Mining Discriminative Patterns for Classifying Trajectories on Road Networks

—Classification has been used for modeling many kinds of data sets, including sets of items, text documents, graphs, and networks. However, there is a lack of study on a new kind of data, trajectories on road networks. Modeling such data is useful with the emerging GPS and RFID technologies and is important for effective transportation and traffic planning. In this work, we study methods for classifying trajectories on road networks. By analyzing the behavior of trajectories on road networks, we observe that, in addition to the locations where vehicles have visited, the order of these visited locations is crucial for improving classification accuracy. Based on our analysis, we contend that (frequent) sequential patterns are good feature candidates since they preserve this order information. Furthermore, when mining sequential patterns, we propose to confine the length of sequential patterns to ensure high efficiency. Compared with closed sequential patterns, these partial (i.e., leng...

• Type: Journal
• Year: 2011
• Where: TKDE
• Authors: Jae-Gil Lee, Jiawei Han, Xiaolei Li, Hong Cheng
• Keywords: Road Networks, Sequential Patterns, TKDE 2011, Trajectories