一种面向定点轨迹数据的行程识别方法

Travel Recognition Method for Fixed-Point Trajectory Data

为了对长周期定点轨迹数据进行行程识别,提出了一种基于动态阈值的定点轨迹数据行程识别方法.首先,采用聚类方法确定与阈值相关的时空多粒度参数;其次,根据参数对历史记录进行统计,计算参数对应的阈值;利用时空相关参数获取对应阈值,对轨迹进行分段,进而实现行程识别.基于真实的城市交通卡口数据的实验结果表明,使用时空相关的动态阈值方法对定点轨迹数据进行行程识别在准确率和覆盖率上都要优于传统基于固定和单一阈值的方法.

To satisfy the requirements of long-periodic fixed-point trajectory travel recognition,a dynamic threshold travel recognition method for fixed point trajectory data is proposed. At first,use hierarchical clustering to determine the spatial-temporal multiple granularity parameters which relate to the threshold.Then count historical records according to parameters to calculate the threshold corresponding to each parameter. Last,execute trajectory segmentation process with spatial-temporal threshold to get the precise travel recognition result. Experiment based on fixed-point trajectory data from real world city shows that using spatial-temporal dynamic threshold method to recognize travel in fixed point trajectory data is superior to the traditional stable and single threshold method on accuracy and coverage.