Ammonium-based protic ionic liquids (PILs) have shown promising physicochemical properties as proton conductors in polymer membrane fuel cells. In this work, molecular dynamics simulations are used to study the structural, dynamic, and transport properties of a series of tertiary ammonium triflate PILs. Nonpolarizable all-atom force fields were used, including two different models for the triflate anion. Previous simulation studies of these PILs have yielded poor results for transport properties due to unrealistically slow dynamics. To improve performance, polarization and charge transfer effects were approximately accounted for by scaling all atomic partial charges by a uniform factor, gamma. The optimum scaling factor gamma = 0.69 was determined by comparing simulation results with available experimental data and found to be transferable between different ammonium cations and insensitive to both the temperature and choice of experimental data used for comparison. Simulations performed using optimized charge scaling showed that the transport properties significantly improved over previous studies. Both the self-diffusion coefficients and viscosity were in good agreement with experiment over the whole range of systems and temperatures studied. Simulated PIL densities were also in good agreement with experiment, although the thermal expansivity was underestimated. Structural analysis revealed a strong directionality in interionic interactions. Triflate anions preferentially approach the ammonium cation so as to form strong hydrogen bonds between sulfonate oxygen atoms and the acidic ammonium hydrogen. The ionic conductivity was somewhat underestimated, especially at lower temperatures. Analysis of conductivity data suggests that there is significant correlated motion of oppositely charged ions in these PILs, especially at short times. These results overall indicate that the transport properties of this class of PILs are accurately modeled by these force fields if charge scaling is used and properly calibrated against selected experimental data.