Commercial heavy-duty trucks operate over long service lives and frequently change ownership through active secondary markets. While most transportation research focuses on new vehicle sales, limited attention has been given to how secondary markets influence fleet turnover and long-term technology adoption. This gap reduces the accuracy of models used to forecast fleet evolution, especially as new vehicle technologies enter the market. This project empirically characterizes primary and secondary truck markets and develops a simulation framework to examine how turnover dynamics shape technology diffusion. Using publicly available used truck listing data, the study will estimate how vehicle prices decline with age and mileage, how usage changes over time, and when trucks typically appear in the secondary market. These empirical patterns will be used to construct a fleet turnover model that represents vehicle aging, resale, and retirement. The model will simulate how a new vehicle technology introduced through new sales spreads through the fleet as trucks transition across ownership stages. The analysis will generate estimates of fleet composition over time under different assumptions about resale behavior, utilization, and adoption rates. By incorporating observed secondary market behavior, this research improves understanding of how commercial vehicle fleets change over time and supports more accurate forecasting for transportation planning and policy design.