Open AccessApproach to discovering companion patterns based on traffic data stream
Author(s) -
Zhu Meiling,
Liu Chen,
Han Yanbo
Publication year - 2018
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2018.5166
Subject(s) - platoon , tree traversal , computer science , tree (set theory) , object (grammar) , data mining , real time computing , data stream , artificial intelligence , control (management) , mathematics , algorithm , telecommunications , mathematical analysis
A companion of moving objects is an object group that move together in a period of time. Platoon companions are a generalised companion pattern, which describes a group of objects that move together for time segments, each with some minimum consecutive duration of time. This study proposes a method that can instantly discover platoon companions from a special kind of streaming traffic data, called automatic number plate recognition data. Compared to related approaches, the authors transform the companion discovery into a frequent sequence mining problem. The authors propose a data structure, platoon tree (PTree), to record discovered platoon companions. To reduce the cost of tree traversal during mining platoon companions, they utilise the last two together‐moving objects of a group to update PTree. Finally, a lot of experiments have been carried out to show the efficiency and effectiveness of the proposed approach.