediction

A pytorch implementation for the paper: OpenCity: Open Spatio-Temporal Foundation Models for Traffic Prediction

Zhonghang Li, Long Xia, Lei Shi, Yong Xu, Dawei Yin, Chao Huang* (*Correspondence)

Data Intelligence Lab@University of Hong Kong, South China University of Technology, Baidu Inc

This repository hosts the code, data, and model weights of OpenCity.

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🎉 News

• [2024.08.21] Release the full paper.
• [2024.08.20] Add video.
• [2024.08.15] 🚀🚀 Release the code, model weights and datasets of OpenCity.
• [2024.08.15] Release baselines codes.

🎯🎯📢📢 We upload the models and data used in our OpenCity on 🤗 Huggingface. We highly recommend referring to the table below for further details:

🤗 Huggingface Address	🎯 Description
https://huggingface.co/hkuds/OpenCity-Plus	It's the model weights of our OpenCity-Plus.
https://huggingface.co/datasets/hkuds/OpenCity-dataset/tree/main	We released the datasets used in OpenCity.

👉 TODO

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Introduction

In this work, we aim to unlock new possibilities for building versatile, resilient and adaptive spatio-temporal foundation models for traffic prediction. To achieve this goal, we introduce a novel foundation model, named OpenCity, that can effectively capture and normalize the underlying spatio-temporal patterns from diverse data characteristics, facilitating zero-shot generalization across diverse urban environments. OpenCity integrates the Transformer architecture with graph neural networks to model the complex spatio-temporal dependencies in traffic data. By pre-training OpenCity on large-scale, heterogeneous traffic datasets, we enable the model to learn rich, generalizable representations that can be seamlessly applied to a wide range of traffic forecasting scenarios. Experimental results demonstrate that OpenCity exhibits exceptional zero-shot predictive performance in various traffic prediction tasks.

Main Results

Outstanding Zero-shot Prediction Performance. OpenCity achieves significant zero-shot learning breakthroughs, outperforming most baselines even without fine-tuning. This highlights the approach's robustness and effectiveness at learning complex spatio-temporal patterns in large-scale traffic data, extracting universal insights applicable across downstream tasks.

Demo Video

OpenCity_github.mp4

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Getting Started

Table of Contents:

• 1. Code Structure
• 2. Environment
• 3. Training OpenCity
  • 3.1. Preparing Pre-trained Data
  • 3.2. Pre-training
• 4. Evaluating

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1. Code Structure [Back to Top]

├── conf/
│   ├── AGCRN/
│   │   └── AGCRN.conf
│   ├── ASTGCN/
│   │   └── ASTGCN.conf
│   ├── general_conf/
│   │   ├── global_baselines.conf
│   │   └── pretrain.conf
│   ├── GWN/
│   │   └── GWN.conf
│   ├── MSDR/
│   │   └── MSDR.conf
│   ├── MTGNN/
│   │   └── MTGNN.conf
│   ├── OpenCity/
│   │   └── OpenCity.conf
│   ├── PDFormer/
│   │   └── PDFormer.conf
│   ├── STGCN/
│   │   └── STGCN.conf
│   ├── STSGCN/
│   │   └── STSGCN.conf
│   ├── STWA/
│   │   └── STWA.conf
│   └── TGCN/
│       └── TGCN.conf
├── data/
│   ├── generate_ca_data.py
│   └── README.md
├── lib/
│   ├── data_process.py
│   ├── logger.py
│   ├── metrics.py
│   ├── Params_predictor.py
│   ├── Params_pretrain.py
│   ├── predifineGraph.py
│   └── TrainInits.py
├── model/
│   ├── AGCRN/
│   │   ├── AGCN.py
│   │   ├── AGCRN.py
│   │   ├── AGCRNCell.py
│   │   └── args.py
│   ├── ASTGCN/
│   │   ├── args.py
│   │   └── ASTGCN.py
│   ├── GWN/
│   │   ├── args.py
│   │   └── GWN.py
│   ├── MSDR/
│   │   ├── args.py
│   │   ├── gmsdr_cell.py
│   │   └── gmsdr_model.py
│   ├── MTGNN/
│   │   ├── args.py
│   │   └── MTGNN.py
│   ├── OpenCity/
│   │   ├── args.py
│   │   └── OpenCity.py
│   ├── PDFormer/
│   │   ├── args.py
│   │   └── PDFormer.py
│   ├── ST_WA/
│   │   ├── args.py
│   │   ├── attention.py
│   │   └── ST_WA.py
│   ├── STGCN/
│   │   ├── args.py
│   │   └── stgcn.py
│   ├── STSGCN/
│   │   ├── args.py
│   │   └── STSGCN.py
│   └── TGCN/
│       ├── args.py
│       └── TGCN.py
│   ├── Model.py
│   ├── BasicTrainer.py
│   ├── Run.py
└── model_weights/
    ├── OpenCity/
    └── README.md

