UCTB: An Urban Computing Tool Box for Building Spatiotemporal Prediction Services
Jiangyi Fang, Liyue Chen, Di Chai, Yayao Hong, Xiuhuai Xie, Longbiao, Chen, Leye Wang

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…
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Taxonomy
TopicsHuman Mobility and Location-Based Analysis · Traffic Prediction and Management Techniques · Data Management and Algorithms
