Matching Patterns for Updating Missing Values of Traffic Counts

The presence of missing values is an important issue for traffic data programs. Previous studies indicate that a large percentage of permanent traffic counts (PTCs) from highway agencies have missing hourly volumes. These missing values make data analysis and usage difficult. A literature review of imputation practice and previous research reveals that simple factor and time series analysis models have been applied to estimate missing values for transport related data. However, no detailed statistical results are available for assessing imputation accuracy. In this study, typical traditional imputation models identified from practice and previous research are evaluated statistically based on data from an automatic traffic recorder (ATR) in Alberta, Canada. A new method based on a pattern matching technique is then proposed for estimating missing values. Study results show that the proposed models have superior levels of performance over traditional imputation models.