Particulate matter (PM) emissions have become an increasingly noticeable concern for gasoline vehicles, especially for gasoline direct injection (GDI) one. The PM index (PMI) model developed by Aikiwa et al. provides an effective model linking the fuel properties to the PM emissions from gasoline vehicles. In this study, a more practical reduced PM index (RPMI) was developed based on the detailed analysis of 22 fuels with various physical properties and chemical compositions. Two approaches of statistical mathematics of correlation analysis and multivariate linear regression (MLR) were adopted in the modeling process. The RPMI involving two global fuel properties of T90 (distillation temperature of 90% by volume) and T70 (distillation temperature of 70% by volume) was tested to be statistically valid. In addition, the model was verified through the engine bench tests and the vehicle emissions tests. The results reveal that the RPMI showed good correlations to the engine-out particle number (PN) emissions under all of the typical test conditions, including PFI (port fuel injection) mode, GDI (gasoline direct injection) mode and compound injection (PFI + GDI) mode. In addition, the RPMI demonstrated a significantly high correlation to vehicle-out PN emissions over the New European Driving Cycle (NEDC) with the determination coefficient R-2 = 0.947. Moreover, a moderate correlation (R2 = 0.801) of the index to the filter PM mass emissions was observed. In the light of no additional components analysis is needed except for the legitimate tests of fuels, the RPMI has the potential to be a useful tool for original equipment manufacturers (OEMs) and environmental certification departments to expediently evaluate the fuel effect on PM emissions.