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

Jiangyi Fang, Liyue Chen, Di Chai, Yayao Hong, Xiuhuai Xie +2 more
6/7/2023
cs.LGcs.CV

Official code: uctb/UCTB (219 stars, MIT, last commit 2024-10-11).

Abstract

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.

View on arXivView PDF

Code Implementations(1)

uctb/UCTBOfficial100%
MIT

An Open Source Spatio-Temporal Prediction Package

21942Python, Jupyter NotebookFeb 3, 20192 years agoMIT

Similar Papers

Spatio-Temporal Contrastive Self-Supervised Learning for POI-level Crowd Flow Inference

85% match2023

A Universal Model for Human Mobility Prediction

84% match2024

A Data-driven Crowd Simulation Framework Integrating Physics-informed Machine Learning with Navigation Potential Fields

84% match2024

STContext: A Multifaceted Dataset for Developing Context-aware Spatio-temporal Crowd Mobility Prediction Models

83% match1 repo82025

Crowd Safety Manager: Towards Data-Driven Active Decision Support for Planning and Control of Crowd Events

83% match2023

PASTA: PArallel Spatio-Temporal Attention with spatial auto-correlation gating for fine-grained crowd flow prediction

82% match2023

Cite this paper

@article{fang2023uctb,
  title  = {UCTB: An Urban Computing Tool Box for Building Spatiotemporal Prediction Services},
  author = {Jiangyi Fang and Liyue Chen and Di Chai and Yayao Hong and Xiuhuai Xie and Longbiao Chen and Leye Wang},
  year   = {2023},
  eprint = {2306.04144},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2306.04144v2}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2306.04144.svg)](https://codeofpaper.com/paper/2306.04144)

Discussion