计算机科学与探索 ›› 2022, Vol. 16 ›› Issue (4): 835-843.DOI: 10.3778/j.issn.1673-9418.2012072
收稿日期:
2020-12-04
修回日期:
2021-02-01
出版日期:
2022-04-01
发布日期:
2021-02-04
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。基金资助:
CHEN Liyue1,2, CHAI Di3, WANG Leye1,2,+()
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.Supported by:
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。
中图分类号:
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.
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.
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 |
表1 UCTB通用数据集格式
Table 1 Datasets format in UCTB
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 |
表2 UCTB中的数据处理接口
Table 2 Data processing interface in UCTB
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[ | √ | — |
HM | √ | — |
XGBoost[ | √ | — |
ST-ResNet[ | √ | √ |
DCRNN[ | √ | √ |
ST-MGCN[ | √ | √ |
STMeta[ | √ | √ |
表3 UCTB中集成的模型
Table 3 Implemented models in UCTB
模型 | 时间知识 | 空间知识 |
---|---|---|
ARIMA[ | √ | — |
HM | √ | — |
XGBoost[ | √ | — |
ST-ResNet[ | √ | √ |
DCRNN[ | √ | √ |
ST-MGCN[ | √ | √ |
STMeta[ | √ | √ |
高级模型层 | 模型层描述 |
---|---|
DCGRU[ | 同时建模时空关联 |
GCLSTM[ | 同时建模时空关联 |
GCL[ | 基于ChebNet谱域图卷积 |
GAL[ | 图注意层 |
表4 UCTB中的高级模型层
Table 4 High-level layers in UCTB
高级模型层 | 模型层描述 |
---|---|
DCGRU[ | 同时建模时空关联 |
GCLSTM[ | 同时建模时空关联 |
GCL[ | 基于ChebNet谱域图卷积 |
GAL[ | 图注意层 |
模块 | 接口 | 接口功能 |
---|---|---|
训练 | MiniBatchFeedDict | 分批训练 |
EarlyStopping | 早停机制 | |
评估 | RMSE/MAPE | 评估模型 |
表5 UCTB中的训练与评估接口
Table 5 Training and evaluating interface in UCTB
模块 | 接口 | 接口功能 |
---|---|---|
训练 | MiniBatchFeedDict | 分批训练 |
EarlyStopping | 早停机制 | |
评估 | RMSE/MAPE | 评估模型 |
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