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An Open Source Spatio-Temporal Prediction Package

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UCTB (Urban Computing Tool Box)

Python PyPI https://img.shields.io/badge/license-MIT-green Documentation


Urban Computing Tool Box is a package providing ST paper list, urban datasets, spatial-temporal prediction models, and visualization tools for various urban computing tasks, such as traffic prediction, crowd flow prediction, ride-sharing demand prediction, etc.

UCTB is a flexible and open package. You can use the data we provided or use your data, and the data structure is well stated in the document.

News

2024-03: We have released two new datasets for Metro and Bus applications. These datasets provide hourly estimates of subway and bus ridership. Welcome to explore them!

2023-06: We have released a technical report entitled 'UCTB: An Urban Computing Tool Box for Spatiotemporal Crowd Flow Prediction', introducing the design and implementation principles of UCTB. [arXiv]

2021-11: Our paper on UCTB, entitled 'Exploring the Generalizability of Spatio-Temporal Traffic Prediction: Meta-Modeling and an Analytic Framework', has been accepted by IEEE TKDE! [IEEE Xplore] [arXiv]


ST-Paper List

We maintain a paper list focusing on spatio-temporal prediction papers from venues such as KDD, NeurIPS, AAAI, WWW, ICDE, IJCAI, WSDM, CIKM, and IEEE T-ITS. Note that the metadata may not encompass all relevant papers and could include unrelated ones, as selected by large language models.

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Urban Datasets

UCTB releases a public dataset repository including the following applications in 4 scenarios, with the detailed information provided in the table below. We are constantly working to release more datasets in the future.

Application City Time Span Interval Link
Bike-sharing NYC 2013.07.01-2017.09.30 5 & 60 mins 5 mins 60 mins
Bike-sharing Chicago 2013.07.01-2017.09.30 5 & 60 mins 5 mins 60 mins
Bike-sharing DC 2013.07.01-2017.09.30 5 & 60 mins 5 mins 60 mins
Bus NYC 2022.02.01-2024.01.13 60 mins 60 mins
Vehicle Speed LA 2012.03.01-2012.06.28 5 mins 5 mins
Vehicle Speed BAY 2017.01.01-2017.07.01 5 mins 5 mins
Pedestrian Count Melbourne 2021.01.01-2022.11.01 60 mins 60 mins
Ride-sharing Chicago (community) 2013.01.01-2018.01.01 15 mins 15 mins
Ride-sharing Chicago (census tract) 2013.01.01-2018.01.01 15 mins 15 mins
Ride-sharing NYC 2009.01.01-2023.06.01 5 mins 5 mins
Metro NYC 2022.02.01-2023.12.21 60 mins 60 mins

We provide detailed documents about how to use these datasets.


Prediction Models

Currently, the ST prediction model package supports the following models: (This toolbox is constructed based on some open-source repos. We appreciate these awesome implements. See more details).

Model Data Format Spatial Modeling Technique Graph Type Temporal Modeling Technique Temporal Knowledge Module
ARIMA Both N/A N/A SARIMA Closeness UCTB.model.ARIMA
HM Both N/A N/A N/A Closeness UCTB.model.HM
HMM Both N/A N/A HMM Closeness UCTB.model.HMM
XGBoost Both N/A N/A XGBoost Closeness UCTB.model.XGBoost
DeepST [SIGSPATIAL 2016] Grid CNN N/A CNN Closeness, Period, Trend UCTB.model.DeepST
ST-ResNet [AAAI 2017] Grid CNN N/A CNN Closeness, Period, Trend UCTB.model.ST_ResNet
DCRNN [ICLR 2018] Node GNN Prior (Sensor Network) RNN Closeness UCTB.model.DCRNN
GeoMAN [IJCAI 2018] Node Attention Prior (Sensor Networks) Attention+LSTM Closeness UCTB.model.GeoMAN
STGCN [IJCAI 2018] Node GNN Prior (Traffic Network) Gated CNN Closeness UCTB.model.STGCN
GraphWaveNet [IJCAI 2019] Node GNN Prior (Sensor Network) + Adaptive TCN Closeness UCTB.model.GraphWaveNet
ASTGCN [AAAI 2019] Node GNN+Attention Prior (Traffic Network) Attention Closeness, Period, Trend UCTB.model.ASTGCN
ST-MGCN [AAAI 2019] Node GNN Prior (Neighborhood, Functional similarity, Transportation connectivity) CGRNN Closeness UCTB.model.ST_MGCN
GMAN [AAAI 2020] Node Attention Prior (Road Network) Attention Closeness UCTB.model.GMAN
STSGCN [AAAI 2020] Node GNN+Attention Prior (Spatial Network) Attention Closeness UCTB.model.STSGCN
AGCRN [NeurIPS 2020] Node GNN Adaptive RNN Closeness UCTB.model.AGCRN
MTGNN [KDD 2020] Node GNN Adaptive TCN Closeness UCTB.model.MTGNN
STMeta [TKDE 2021] Node GNN Prior (Proximity, Functionality, Interaction/Same-line) LSTM/RNN Closeness, Period, Trend UCTB.model.STMeta

Visualization Tool

The Visualization tool integrates visualization, error localization, and error diagnosis. Specifically, it allows data to be uploaded and provides interactive visual charts to show model errors, combined with spatiotemporal knowledge for error diagnosis.

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Welcome to visit the website for a trial!

Installation

UCTB toolbox may not work successfully with the upgrade of some packages. We thus encourage you to use the specific version of packages to avoid unseen errors. To avoid potential conflict, we highly recommend you install UCTB vis Anaconda. The installation details are in our documents.

Citation

If UCTB is helpful for your work, please cite and star our project.

@article{uctb_2023,
  title={UCTB: An Urban Computing Tool Box for Spatiotemporal Crowd Flow Prediction},
  author={Chen, Liyue and Chai, Di and Wang, Leye},
  journal={arXiv preprint arXiv:2306.04144},
  year={2023}}

@article{STMeta,
  author={Wang, Leye and Chai, Di and Liu, Xuanzhe and Chen, Liyue and Chen, Kai},
  journal={IEEE Transactions on Knowledge and Data Engineering}, 
  title={Exploring the Generalizability of Spatio-Temporal Traffic Prediction: Meta-Modeling and an Analytic Framework}, 
  year={2023},
  volume={35},
  number={4},
  pages={3870-3884},
  doi={10.1109/TKDE.2021.3130762}}