mlsanityData to 7 Oct 2026

UCTB: An Urban Computing Tool Box for Building Spatiotemporal Prediction Services

arXiv 2306.04144 ↗First posted 7 Jun 2023cs.LG cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

Spatiotemporal crowd flow prediction is one of the key technologies in smart cities. Currently, there are two major pain points that plague related research and practitioners. Firstly, crowd flow is related to multiple domain knowledge factors; however, due to the diversity of application scenarios, it is difficult for subsequent work to make reasonable and comprehensive use of domain knowledge. Secondly, with the development of deep learning technology, the implementation of relevant techniques has become increasingly complex; reproducing advanced models has become a time-consuming and increasingly cumbersome task. To address these issues, we design and implement a spatiotemporal crowd flow prediction toolbox called UCTB (Urban Computing Tool Box), which integrates multiple spatiotemporal domain knowledge and state-of-the-art models simultaneously. The relevant code and supporting documents have been open-sourced at https://github.com/uctb/UCTB.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 25 of its 53 references that are in the map. First posted on arXiv 7 Jun 2023.

Builds on

25 of 53 references.

References · 25

  1. 2017Attention Is All You Needcited by 59,932
  2. 2017Graph Attention Networkscited by 8,854
  3. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
  4. 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,085
  5. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,125
  6. 2019Graph WaveNet for Deep Spatial-Temporal Graph Modelingcited by 820
  7. 2020Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networkscited by 563
  8. 2020Adaptive Graph Convolutional Recurrent Network for Traffic Forecastingcited by 525
  9. 2019GMAN: A Graph Multi-Attention Network for Traffic Predictioncited by 433
  10. 2016Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Predictioncited by 330
  11. 2021Spatial-Temporal Graph ODE Networks for Traffic Flow Forecastingcited by 209
  12. 2019Deep Learning for Spatio-Temporal Data Mining: A Surveycited by 171
13 more
  1. 2021Dynamic Graph Convolutional Recurrent Network for Traffic Prediction: Benchmark and Solutioncited by 156
  2. 2023Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Surveycited by 140
  3. 2022Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecastingcited by 122
  4. 2018Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Predictioncited by 119
  5. 2021Traffic Flow Forecasting with Spatial-Temporal Graph Diffusion Networkcited by 52
  6. 2022STDEN: Towards Physics-Guided Neural Networks for Traffic Flow Predictioncited by 45
  7. 2018Bike Flow Prediction with Multi-Graph Convolutional Networkscited by 42
  8. 2017Fluid Communities: A Competitive, Scalable and Diverse Community Detection Algorithmcited by 16
  9. 2016ModelHub: Towards Unified Data and Lifecycle Management for Deep Learningcited by 13
  10. 2020Exploring the Generalizability of Spatio-Temporal Traffic Prediction: Meta-Modeling and an Analytic Frameworkcited by 12
  11. 2023A Data-driven Region Generation Framework for Spatiotemporal Transportation Service Managementcited by 6
  12. 2022GridTuner: Reinvestigate Grid Size Selection for Spatiotemporal Prediction Models [Technical Report]cited by 4
  13. 2022Continuous Deep Learning: A Workflow to Bring Models into Productioncited by 1

Built on by

Not cited yet.