基于手机信令数据的快递人员辨识方法

    Identification of City Couriers Based on Mobile Phone Data

    • 摘要: 提出一种基于朴素贝叶斯分类法(naive Bayesian classifier,NBC)的城市快递人员辨识方法. 首先,通过相关问卷调查,研究快递派送人员的手机信令发生规则. 然后,依据北京市移动用户手机通信信令数据,利用问卷调查数据和手机信令数据2种数据源中同时包含的通信数据属性,建立通信数据与调查数据中类别变量(快递人员/非快递人员)之间的贝叶斯概率关系,以此为基础构建NBC模型并对其进行训练. 最后,使用未参与训练的样本数据测试标定后模型的准确性,测试结果显示快递人员的预测成功率达到88.3%. 结果表明:该方法具有较高的精度,可以满足实际应用需求.

       

      Abstract: An identification method of urban express based on the naive Bayesian classifier (NBC) was proposed in this paper. Firstly, the rules of express delivery personnel phone signaling was researched. And then, based on the mobile phone communication signaling data in Beijing, the Bayesian probabilistic relations were established between the interviewer’s category variable (couriers/non-couriers) and the bus travel information which were contained in both questionnaire and mobile phone signaling data. On the basis of this, the Bayesian model was constructed and its training was carried out. Finally, the accuracy of the calibrated model was tested by using the sample data which was not involved in training, and the test showed that the success rate of the courier prediction reached 88.3%. It is shown that the method has high accuracy, which can meet the demand of practical application.

       

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