1. Introduction¶
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.
1.1. Urban Datasts¶
UCTB also releases a public dataset repository including the following applications:
| Application | City | Time Range | Temporal Granularity | Dataset Link |
|---|---|---|---|---|
| Bike-sharing | NYC | 2013.07.01-2017.09.30 | 5 minutes | 66.0M |
| Bike-sharing | Chicago | 2013.07.01-2017.09.30 | 5 minutes | 30.2M |
| Bike-sharing | DC | 2013.07.01-2017.09.30 | 5 minutes | 31.0M |
| Pedestrian Count | Melbourne | 2021.01.01-2022.11.01 | 60 minutes | 1.18M |
| Vehicle Speed | LA | 2012.03.01-2012.06.28 | 5 minutes | 11.8M |
| Vehicle Speed | BAY | 2017.01.01-2017.07.01 | 5 minutes | 27.9M |
| Taxi Demand | Chicago | 2013.01.01-2018.01.01 | 15 minutes | 6.1M |
| Bus | NYC | 2022.02.01-2024.01.13 | 60 mins | 4.89M |
| Metro | NYC | 2022.02.01-2023.12.21 | 60 mins | 11.3M |
| Traffic Flow | Luzern | 2015.01.01-2016.01.01 | 3 mins | 21M |
| Ride-sharing | Chicago (community) | 2013.01.01-2018.01.01 | 15 mins | 6.06 |
| Ride-sharing | Chicago (census tract) | 2013.01.01-2018.01.01 | 15 mins | 10M |
| Ride-sharing | NYC | 2009.01.01-2023.06.01 | 5 mins |
We provide detailed documents about how to build and how to use these datasets.
1.2. Predictive Tool¶
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 |
1.3. 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!