Recognition and reduction of traffic flow redundant data
交通流冗余数据识别和约简方法

The detected data often appear redundant, which affects the actual application of traffic models. A method of recognizing and reducing redundant data was proposed. Redundant data were recognized based on rank-based weights and packet method. Firstly, each of traffic parameters was endowed with certain weight according to rank-based weights method. Secondly, in terms of group thought, large data sets were divided into many non-intersecting small data sets. Finally, redundant data were detected and eliminated in each small data set. To avoid missing, the above steps can be repeated. And the recognized redundant data were reduced by average method. An application example shows that, the proposed recognition method of redundant data has a good detection precision, the recall and the precision decreased with the threshold increasing, but still over 93%. The reduced data have a high fitting degree, up to 0.938. The results indicate that, the problem of single data source can be solved effectively.