Forecasting long term highway staffing requirements for state transportation agencies.

摘要

The transportation system is vital to the nation's economic growth and stability, as it provides mobility for commuters while supporting the United States' ability to compete in an increasingly competitive global economy. State Transportation Agencies across the country continue to face many challenges to repair and enhance highway infrastructure to meet the rapid 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 staffing requirements. However, current methods in use cannot function without well-defined projects and therefore making long term forecasts is difficult.;This dissertation seeks to develop a dynamic model which captures the feedback mechanisms within the system that determines highway staffing 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, federal and state database 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.;By performing sensitivity simulations and statistical screening on possible drivers of the system behavior, the dynamic impacts of desired highway pavement performance level, availability of road fund and bridge fund on the required numbers of Engineers and Technicians throughout a 25-year simulation period were closely examined. Staffing strategies such as recruiting options (in-house vs. consultants) and hiring levels (entry level vs. senior level) were tested.;Finally the model was calibrated using input data specific to Kentucky to simulate an expected retirement wave and search for solutions to address temporary staffing shortage.