Traffic prediction technology can often be daunting, but fear not! In this guide, we will walk you through the installation, setup, and running of models in LibCity, a remarkable library developed for urban traffic predictions. Get ready to turn complex data into insightful predictions with ease!

What is LibCity?

LibCity is a unified, comprehensive, and extensible library designed for researchers in the traffic prediction field. Built on the powerful PyTorch framework, LibCity offers a systematic pipeline encompassing all necessary steps for traffic prediction, thus contributing to standardization and reproducibility within the academic and urban planning domains.

Features of LibCity

• Unified: LibCity offers a systematic pipeline for implementing, using, and evaluating traffic prediction models.
• Comprehensive: The library reproduces 74 models covering 9 prediction tasks and collects 52 datasets for rigorous performance evaluation.
• Extensible: Users can easily customize components to develop new models, making LibCity a great choice for researchers looking to innovate.

Installing LibCity

To get started, you’ll need to install LibCity from the source code. Follow these steps:

git clone https://github.com/LibCityBigscity-LibCity
cd Bigscity-LibCity

For environmental configurations, refer to the Docs.

Quick-Start Guide

Before running models in LibCity, ensure you download at least one dataset and place it in the directory named .raw_data. You can find datasets on:

• BaiduDisk with code 1231
• Google Drive

Keep in mind that all datasets used in LibCity must be processed into the atomic files format.

Running a Model

The script run_model.py is your go-to for training and evaluating a model in LibCity. You’ll need to specify three parameters: task, dataset, and model. For example:

python run_model.py --task traffic_state_pred --model GRU --dataset METR_LA

This command runs the GRU model on the METR_LA dataset for traffic state prediction.

Troubleshooting

While using LibCity, you may encounter some issues. Here are a few troubleshooting ideas:

• If you cannot find your dataset, double-check its location in the .raw_data directory.
• Ensure that your environment meets all library dependencies outlined in the installation guide.
• If an error occurs during model execution, revisit the parameter specifications while running run_model.py.
• For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Visualizing with TensorBoard

To visualize the training progress, LibCity supports TensorBoard. After running a model, use the following command to visualize:

tensorboard --logdir libcity/cache

You can then view the results at http://localhost:6006.

Conclusion

LibCity not only makes traffic prediction simpler but also standardizes the process for researchers. Dive in, explore the extensive features, and enhance your projects!

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.