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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Effect of missing values estimations on traffic parameters


    Contributors:

    Published in:

    Publication date :

    2004-04-01




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




    Effect of missing values estimations on traffic parameters

    Sharma, Satish | Online Contents | 2004


    Estimation of missing values in traffic density maps

    Petrlik, Jiri / Korcek, Pavol / Fucik, Otto et al. | IEEE | 2012



    Matching Patterns for Updating Missing Values of Traffic Counts

    Zhong, Ming / Sharma, Satish / Lingras, Pawan | Taylor & Francis Verlag | 2006


    Lane-level traffic estimations using microscopic traffic variables

    Thajchayapong, S / Barria, J A / Garcia-Trevino, E | IEEE | 2010