Multiple Imputation Scheme for Overcoming the Missing Values and Variability Issues in ITS Data

Abstract

Traffic engineering studies such as validating Highway Capacity Manual (HCM) models require complete and reliable field data. However, the wealth of intelligent transportation systems (ITS) data is sometimes rendered useless for these purposes because of missing values in the data. Many imputation techniques have been developed in the past with virtually all of them imputing a single value for a missing datum. While this provides somewhat simple and fast estimates, it does not eliminate the possibility of producing biased results and it also fails to account for the uncertainty brought about by missing data. To overcome these limitations, a multiple imputation scheme is developed which provides multiple estimates for a missing value, simulating multiple draws from a population to estimate the unknown parameter. This paper also develops a framework of imputation which gives a broad perspective so that one can relate imputation methods to each other.