摘要： 针对伴随车辆发现及其实时性问题,基于随时间变化的车牌识别流数据,提出一种采用并行频繁项集发现(PFID)技术的伴随车辆组实时发现算法。该算法根据频繁项挖掘Eclat算法的思想,并利用分布式流数据处理框架Spark Streaming生成最大伴随车辆组。实验结果表明,与排列组合算法及FP-Growth算法相比,PFID算法消耗内存更少,响应时间更短,在秒级响应时间内能找到伴随车辆组,达到及时预警目的。

关键词: 智能交通系统, 车牌自动识别流数据, 伴随车辆组, Spark Streaming并行框架, DStream模型, Eclat算法

Abstract: Aiming at the problem of accompanying vehicle discovery and its real-time performance,this paper proposes a real-time accompanying vehicle group discovery algorithm using Parallel Frequent Itemsets Discovery(PFID) technology based on license plate recognition data stream with time variation.The algorithm adopts the idea of Eclat algorithm for frequent items mining,and implements the generation of maximum accompanying vehicle groups by the distributed data stream processing framework named Spark Streaming.Experimental results show that compared with the Permutation and Combination(PM) algorithm and FP-Growth algorithm,the PFID algorithm consumes less memory and has faster response.The accompanying vehicle group is found within seconds of the response time,which achieves warning objective timely.

Key words: intelligent transportation system, Automatic Number Plate Recognition(ANPR) stream data, accompanying vehicle group, Spark Streaming parallel framework, DStream model, Eclat algorithm

中图分类号: 

• TP311