基于停留点和运动特征的交通模式并行检测算法（英文）

A parallel algorithm for detecting traffic patterns using stay point features and moving features

为了更加精确、快速地对城市中移动对象的交通模式进行检测,提出了利用停留点和运动特征进行交通模式检测的并行算法.首先,提取出不同交通模式中停留点特征,即分别对各种交通模式的停留点进行识别,并通过聚类算法挖掘不同交通模式特有的停留点特征.然后,提取出不同交通模式中的运动特征,主要包括最大速度、平均速度、停止率等.利用提取的停留点特征和运动特征构建分类器,以预测新轨迹的交通模式.最后,提出了基于Spark的并行算法进行交通模式检测.实验结果表明,停留点特征和运动特征能够更大程度地呈现出不同交通模式之间的区别,且检测精度高于其他方法.此外,并行算法可以提高交通模式的识别效率.

In order to detect the traffic pattern of moving objects in the city more accurately and quickly, a parallel algorithm for detecting traffic patterns using stay points and moving features is proposed. First, the features of the stay points in different traffic patterns are extracted, that is, the stay points of various traffic patterns are identified, respectively, and the clustering algorithm is used to mine the unique features of the stop points to different traffic patterns. Then, the moving features in different traffic patterns are extracted from a trajectory of a moving object, including the maximum speed, the average speed, and the stopping rate. A classifier is constructed to predict the traffic pattern of the trajectory using the stay points and moving features. Finally, a parallel algorithm based on Spark is proposed to detect traffic patterns. Experimental results show that the stay points and moving features can reflect the difference between different traffic modes to a greater extent, and the detection accuracy is higher than those of other methods. In addition, the parallel algorithm can increase the speed of identifying traffic patterns.