Using Grey Theory in the interpolation for missing value of detector study

• Main Authors: Hsu,Cheng-Yung, 許程詠
• Other Authors: Wang,Jin-Yuan
• Format: Others
• Language: zh-TW
• Published: 2011
• Online Access: http://ndltd.ncl.edu.tw/handle/75651255155927289425

碩士 === 國立交通大學 === 運輸科技與管理學系 === 99 === Purpose of this study to inform road users accurate estimates of travel time information, the most direct source of data collection for vehicle detectors (Vehicle Detector, VD), it returns the information to flow, speed and occupancy and so on. But often occurs in practice the information collected is not entirely the case, if the detector ignores the case of missing travel time prediction model will result in problems. In order to ensure the accuracy of their model estimates, the value of data loss need to be targeted for treatment, this study do not meet the statistical distribution of gray theory, the advantages of developing an effective value of the loss of data interpolation methods. In the empirical analysis on the National Highway No. 3 vehicle detectors for the object, using gray prediction method GM (1,1) and minimum cyclic residual correction method (Minimum Recursive Residual GM(1,1), MRRGM (1,1)) in different proportions and different missing data loss situations (arbitrarytime, peak time, off-peak time), the result of comparing the two interpolation algorithms. The results confirmed that a high proportion of loss when the number of long and multiple interpolation to MGGRM (1,1) the interpolation performance is better than GM (1,1) method.Interpolation mode than in the establishment of other interpolation theory is simple and very good interpolation performance.