To take the spatial distribution into account when identifying and processing single outlier continuous compaction quality

the near neighbor weighted estimation and identification method was developed based on autocorrelation distance. Judge index of single outlier was defined as abnormal index αi. After eliminating the outliers

the original outlier data were estimated using ordinary Kriging interpolation method. Then continuous compaction experiments were carried out in Loudi construction site of Shanghai-Kunming high-speed railway. Results show that the test value can be determined as a single outlier when the abnormal index αi is greater than 0.2. Compared with the current Pauta criterion recognition method

the near neighbor weighted estimation and identification method based on autocorrelation distance has higher accuracy and recognition efficiency. The ordinary Kriging interpolation method could provide a more accurate estimation of the single outlier data

reduce the coefficient variation of the data

and improve the uniformity of continuous compaction quality measured value.