空间区域中对象流动模式构建方法研究

随着时空数据获取设备的普及应用,产生了大量反映移动对象活动的位置数据,数据的海量性及分析的复杂性决定了该类数据为典型的大数据。位置数据中的到达和离开可以反映移动对象的流动规律,可以表示为区域的流动模式,本文研究空间区域中移动对象流动模式的构建方法,将区域的流动规律以时间序列进行定量表示,模式可用于指导交通、安全等方面的可预测调度。由于对象流动的随机性,使得构建高预测精度的模式成为一个挑战。提出一种基于层次聚类的流动模式构建模型,模型中通过数据的离散化、序列化、模式训练等步骤实现模式构建;提出偏斜度层次聚类树及异常序列去除方法,可以有效去除局部异常序列及自动聚簇选取,提高了模式的预测精度。利用真实数据集对所提出的模式训练方法进行了充分的实验,验证了所构建的空间区域中的流动模式可以用于表达区域中对象的流动规律,所提出的模式训练方法与现有的训练方法相比具有较高的预测精度。

A construction method of object flow patterns in spatial regions

With the popularization and application of spatio temporal data acquisition equipment, a large number of object positional data is created, which is typical big data.The positional data of departure and arrival can reflect the flow regularity of moving object, which can be expressed as a regional flow model. Moreover, regional flow models can be used to improve urban planning, intelligent transportation systems, etc.We analyze the methods for constructing object flow patterns in spatial regions. Due to the randomness of object moving, to find patterns with high prediction precision is a big challenge. We therefore propose a model for constructing object flow patterns including data discretization and serialization, pattern training and evaluation and so on, which can quantitatively represent the regional flow regularity as time sequences. We also present a new hierarchical clustering tree with skewness. Based on the skewness, we design a method for removing abnormal sequences and selecting the patterns automatically, which improves the prediction precisionof patterns. Experimental results on real datasets show that the proposed flow patterns can be used to express regional flow patterns, and the proposed pattern training method has higher prediction accuracy compared with the existing ones.