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

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。

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.