Development of Improved Models for Imputing Missing Traffic Counts

Abstract

Estimating missing values is known as data imputation. A literature review indicates that many highway and transportation agencies in North America and Europe use various traditional methods to impute their traffic counts. These methods can be broadly categorized into factor and time series analysis approaches. However, little or no research has been conducted to assess the imputation accuracy. The literature indicates that the current practices are varied, and the methods used by highway agencies are intuitive in nature. Typical imputation methods used by highway agencies are identified and applied to data from six automatic traffic recorders (ATRs) in Alberta, Canada, to evaluate their accuracy. Statistical analysis shows that these traditional methods result in varying accuracy for traffic counts from different types of roads. In some cases, the imputation errors can be unacceptably high. Therefore, improved imputation methods are proposed. Study results indicate that imputation accuracy can be significantly improved by incorporating correction factors and data from both before and after the failure periods into the traditional models. The improved imputations should provide transportation practitioners better information for decision marking purposes.