Updating Missing Values of Traffic Counts: Factor Approaches, Time Series Analysis versus Genetically Designed Regression and Neural Network Models Ming Zhong, Pawan Lingras, Satish Sharma ABSTRACT: The principle of Base Data Integrity addressed by both American Association of State Highway and Transportation Officials (AASHTO) and American Society for Testing and Materials (ASTM) recommends that missing values should not be imputed in the base data. However, updating missing values may be necessary in data analysis and helpful in establishing more cost-effective traffic data programs. The analyses applied to data sets from two highway agencies show that on average over 50% permanent traffic counts (PTCs) have missing values. It will be difficult to eliminate such a significant portion of data from the analysis. Literature review indicates that the limited research uses factor or autoregressive integrated moving average (ARIMA) models for predicting missing values. Factor-based models tend to be less accurate. ARIMA models only use the historical data. In this study, genetically designed neural network and regression models, factor models, and ARIMA models were developed to update pseudo-missing values of six PTCs from Alberta, Canada. Both short-term prediction models and the models based on data from before and after the failure were developed. Factor models were used as benchmark models. It was found that genetically designed regression models based on data from before and after the failure had the most accurate results. Average errors for refined models were lower than 1% and the 95th percentile errors were below 2% for counts with stable patterns. Even for counts with relatively unstable patterns, average errors were lower than 3% in most cases, and the 95th percentile errors were consistently below 9%. ARIMA models and genetically designed neural network models also showed superior performance than benchmark factor models. It is believed that the models proposed in this study would be helpful for highway agencies in their traffic data programs.