Forecasting construction staffing needs for state transportation agencies

State Transportation Agencies across the country continue to face many challenges to repair and enhance highway infrastructure to meet rapidly increasing transportation needs. One of these challenges is maintaining an adequate and efficient agency staff. In order to effectively plan for future staffing levels, State Transportation Agencies need a method for forecasting long term workforce requirements. However, current methods in use cannot function without well-defined projects therefore making long term forecasts is difficult. This work seeks to develop a dynamic model which captures the feedback mechanisms within the system that determines highway workforce requirements. The system dynamics modeling methodology was used to build the forecasting model. The formal model was based on dynamic hypotheses derived from literature review and interviews with transportation experts. Both 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 State Transportation Agencies' strategic plans, funding situations and workforce management strategies while determining future workforce requirements, and will hopefully fill the absence of long-term staffing level forecasting tools at State Transportation Agencies. The model was tested using standard system dynamics validation procedures, after which the model was calibrated using input data specific to the Kentucky Transportation Cabinet to simulate an expected retirement wave and search for solutions to address temporary staffing shortages.