基于时空数据的城市人流移动模式挖掘

doi:10.11896/jsjkx.200100001

计算机科学 ›› 2020, Vol. 47 ›› Issue (10): 91-96.doi: 10.11896/jsjkx.200100001

所属专题：大数据&数据科学虚拟专题

• 数据库&大数据&数据科学 • 上一篇下一篇

基于时空数据的城市人流移动模式挖掘

孙天旭¹, 赵蕴龙^1,2, 练作为¹, 孙毅¹, 蔡月啸¹

1 南京航空航天大学计算机科学与技术学院南京211106
2 软件新技术与产业化协同创新中心南京210023

收稿日期:2020-01-02 修回日期:2020-05-16 出版日期:2020-10-15 发布日期:2020-10-16
通讯作者: 赵蕴龙(zhaoyunlong@nuaa.edu.cn)
作者简介:sun29@nuaa.edu.cn
基金资助:
国防基础科研计划资助(JCKY2016605B006);江苏省“六大人才高峰”高层次人才(XYDXXJS-031)

Mobility Pattern Mining for People Flow Based on Spatio-Temporal Data

SUN Tian-xu¹, ZHAO Yun-long^1,2, LIAN Zuo-wei¹, SUN Yi¹, CAI Yue-xiao¹

1 College of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China
2 Collaborative Innovation Center of Novel Software Technology and Industrialization,Nanjing 210023,China

Received:2020-01-02 Revised:2020-05-16 Online:2020-10-15 Published:2020-10-16
About author:SUN Tian-xu,born in 1995,M.S.candidate,is a member of China Computer Federation.His main research interests include urban computing and pervasive computing.
ZHAO Yun-long,born in 1975,Ph.D,professor,is a member of China Computer Federation.His main research interests include wireless network,collective computing,pervasive computing,data mining,wearable computing,etc.
Supported by:
National Defense Basic Scientific Research Program of China (JCKY2016605B006) and Six Talent Peaks Project in Jiangsu Province (XYDXXJS-031)

摘要/Abstract

摘要： 随着城市的快速发展,城市中人流的管理与移动模式挖掘变得越发重要。同时,随着以群智感知为代表的各种感知技术的发展,提出了智慧城市的概念,智慧城市中的大量感知数据为人流的分析提供了可能性。在智慧城市中,时空数据是最为常见的一种数据。本文基于城市中的时空数据,首先提出一种建模方法,将不同种类的时空数据表示为人流模型;然后基于聚类的思想,通过改进传统的基于密度的聚类算法来对人流的移动模式进行挖掘,提出一种人流的移动模式聚类算法:时空密度聚类(Spatio-Temporal Density-Based Spatial Clustering of Applications with Noise,ST-DBSCAN);接着设计了一个移动模式的交通应用场景,并提出对移动模式的评价方法;最后在中国某城市的真实数据集上进行实验与分析,结果表明本文得到的移动模式结果在统一交通服务的场景下可节省25%的交通成本,验证了本文所提移动模式的有效性。

关键词: 城市人流, 时空数据, 数据挖掘, 移动模式, 智慧城市

Abstract: With the accelerating urbanization of many countries,managing people flow and mining mobility patterns become more and more important.Simultaneously,with the development of information technology,especially mobile crowd sensing,the concept of smart city is proposed by many scholars,sensing data in smart cities also provides the possibility for analysis of people flow.In smart city,spatio-temporal data is the most common data.Based on the spatio-temporal data,this paper proposes a modeling method to represent different kinds of spatio-temporal data as people flow model.Then,based on the thinking of clustering,this paper mines mobility pattern from people flow by an improved density-based clustering algorithm,designs a transportation application in smart city,and proposes a method for evaluating the effectiveness of mobility pattern.Finally,experimenting on a real dataset of a city in China and analyzing the results.The results show that the mobility pattern obtained in this paper can reduce costs by 25% in the transportation application of smart city,verifying the effectiveness of the mobility pattern.

Key words: Data mining, Mobility pattern, People flow, Smart city, Spatio-Temporal data

中图分类号:

TP311.13

孙天旭, 赵蕴龙, 练作为, 孙毅, 蔡月啸. 基于时空数据的城市人流移动模式挖掘[J]. 计算机科学, 2020, 47(10): 91-96. https://doi.org/10.11896/jsjkx.200100001

SUN Tian-xu, ZHAO Yun-long, LIAN Zuo-wei, SUN Yi, CAI Yue-xiao. Mobility Pattern Mining for People Flow Based on Spatio-Temporal Data[J]. Computer Science, 2020, 47(10): 91-96. https://doi.org/10.11896/jsjkx.200100001

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基于时空数据的城市人流移动模式挖掘

Mobility Pattern Mining for People Flow Based on Spatio-Temporal Data

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