Traffic Flow Data Recovery Algorithm Based on Gray Residual GM(1, N) Model

Because of the basic data support and continuous motive force for the intelligent transportation systems (ITS), the quality of the raw traffic data detected from traffic sensors directly affect the follow-up benefits of the entire system. 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 to effectively improve the quality of traffic flow data. 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.

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，可以更好的解决因为数据故障而为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。