UCTB 0.3.5

Urban Computing ToolBox

UCTB (Urban Computing Tool Box)

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Urban Computing Tool Box is a package providing urban datasets, spatial-temporal prediction models, and visualization tools for various urban computing tasks, such as traffic prediction, crowd flow prediction, ridesharing 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 tutorial section.

News

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]

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]

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Urban Datasets

UCTB releases a public dataset repository including the following applications:

Application	City	Granularity	Download Link
Bike-sharing	NYC	5 minutes	66.0M
Bike-sharing	Chicago	5 minutes	30.2M
Bike-sharing	DC	5 minutes	31.0M
Pedestrian Count	Melbourne	60 minutes	9.44M
Vehicle Speed	LA	5 minutes	11.8M
Vehicle Speed	BAY	5 minutes	27.9M
Ride-sharing	Chicago	60 minutes	17.5M

We provide detailed documents about how to build and how to use these datasets.

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Prediction Models

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

Model Name	Input 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	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
STMeta [TKDE 2021]	Node	GNN	Prior(Proximity,Functionality,Interaction/Same-line)	LSTM/RNN	Closeness,Period,Trend	UCTB.model.STMeta

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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.

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 or use our docker environment. The installation details are in our documents.