Investigating trends in traffic count data utilizing exploratory data analysis and regression analysis

Abstract

To decipher and understand data collected for any model or decision-making, a statistical investigation into a dataset is necessary to generate every unearthed information in the data through visual means or a summary of the dataset’s patterns. Statistical analysis is utilized to test: normality, randomness, outliers, the relationship between the dependent and the independent variables, and possible temporal and spatial spread in the dataset. In this study, the annual average daily traffic (AADT) dataset obtained from Montana, Minnesota, and Washington were subjected to statistical investigations using descriptive statistics, regression analysis, and hypothesis testing to understand the data patterns. The results generated from exploring the various techniques indicated that the data acquired were generally random. However, the data’s orderliness is not random. The data values had nearly no relationship with the collection locations, thus requiring a model that can generate a precise fit for better decision-making. Data skewness varied from slight to high. Therefore, data ranged from approximating normality and nonnormality with high outliers.