Forecasting locations and magnitudes of pavement loading due to development activities

Forecasting future at-risk pavement locations can help prevent the expensive costs associated with rebuilding completely destroyed pavement. Optimizing the locations for preemptive maintenance results in better allocation of funds by cutting down total cost. Pavement maintenance makes up one of the largest expenditures for Departments of Transportation (DOTs) in most states, and historically over half of these funds have been spent on just Operations and Maintenance. Pavement lifespans depend on the design, maintenance, environmental influences, and traffic loading. Anticipating activities that cause increased truck loading can help prescribe preventative measures to reinforce pavement structures before they fail. This thesis focuses on developing an ArcGIS-based tool that forecasts at-risk pavement failure locations by characterizing heavy truck trips. This study focuses on two truck travel patterns: 1) urban growth truck patterns and 2) oil and gas industry truck patterns. Truck trip generation tables were linked with forecasted routes using ArcGIS. By forecasting the locations of truck routes and loadings, the Early Warning System (EWS) tool will help officials make better estimates on where unexpected damages will occur in order to give officials time to take action. This thesis includes two models: 1) Oil and Gas industry in Austin TxDOT district. And 2) Urban Growth in Williamson County. The results of the models are maps showing locations of greatest pavement damages due to unusual truck traffic loading.