2.Environment [Back to Top]

Please first clone the repo and install the required environment, which can be done by running the following commands:

conda create -n opencity python=3.9.13

conda activate opencity

# Torch (other versions are also ok)
pip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 torchaudio==0.9.0 -f https://download.pytorch.org/whl/torch_stable.html

# Clone our OpenCity or download it
git clone https://github.com/HKUDS/OpenCity.git
cd OpenCity-main

# Install required libraries
pip install -r requirements.txt

3. Training OpenCity [Back to Top]

3.1. Preparing Pre-trained Data [Back to Top]

• The model's generalization capabilities and predictive performance were extensively evaluated using a diverse set of large-scale, real-world public datasets covering various traffic-related data categories, including Traffic Flow, Taxi Demand, Bicycle Trajectories, Traffic Speed Statistics, and Traffic Index Statistics, from regions across the United States and China, such as New York City, Chicago, Los Angeles, the Bay Area, Shanghai, Shenzhen, and Chengdu.
  
• These data are organized in OpenCity-dataset. Please download it and put it at ./data. Subsequently, unzip all files and run generate_ca_data.py.

3.2. Pre-training [Back to Top]

• To pretrain the OpenCity model with different configurations, you can execute the Run.py code. There are some examples:

# OpenCity-plus
python Run.py -mode pretrain -model OpenCity -save_pretrain_path OpenCity-plus2.0.pth -batch_size 4 --embed_dim 512 --skip_dim 512 --enc_depth 6

# OpenCity-base
python Run.py -mode pretrain -model OpenCity -save_pretrain_path OpenCity-base2.0.pth -batch_size 8 --embed_dim 256 --skip_dim 256 --enc_depth 3

# OpenCity-mini
python Run.py -mode pretrain -model OpenCity -save_pretrain_path OpenCity-mini2.0.pth -batch_size 16 --embed_dim 128 --skip_dim 128 --enc_depth 3

• Parameter setting instructions. The parameter settings consist of two parts: the pretrain config and other configs. To avoid any confusion arising from potential overlapping parameter names, we employ a hyphen (-) to specify the parameters of pretrain config and use a double hyphen (--) to specify the parameters of other configs. Please note that if two parameters have the same name, the settings of the latter can override those of the former.

4. Evaluating [Back to Top]

• Preparing Checkpoints of OpenCity. You can download our model using the following link: OpenCity-Plus, OpenCity-Base, OpenCity-Mini

• Running Evaluation of OpenCity. You can use our release model weights to evaluate, There is an example as below:

# Use OpenCity-plus to evaluate, please use only one dataset to test (e.g. dataset_use = ['PEMS07M'] in pretrain.config).
python Run.py -mode test -model OpenCity -load_pretrain_path OpenCity-plus.pth -batch_size 2 --embed_dim 512 --skip_dim 512 --enc_depth 6

• Running Evaluation of other baselines. You can Replace the model name or use ori mode to train and test. For example:

# Run STGCN in ori mode
python Run.py -mode ori -model STGCN -batch_size 64 --real_value False

Citation

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

@misc{li2024opencity,
      title={OpenCity: Open Spatio-Temporal Foundation Models for Traffic Prediction}, 
      author={Zhonghang Li and Long Xia and Lei Shi and Yong Xu and Dawei Yin and Chao Huang},
      year={2024},
      eprint={2408.10269},
      archivePrefix={arXiv}
}