摘要
为了有效判别行驶车辆内车载乘客手机数量实际匹配的乘客人数,提出一种将聚类算法和呼叫指纹识别算法相组合的算法(CHC-CFA)。运用组合算法结合车辆内乘客携带手机的实时轨迹数据以及历史呼叫指纹数据建立同一用户识别模型,有效地判别出车辆内实际乘客人数,用于判别行驶车辆是否存在超员的异常问题,也可以对当前HOV车道的车辆内乘客数实时监测提供一种新的辅助检测方法。实验结果表明,该模型能有效判别行驶车辆内车载乘客手机数量实际匹配的乘客人数并有较高的检测准确率。
A combined algorithm CHC-CFA(condensed hierarchical clustering-call fingerprint algorithm)is proposed,which combines the clustering algorithm and call fingerprint recognition algorithm,so as to effectively identify the number of passengers actually matched with the number of mobile phones of passengers in the running vehicle.The same user identification model is established with the combined algorithm in combination of the real-time trajectory data and the historical call fingerprint data of the mobile phone carried by passengers in the vehicle.It can effectively identify the actual number of passengers inside the vehicle,which can be used to identify whether the running vehicle is overloaded,and also provide a new auxiliary detection method for the real-time monitoring of the number of passengers in the current HOV lane.The experiments show that the model can effectively identify the number of passengers actually matched with the number of mobile phones of passengers in a running vehicle,and has high detection accuracy.
作者
刘云翔
陈斌
林涛
施伟
LIU Yunxiang;CHEN Bin;LIN Tao;SHI Wei(School of Computer Science and Information Engineering,Shanghai Institute of Technology,Shanghai 201400,China;School of Automobile Engineering,Jiangsu Automobile Technician Institute,Yangzhou 225000,China)
出处
《现代电子技术》
北大核心
2020年第6期70-74,共5页
Modern Electronics Technique
基金
国家自然科学基金项目(61702334)
上海市自然科学基金项目(17ZR1429700)。
