区别多种出行方式的城市活动轨迹预测

信息社会中,基于用户的历史活动轨迹发掘和预测人类位置轨迹及活动规律至关重要.已有研究大多采用基于时间和轨迹间相似度分类的马尔可夫模型,忽略了不同出行方式下的移动规律差异.对此,区别不同出行方式,基于轨迹的速度、加速度和航向变化速度等特征,用XGBoost算法识别轨迹所对应的出行方式,并采用基于优化的轨迹分割算法,将人类出行轨迹按出行方式分解成多个轨迹,采用由不同出行方式轨迹建立的马尔可夫模型实现出行轨迹的精准预测.实验表明,不同出行方式的轨迹的移动规律存在显著差异,且所提出方法的预测精度和距离偏差明显优于几个基准方法.

Urban activity trajectory prediction with different travel modes

In the information society, it is very important to discover and predict human position trajectories and activity patterns based on users'' historical activity trajectories. Most of the existing studies use the Markov model based on the similarity classification between time and trajectory, ignoring the differences of movement laws under different travel modes. This paper distinguishes different travel modes, based on the characteristics of the trajectory''s speed, acceleration, and heading change speed, the XGBoost algorithm is used to identify the travel mode of trajectory, and the optimized trajectory segmentation algorithm is used to decompose the human travel trajectory into travel modes. Markov models trained by trajectories of different travel modes are used to accurately predict travel trajectories. Experiments show that there are significant differences in the trajectory movement laws of different travel modes, and the prediction accuracy and distance deviation of the proposed method are obviously better than several benchmark methods.