Modeling long-term highway staffing requirements for state transportation agencies

State transportation agencies (STAs) across the country face many challenges in repairing and enhancing highway infrastructure to meet rapidly increasing transportation needs. One of these challenges is maintaining an adequate and efficient agency staff. To effectively plan for future staffing levels, STAs need a method for forecasting long-term staffing requirements. However, the methods currently in use cannot function without well-defined projects; therefore, making long-term forecasts is difficult. This paper seeks to develop a dynamic model that captures the feedback mechanisms within the system that determines highway staffing requirements. The system dynamics modeling method was used to build the forecasting model. The formal model was based on dynamic hypotheses derived from a literature review and interviews with transportation experts. Qualitative and quantitative data from literature and federal and state databases were used to support the values and equations in the model. The model integrates STAs’ strategic plans, funding situations, and staffing strategies and determines future staffing levels and will hopefully fill the absence of long-term forecasting tools at STAs. Standard system dynamics validation procedures were used to test the model, after which input data specific to the Kentucky Transportation Cabinet were used to calibrate the model and to simulate an expected retirement wave and search for solutions to address temporary staffing shortages.