Effect of missing values estimations on traffic parameters

Estimating missing values is known as data imputation. The proper imputation of missing values of permanent traffic counts (PTCs) could result in significant cost savings for highway agencies in their traffic data programs. However, little research has been done on missing values and only limited research has used factor or time series analysis models for predicting them. Moreover, studies of the effect of the imputations on traffic parameters estimations are not available. This study used factor models, genetically designed neural network and regression models, and autoregressive integrated moving average (ARIMA) models to update pseudo‐missing values of six PTCs from Alberta, Canada. The influences of these imputations on the estimations of annual average daily traffic (AADT) and design hourly volume (DHV) were studied. It was found that simple models usually resulted in large AADT and DHV estimation errors. As models were refined, resulting estimations for individual missing hourly volumes significantly improved. Usually these models provided highly accurate AADT and DHV estimations.

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