Welcome to the exciting world of Spatio-Temporal Self-Supervised Learning (ST-SSL) in traffic prediction! In this blog, we will guide you step-by-step through the implementation of the ST-SSL model based on the research paper “Spatio-Temporal Self-Supervised Learning for Traffic Flow Prediction” by Ji et al., presented at AAAI 2023. This guide will make the process user-friendly while providing troubleshooting tips for a seamless experience.

Understanding ST-SSL: A Helpful Analogy

Imagine a city with various intersections, each acting like nodes on a massive network. Traffic flows through these intersections continuously, influenced by the roads connecting them, like nodes in a social network exchanging messages. In our analogy, X represents the last two hours of traffic data from these intersections (lookback_window), while Y communicates predictions for the next time step (predict_horizon). Each condition affects the other, painting a dynamic picture of traffic behavior much like conversations happening in a community, with all intersections aware of their neighbors through an adjacency matrix (adj_mx.npz). This allows us to forecast how traffic will evolve based on past patterns.

Requirements

Before diving into the implementation, ensure you have the following software setup:

• Python 3.8
• Numpy 1.21.2
• Pandas 1.3.5
• PyYAML 6.0
• Torch 1.10.1

Downloading Datasets

To train your model, you will need datasets from various sources such as NYCBike1, NYCBike2, NYCTaxi, and BJTaxi. Download them from the following locations:

• GitHub Repo
• Beihang Cloud Drive
• Google Drive

Each dataset comprises four files:

• train.npz – Training data
• val.npz – Validation data
• test.npz – Test data
• adj_mx.npz – Predefined graph structure

Model Training and Evaluation

Once you’ve set your environment and downloaded the datasets, it’s time to train your model. Use the following command to start training on the selected dataset (for example, NYCBike1):

bash
cd ST-SSL
runme 0 NYCBike1  # '0' specifies the GPU id, 'NYCBike1' indicates the dataset

Keep in mind that this repository only contains the NYCBike1 data for efficiency reasons.

Troubleshooting

If you encounter issues, consider the following tips:

• Ensure all required packages are installed and correspond to the specified versions.
• Verify that the dataset files are correctly placed in the appropriate folder.
• Check for any GPU compatibility issues if running on a CUDA-enabled device.

If you are still stuck, feel free to reach out for help. For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.