东南大学学报（英文版）2019，Vol.35Issue(1)：22-29,8.DOI:10.3969/j.issn.1003-7985.2019.01.004
基于停留点和运动特征的交通模式并行检测算法
A parallel algorithm for detecting traffic patterns using stay point features and moving features
摘要
Abstract
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.关键词
交通模式检测/停留点/轨迹分类/轨迹挖掘并行化Key words
traffic patterns detection/stay point/trajectory classification/parallel mining of trajectory分类
信息技术与安全科学引用本文复制引用
吉根林,周星星,赵竹珺,赵斌..基于停留点和运动特征的交通模式并行检测算法[J].东南大学学报（英文版）,2019,35(1):22-29,8.基金项目
The National Natural Science Foundation of China (No.41471371) （No.41471371）
