Estimating Annual Average Daily Traffic for Local Roads for Highway Safety Analysis

Abstract

Annual average daily traffic (AADT) is a required input to the newly released SafetyAnalyst software application. Further, AADT is also required to calculate crash rates. Traditionally, AADTs are estimated by using a mix of permanent and temporary traffic counts collected in the field. Because field collection of traffic counts is expensive, it is usually performed only for major roads and only a small percentage of nonstate local roads have reliable AADT data. A method is presented to estimate AADTs for local roads by using the travel demand modeling method. A major component of the method involves a parcel-level trip generation model that estimates the trips generated by each parcel. The generated trips are then distributed to existing traffic count sites by using a parcel-level trip distribution gravity model. The all-or-nothing trip assignment method is then applied to assign the trips between the parcels and the traffic count sites onto the local roadway network to yield estimates of AADTs. The estimated AADTs were compared with those from an existing regression-based method using actual traffic counts from Broward County, Florida. The results show that the proposed method produces significantly lower mean absolute percentage errors.