微信服务号

微信订阅号

2025-4-3- 21
Home > Archive>Volume 28, Issue 6, 2017 >1498-1515. DOI:10.13328/j.cnki.jos.005220
PDF HTML XML Export Cite reminder
Approach to Discover Companion Pattern Based on ANPR Data Stream
Author:
Affiliation:

Fund Project:

National Natural Science Foundation of China (61672042); Program for Youth Backbone Individual, Beijing Municipal Party Committee Organization Department; Training Plan of Top Young Talent in North China University of Technology

  • Article
    • | |
  • Metrics
    • |
  • Reference
    • |
  • Related
    • |
  • Cited by
    • | |
  • Comments
    Abstract:

    Companion vehicle discovery is a newly emerging intelligent transportation application. Aiming at it, this paper redefines the Platoon companion pattern over a special type of spatio-temporal data stream, or ANPR (automatic number plate recognition data). Accordingly, a PlatoonFinder algorithm is also proposed to mine Platoon companions over ANPR data stream instantly. First, Platoon discovery problem is transformed into frequent sequence mining problem with customized spatio-temporal constraints. Compared to traditional frequent sequence mining algorithms, this new algorithm can effectively handle complex spatio-temporal relationships among sequence elements rather than their positions. Second, the new algorithm also integrates several optimization techniques such as pseudo projection to greatly improve the efficiency. It can efficiently deal with high speed and large scale ANPR data stream so as to instantly discover Platoon companions. Experiments show that the latency of the algorithm is significantly lower than classic frequent pattern mining algorithms including Apriori and Prefixspan. Furthermore, it is also lower than the minimum time interval between any two real ANPR data records. Hence, the proposed algorithm can discover Platoon companions effectively and efficiently.

    Reference
    Related
    Cited by
Get Citation

朱美玲,刘晨,王雄斌,韩燕波.基于车牌识别流数据的车辆伴随模式发现方法.软件学报,2017,28(6):1498-1515

Copy
Share
Article Metrics
  • Abstract:
  • PDF:
  • HTML:
  • Cited by:
History
  • Received:May 07,2016
  • Revised:July 15,2016
  • Online: February 21,2017
Links:Institute of Software, CASChina Computer FederationCASNSFC
You are the firstVisitors
Copyright: Institute of Software, Chinese Academy of Sciences Beijing ICP No. 05046678-4
Address：4# South Fourth Street, Zhong Guan Cun, Beijing 100190,Postal Code：100190
Phone：010-62562563 Fax：010-62562533 Email：jos@iscas.ac.cn
Technical Support：Beijing Qinyun Technology Development Co., Ltd.

Beijing Public Network Security No. 11040202500063