![](/_static/logo.png)

## Introduction

[HomePage](https://libcity.ai/)|[Docs](https://bigscity-libcity-docs.readthedocs.io/en/latest/index.html)|[Datasets](https://github.com/LibCity/Bigscity-LibCity-Datasets)|[Paper List](https://github.com/LibCity/Bigscity-LibCity-Paper)

LibCity is a unified, comprehensive, and extensible library, which provides researchers with a credible experimental tool and a convenient development framework in the traffic prediction field. Our library is implemented based on PyTorch and includes all the necessary steps or components related to traffic prediction into a systematic pipeline, allowing researchers to conduct comprehensive experiments. Our library will contribute to the standardization and reproducibility in the field of traffic prediction.

LibCity currently supports the following tasks:

* Traffic State Prediction
  * Traffic Flow Prediction
  * Traffic Speed Prediction
  * On-Demand Service Prediction
  * Origin-destination Matrix Prediction
  * Traffic Accidents Prediction
* Trajectory Next-Location Prediction
* Estimated Time of Arrival
* Map Matching
* Road Network Representation Learning

#### Features

* **Unified**: LibCity builds a systematic pipeline to implement, use and evaluate traffic prediction models in a unified platform. We design basic spatial-temporal data storage, unified model instantiation interfaces, and standardized evaluation procedure.
* **Comprehensive**: 60 models covering 9 traffic prediction tasks have been reproduced to form a comprehensive model warehouse. Meanwhile, LibCity collects 35 commonly used datasets of different sources and implements a series of commonly used evaluation metrics and strategies for performance evaluation. 

* **Extensible**: LibCity enables a modular design of different components, allowing users to flexibly insert customized components into the library. Therefore, new researchers can easily develop new models with the support of LibCity.

#### Overall Framework

![](/_static/framework.png)

* **Configuration Module**: Responsible for managing all the parameters involved in the framework.
* **Data Module**: Responsible for loading datasets and data preprocessing operations.
* **Model Module**: Responsible for initializing the reproduced baseline model or custom model.
* **Evaluation Module**: Responsible for evaluating model prediction results through multiple indicators.
* **Execution Module**: Responsible for model training and prediction.

#### Cite

Our paper is accepted by ACM SIGSPATIAL 2021. If you find LibCity useful for your research or development, please cite our [paper](https://dl.acm.org/doi/10.1145/3474717.3483923).

```
@inproceedings{10.1145/3474717.3483923,
  author = {Wang, Jingyuan and Jiang, Jiawei and Jiang, Wenjun and Li, Chao and Zhao, Wayne Xin},
  title = {LibCity: An Open Library for Traffic Prediction},
  year = {2021},
  isbn = {9781450386647},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/3474717.3483923},
  doi = {10.1145/3474717.3483923},
  booktitle = {Proceedings of the 29th International Conference on Advances in Geographic Information Systems},
  pages = {145–148},
  numpages = {4},
  keywords = {Spatial-temporal System, Reproducibility, Traffic Prediction},
  location = {Beijing, China},
  series = {SIGSPATIAL '21}
}
```

```
Jingyuan Wang, Jiawei Jiang, Wenjun Jiang, Chao Li, and Wayne Xin Zhao. 2021. LibCity: An Open Library for Traffic Prediction. In Proceedings of the 29th International Conference on Advances in Geographic Information Systems (SIGSPATIAL '21). Association for Computing Machinery, New York, NY, USA, 145–148. DOI:https://doi.org/10.1145/3474717.3483923
```

The LibCity is mainly developed and maintained by Beihang Interest Group on SmartCity ([BIGSCITY](https://www.bigcity.ai/)). The core developers of this library are [@aptx1231](https://github.com/aptx1231) and [@WenMellors](https://github.com/WenMellors). 

If you encounter a bug or have any suggestion, please contact us by [raising an issue](https://github.com/LibCity/Bigscity-LibCity/issues). You can also contact us by sending an email to bigscity@126.com.