关键词: 呼叫详细记录, 功能区, 机器学习, 城市感知, 高斯混合模型

Abstract: Urban function areas can be differentiated either by their external physical characteristics or by inherent social functions. And, they have been keeping in dynamic process over time. Remote sensing, as a typical traditional method in urban function area classification, has its critical defects such as high time cost and helpless in their social functions. In order to solve the problem, a new urban functional area identification method based on Call Detail Record (CDR) data was proposed. The application of this new data source in urban land use classification was verified as follow steps. First, communication station cells were labeled with five categories (residence area, office area, commercial area, college area, scenic-spot area). Second, call duration distribution features and move-frequency features, extracted from these five urban function areas were compared and analyzed. Finally, a weighted decision algorithm based on the Gaussian Mixture Model (GMM) was designed, and the simulation on the training set was conducted. The experimental results prove that the CDR data is capable of delivering useful information between different urban function areas. There are corresponding relationships between the nature of urban functional areas and the behavior characteristics of mobile phone users. When decision weight is 0.6, the weighted decision algorithm achieves 51.08% recall rate in current datasets. Combined with the error analysis, this work indicates the feasibility of CDR data in solving the problem of urban functional area identification.

Key words: Call Detail Record (CDR), function area, machine learning, urban sensing, Gaussian Mixture Model (GMM)