北京交通大学学报2018，Vol.42Issue(2)：31-37,7.DOI:10.11860/j.issn.1673-0291.2018.02.005
基于弱关联频繁模式的超限行为挖掘优化
An optimization for overload behavior mining based on weakly correlated frequent patterns
摘要
Abstract
At present,adopting game theory analysis and traffic prediction models to identify in advance overload vehicle in future has achieved certain detection effectiveness.However,it has the limitations on the spatiotemporal dynamics and migration of overload vehicle distribution mining.According to the characteristics of the overload vehicle data,this paper proposes an overload behavior mining optimization algorithm based on weakly correlated frequent patterns.In this algorithm,the spatial weakly correlated frequent pattern mining method is adopted to build the overload frequency pattern tree and the state transformation model of time weakly correlated.The predication value of frequency pattern is obtained.On the basis of FP-growth frequent pattern mining algorithm,this paper achieves the weak correlated between vehicle behavior data and overload behavior pattern mining.The error rate of overload behavior prediction algorithm is dropped to less than 6％ and the detection efficiency is improved effectively.关键词
频繁模式/行为挖掘/超限/交通安全Key words
frequent pattern/behavior mining/overload/traffic safety分类
信息技术与安全科学引用本文复制引用
万芳,胡东辉..基于弱关联频繁模式的超限行为挖掘优化[J].北京交通大学学报,2018,42(2):31-37,7.基金项目
国家自然科学基金(61272540)National Natural Science Foundation of China(61272540) （61272540）
