基于用户行为模型的客流量分析与预测

为了预测无线城市接入中商圈的短时客流量,通过分析顾客购物行为模式,提出了一种基于停留时间和区间活跃度的身份识别方案,用于区分工作人员和顾客;采用二元线性回归方法对停留时间和活跃次数进行置信水平为95%的拟合,分析了不同拟合参数对预测的影响.实验结果表明:停留时间和活跃度用于区分身份信息合理有效,且在时间阈值为3小时,活跃度阈值为2次时,用小波神经网络预测效果最好.

Customer Traffic Analysis and Forecast Based on User's Behavior Model

In order to forecast the short-term customer flow in trading area under wireless access, through analyzing of customer shopping behaviors, this paper presents an identification scheme based on staying time and activeness to distinguish the staff and customers. We use the binary linear regression method to fit the data under confidence level of 95%, and analyze the influence of different parameters to predict. Experimental results show that the staying time and activeness are reasonable and effective to distinguish the identity information, when time threshold is 3 and activeness threshold is 2, the wavelet neural network prediction effect is best.