arXiv 2306.041442024-06-09cs.LGcs.CV

UCTB：用于构建时空预测服务的城市计算工具箱

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

Hong Kong University of Science and Technology, Hong Kong（香港科技大学）；Fujian Key Laboratory of Sensing and Computing for Smart Cities, Xiamen（厦门福建省智慧城市感知与计算重点实验室）；School of Informatics, Xiamen University, Xiamen（厦门大学信息学院）；School of Computer Science, Peking University, Beijing（北京大学计算机学院）

Jiangyi Fang, Liyue Chen, Di Chai, Yayao Hong, Xiuhuai Xie, Longbiao Chen, Leye Wang

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AI总结：

本文提出城市计算工具箱UCTB，通过集成多种时空领域知识与先进模型，解决人群流量预测中领域知识利用困难和模型复现复杂的问题。

摘要：

时空人群流量预测是智慧城市中的关键技术之一。目前，困扰相关研究和从业者的主要痛点有两个。首先，人群流量与多个领域知识因素相关；然而，由于应用场景的多样性，后续工作难以合理、全面地利用领域知识。其次，随着深度学习技术的发展，相关技术的实现变得越来越复杂；复现先进模型已成为一项耗时且日益繁琐的任务。为解决这些问题，我们设计并实现了一个名为UCTB（Urban Computing Tool Box，城市计算工具箱）的时空人群流量预测工具箱，该工具箱同时集成了多种时空领域知识和最先进的模型。相关代码和支持文档已在 https://github.com/uctb/UCTB 开源。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。