UCTB: Spatiotemporal Crowd Flow Prediction Toolbox

doi:10.3778/j.issn.1673-9418.2012072

Journal of Frontiers of Computer Science and Technology ›› 2022, Vol. 16 ›› Issue (4): 835-843.DOI: 10.3778/j.issn.1673-9418.2012072

• System Software and Software Engineering • Previous Articles Next Articles

UCTB: Spatiotemporal Crowd Flow Prediction Toolbox

CHEN Liyue¹^,², CHAI Di³, WANG Leye¹^,²^,⁺()

1. Key Lab of High Confidence Software Technologies (Peking University), Ministry of Education, Beijing 100871, China
2. Department of Computer Science and Technology, School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China
3. Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong 999077, China

Received:2020-12-04 Revised:2021-02-01 Online:2022-04-01 Published:2021-02-04
About author:CHEN Liyue, born in 1998, Ph.D. candidate. His research interests include ubiquitous computing and urban computing.
CHAI Di, born in 1995, Ph.D. candidate. His research interests include federated learning and privacy-preserving.
WANG Leye, born in 1987, Ph.D., assistant professor, Ph.D. supervisor. His research interests include ubiquitous computing, mobile crowdsensing and urban computing.
Supported by:
National Natural Science Foundation of China(61972008)

UCTB：时空人群流动预测工具箱

陈李越¹^,², 柴迪³, 王乐业¹^,²^,⁺()

1.高可信软件技术教育部重点实验室（北京大学）,北京 100871
2.北京大学信息科学技术学院计算机科学技术系,北京 100871
3.香港科技大学计算机科学与工程系,香港 999077

通讯作者: + E-mail: leyewang@pku.edu.cn
作者简介:陈李越（1998—）,男,湖北黄冈人,博士研究生,主要研究方向为普适计算、城市计算。
柴迪（1995—）,男,河北保定人,博士研究生,主要研究方向为联邦学习、隐私保护学习。
王乐业（1987—）,男,上海人,博士,助理教授,博士生导师,主要研究方向为普适计算、群智感知、城市计算。
基金资助:
国家自然科学基金(61972008)

Abstract

Abstract:

Spatiotemporal crowd flow prediction is one of the key technologies in smart cities. There are mainly two major shortcomings that plague researchers and developers. Firstly, crowd flow is affected by complex factors and previous studies have summarized a variety of spatiotemporal prior knowledge. However, it is difficult for follow-up work to comprehensively incorporate this prior knowledge as its application scenarios are diverse. Secondly, with the development of deep learning technology, implementing state-of-the-art models is cumbersome work and becomes more and more complicated. To fill in the above gaps, this paper designs time-series sampling interfaces and graph construction interfaces. The time series sampling interfaces can generate different types of time series based on diverse prior knowledge, and the graph construction interfaces can build different types of spatial graphs. Moreover, users can extend the above two interfaces to utilize new spatiotemporal prior knowledge. Based on the TensorFlow framework, this paper implements a variety of advanced spatiotemporal graph models and encapsulates the frequently-used spatiotemporal modeling units. Users can leverage state-of-the-art spatiotemporal models and perform customized development based on these advanced layers. In summary, the spatiotemporal crowd flow prediction tool box UCTB integrates diverse spatiotemporal prior knowledge and a variety of advanced models, which may promote the development of spatiotemporal crowd flow prediction applications. The codes and detailed documents are open-source. The URL of UCTB is https://github.com/uctb/UCTB.

Key words: urban computing, crowd flow, spatiotemporal prediction, open-source toolbox

摘要：

时空人群流动预测是智慧城市中的关键技术之一。目前主要有两大痛点困扰着相关研究、从业人员：第一,人群流动与多种因素相关,先前的研究总结出了多种时空先验知识,但由于人群流动预测应用场景的多样性,后续工作很难合理而全面地利用这些先验知识;第二,随着深度学习技术的发展,相关技术的实现越来越复杂,复现先进的模型是一件费时且愈发繁琐的事情。针对上述痛点,设计了时间序列采样接口和图构建接口,时间序列采样接口能够基于不同的先验知识产生不同类型的时间序列,图构建接口能够产生不同类型的空间图,上述两个接口还可通过继承接口实现自定义,以利用新的时空先验知识;基于TensorFlow框架实现了多种先进的时空图模型并封装了其中常用的时空建模单元,使用者不仅能够直接使用先进的时空模型,还能够基于这些高级模型层进行二次开发。综上,时空人群流动预测工具箱UCTB内同时集成了多种时空先验知识和多种先进的模型,对开发时空人群流动预测相关应用有着促进作用。相关的代码和配套文档均已开源,工具箱的网址是https://github.com/uctb/UCTB。

关键词: 城市计算, 人群流动, 时空预测, 开源工具箱

CLC Number:

TP311

CHEN Liyue, CHAI Di, WANG Leye. UCTB: Spatiotemporal Crowd Flow Prediction Toolbox[J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(4): 835-843.

陈李越, 柴迪, 王乐业. UCTB：时空人群流动预测工具箱[J]. 计算机科学与探索, 2022, 16(4): 835-843.

Figures/Tables 9

References 26

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键	值（含义）
TimeRange	数据的时间范围
TimeFitness	数据的时间粒度
Node	存储节点型数据
Grid	存储网格型数据
ExternalFeature	存储外部特征

键	值（含义）
TimeRange	数据的时间范围
TimeFitness	数据的时间粒度
Node	存储节点型数据
Grid	存储网格型数据
ExternalFeature	存储外部特征

接口名称	接口功能
GridTrafficLoader	读取并预处理网格型数据
NodeTrafficLoader	读取并预处理节点型数据
ST_MoveSample	以不同间隔采样时序数据
GraphGenerator	产生不同类型的图

接口名称	接口功能
GridTrafficLoader	读取并预处理网格型数据
NodeTrafficLoader	读取并预处理节点型数据
ST_MoveSample	以不同间隔采样时序数据
GraphGenerator	产生不同类型的图

模型	时间知识	空间知识
ARIMA^[2]	√	—
HM	√	—
XGBoost^[24]	√	—
ST-ResNet^[12]	√	√
DCRNN^[14]	√	√
ST-MGCN^[16]	√	√
STMeta^[25]	√	√

UCTB: Spatiotemporal Crowd Flow Prediction Toolbox

UCTB：时空人群流动预测工具箱

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Figures/Tables 9

References 26

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Metrics

高级模型层	模型层描述
DCGRU^[14]	同时建模时空关联
GCLSTM^[15]	同时建模时空关联
GCL^[23]	基于ChebNet谱域图卷积
GAL^[26]	图注意层

模块	接口	接口功能
训练	MiniBatchFeedDict	分批训练
训练	EarlyStopping	早停机制
评估	RMSE/MAPE	评估模型

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