手机信令的时空密度轨迹点识别算法

手机信令具有时空序列性以及数据量大、采样频率不均、定位精度低与基站振荡等特点,导致传统手机信令聚类方法数据密度分布不均、时空开销大且聚类效果差.提出一种用于手机信令的时空密度轨迹点识别算法.将手机信令数据网格化以统一评估尺度,根据振荡噪声特征对网格簇进行时空联结减少空间不确定性和计算量,结合网络轨迹的曲折性以及移动与停...

Recognition Algorithm for Space-Time Density Track Points of Celluar Signaling

Celluar signaling is spatiotemporal sequential,and has some features such as large amount of data,uneven sampling frequency,low positioning accuracy and base station oscillation,which lead to the uneven distribution of data density,large space-time overhead and poor clustering effect of traditional Celluar signaling clustering methods.To address the problem,this paper proposes an algorithm to recognize the track points of space-time density of celluar signaling.The celluar signaling data is gridded to unify the evaluation scale.According to the characteristics of oscillatory noise,the grid clusters are spatiotemporal connected to reduce the spatial uncertainty and the amount of computation.Combined with the tortuosity of the network trajectory and the movement and residence time,the spatiotemporal movement ability of the trajectory points in the grid cluster is redefined.The spatiotemporal density of the grid cluster is calculated to judge the user's residence area,and the mobile residence tags Trajectory data are collected to verify the effectiveness and recognition efficiency of the algorithm.Experimental results show that the recognition accuracy of this algorithm is higher than that of the improved DBSCAN algorithm,which is suitable for identifying the residence area of celluar signaling data,and the recognition effect of complex trajectory residence area is better.