Missing traffic data imputation using a dual-stage error-corrected boosting regressor with uncertainty estimation

The missing data problem - attributed to malfunctioning detectors, packet loss during transmission, or data removed by quality control procedures - is unavoidable in most traffic-related datasets. However, this problem has adversely affected traffic engineering applications as they heavily rely on accurate and comprehensive data. This study aims to impute missing loop detector data in order to improve the estimation results of traffic flow analysis. This paper presents a statistically principled methodology that focuses not only on proposing a computationally efficient imputation approach, but also on assessing the uncertainty associated with imputed values. The proposed methodology quantifies the accuracy of imputation and estimation of uncertainty for a range of challenging patterns of missing loop detector data, and compares them with existing methods. The results of the analysis demonstrate that the performance of the proposed approach remains unaffected by the presence of a large number of missing patterns and reflects the true statistical properties of the principal data. The proposed approach is also comparatively less computationally complex than the existing methods. Further, the comparative analysis of the proposed estimator shows that the generated prediction intervals are reasonably accurate and conform to the desired confidence levels with relatively small interval width. (c) 2021 Elsevier Inc. All rights reserved.

Automated summary

This study proposes a statistically principled methodology to impute missing traffic data and improve the estimation results of traffic flow analysis. By quantifying the accuracy and uncertainty of imputation, comparing different missing data patterns, and evaluating performance against existing methods, the proposed approach shows robustness in handling missing data and estimating uncertainty.