The primary objectives of this study were to determine patterns of traffic flow and develop traffic growth rates by traffic composition and highway type for Kentucky’s system of highways. Additional subtasks included the following: 1) a literature search to determine if there were new procedures being used to more accurately represent traffic growth rates, 2) development of a random sampling procedure for collecting traffic count data on local roads and streets, 3) prediction of vehicle miles traveled based on socioeconomic data, 4) development of a procedure for explaining the relationship and magnitude of traffic volumes on routes functionally classified as collectors and locals, and 5) development of county-level growth rates based on procedures to estimate or model trends in vehicle miles traveled and average daily traffic.
Results produced a random sampling procedure for traffic counting on local roads which were used as part of the effort to model traffic growth at the county level in Kentucky. Promising results were produced to minimize the level of effort required to estimate traffic volumes on local roads by development of a relationship between functionally classified collector roads and local roads. Both regression and logarithmic equations were also developed to explain the relationship between local and collector roads. County-level growth rates in traffic volumes were analyzed and linear regression was used to represent changes in ADT to produce county-level growth rates by functional class. Linear regression and Neural Networks models were developed in an effort to estimate interstate and non-interstate vehicle miles traveled
