计算机工程 ›› 2020, Vol. 46 ›› Issue (3): 292-298,308.doi: 10.19678/j.issn.1000-3428.0055439

• 开发研究与工程应用 • 上一篇    下一篇

基于群体行为分析的人群异常聚集预测方法

黄贺贺, 曾园园, 张毅, 奈何   

  1. 武汉大学 电子信息学院, 武汉 430072
  • 收稿日期:2019-07-10 修回日期:2019-08-28 发布日期:2019-09-09
  • 作者简介:黄贺贺(1995-),男,硕士研究生,主研方向为机器学习、数据挖掘;曾园园,副教授、博士;张毅,硕士;奈何,博士。
  • 基金项目:
    国家自然科学基金(61371126)。

Prediction Method of Abnormal Crowd AggregationBased on Group Behavior Analysis

HUANG Hehe, ZENG Yuanyuan, ZHANG Yi, NAI He   

  1. Electronic Information School, Wuhan University, Wuhan 430072, China
  • Received:2019-07-10 Revised:2019-08-28 Published:2019-09-09

摘要: 随着智能通信设备的普及和通信基站定位精度的提升,利用通信基站记录的用户行为数据监测和预测人群密度成为可能。由于人群异常聚集事件具有突发性,利用时间序列分析方法和概率模型进行预测的效果较差。针对该问题,提出一种基于群体行为分析的预测方法。通过分析聚集人群的上网行为和基站间的人群移动行为特征,得到两者之间的相关性,结合基站的人群密度时间序列信息,利用扩张因果卷积神经网络和逻辑回归模型得出预测结果。运营商提供的手机用户上网记录数据集上的实验结果表明,该预测方法的精确率为0.93,召回率为0.97,显著优于ARIMA算法、LSTM算法和XGBoost算法,证明了引入用户群体的上网行为和移动特征能够有效提升人群异常聚集预测的准确性。

关键词: 人群异常聚集, 移动互联网, 群体行为分析, 聚集预测, 卷积神经网络

Abstract: With the popularization of intelligent communication devices and the improvement of positioning accuracy of communication base stations,it becomes feasible to monitor and predict crowd density using the behavior data of users recorded by communication base stations.However,the prediction performance of the frequently methods using time series and probability models is reduced by the suddenness of crowd gathering events.To address the problem,this paper proposes a prediction method based on group behavior analysis.By analyzing the online behavior of crowds and the behavior features of crowds moving between base stations,their correlation is obtained.On this basis,in combination with the time series information of the crowd density of stations,the prediction result is obtained by using the expanded causal convolutional neural network and logistic regression model.Experimental results on the online behavior record dataset of mobile phone users provided by operators show that the accuracy of this prediction method is 0.93 and the recall rate is 0.97,which is significantly better than the ARIMA algorithm,LSTM algorithm and XGBoost algorithm,proving the introduction of online behavior and movement features of users can effectively improve the accuracy of abnormal crowd aggregation prediction.

Key words: abnormal crowd aggregation, mobile Internet, group behavior analysis, crowd prediction, Convolutional Neural Network(CNN)

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