摘要
交通检测器采集的原始交通数据的质量会直接影响智能交通系统的后续效益.本文针对采集的交通数据普遍存在的故障问题,以交叉口检测器的交通流数据为研究对象,提出基于灰色残差GM(1,N)模型的数据修复算法.首先针对交叉口四个路口的交通流进行灰色相关分析,然后建立灰色GM(1,N)模型对故障数据进行预测修复,并进行了残差修正,提高了修复数据的精度.分析结果表明,提出的故障数据灰色残差GM(1,N)模型算法是可行的,可以更好地解决因为数据故障而对后续处理带来的困难,同时也为其他领域的故障数据修复提供借鉴.
The quality of the raw traffic data detected from traffic sensors directly affect the follow-up benefits of the the intelligent transportation systems. In view of the widespread failure problems of collected traffic data, the paper takes the traffic flow data of intersection detector as the research object. A traffic flow data recovery algorithm based gray residual GM （1, N） model is proposed. First, the grey relational analysis is conducted on the traffic flow of four links at an intersection. Then a grey model GM （ 1, N） is developed for the estimated recovery of failure data. The residual modification is used to improve the accuracy of the repaired data. The results indicate that the proposed traffic flow data recovery algorithm is feasible. It is able to solve the post-processing difficulties due to data failure and it serves as a good method for failure data recovery in other areas as well.
出处
《交通运输系统工程与信息》
EI
CSCD
北大核心
2012年第1期42-47,共6页
Journal of Transportation Systems Engineering and Information Technology
基金
北京市自然科学基金(4102038)
国家自然科学基金(61174181)
关键词
智能交通
数据修复
GM(1
N)模型
交叉口
残差修正
intelligent transportation systems （ITS）
data recovery
GM （ 1, N） model
intersection
residual modification
