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

TL;DR

The paper introduces UCTB, a comprehensive toolbox that integrates domain knowledge and advanced models for spatiotemporal crowd flow prediction in smart cities, addressing complexity and reproducibility issues.

Contribution

It presents a unified, open-source toolbox that combines multiple domain factors and models, facilitating research and application in urban spatiotemporal prediction.

Findings

Enhanced prediction accuracy with integrated models

Simplified implementation of complex deep learning techniques

Open-source code promotes reproducibility and collaboration

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.