Multi-representation Trajectory Clustering Method in Visualization of Road Traffic Trend

被引：0
|
作者：
Lu Q. [1 ,2 ]
Yang G. [1 ]
Tan J. [1 ]
Yu Y. [1 ,2 ]
Yuan X. [3 ]
机构：
[1] Visualization & Cooperative Computing, School of Computer and Information, Hefei University of Technology, Hefei
[2] Anhui Province Key Laboratory of Industry Safety and Emergency Technology, Hefei University of Technology, Hefei
[3] Department of Computer Science and Engineering, University of North Texas, Denton, 76201, TX
来源：
Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics | 2019年 / 31卷 / 07期
关键词：
Clustering; Multi-representative trajectories; Traffic trends; Vehicle trajectory;
D O I：
10.3724/SP.J.1089.2019.17306
中图分类号：
学科分类号：
摘要：
The vehicle trajectory data contains macroscopic information about the behavior of urban traffic and moving objects, from which valuable city traffic trends and vehicle behavior patterns and other information can be discovered. Analyzing trajectory data is important for traffic management. In response to the irregular vehicle trajectory data and the lack of an accurate description of group behaviors, this paper proposes a density-based trajectory clustering method. Our method partitions the trajectory data into segments according to angle and distance. The similarity of segments is measured with a new trajectory distance function. Representative trajectories are generated for the clustering results. Our experimental results based on three trajectory datasets demonstrate that the representative trajectories generated from our proposed method provide a better description for the overall trend of traffics, which could better serve the traffic management. © 2019, Beijing China Science Journal Publishing Co. Ltd. All right reserved.
引用
收藏
页码：1194 / 1202
页数：8
相关论文
共 16 条
  • [1] Ye Y.Q., Zuo Z.J., Yuan X.H., Et al., Geographically weighted regression model for urban traffic black-spot analysis, Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, pp. 4866-4869, (2017)
  • [2] Andrienko N., Andrienko G., Visual analytics of movement: an overview of methods, tools and procedures, Information Visualization, 12, 1, pp. 3-24, (2013)
  • [3] Han J.W., Pei J., Kamber M., Data Mining: Concepts and Techniques, (2011)
  • [4] Ester M., Kriegel H.P., Sander J., Et al., A density-based algorithm for discovering clusters in large spatial databases with noise, Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, pp. 226-231, (1996)
  • [5] Lee J.G., Han J.W., Whang K.Y., Trajectory clustering: a partition-and-group framework, Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 593-604, (2007)
  • [6] Gao X., Yu F.S., Trajectory clustering using a new distance based on minimum convex hull, Proceedings of the 17th Joint World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems, pp. 1-6, (2017)
  • [7] Ankerst M., Breunig M.M., Kriegel H.P., Et al., OPTICS: ordering points to identify the clustering structure, Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 49-60, (1999)
  • [8] Hong Z.H., Chen Y., Mahmassani H.S., Recognizing network trip patterns using a spatio-temporal vehicle trajectory clustering algorithm, IEEE Transactions on Intelligent Transportation Systems, 19, 8, pp. 2548-2557, (2017)
  • [9] Zhao L.B., Shi G.Y., Yang J.X., An adaptive hierarchical cluster- ing method for ship trajectory data based on DBSCAN algorithm, Proceedings of the 2nd IEEE International Conference on Big Data Analysis, pp. 329-336, (2017)
  • [10] Andrienko G., Andrienko N., Fuchs G., Et al., Clustering trajectories by relevant parts for air traffic analysis, IEEE Transactions on Visualization and Computer Graphics, 24, 1, pp. 34-44, (2018)
12