It is difficult to separate the effect of route topography from that of other test boundaries in real driving emission (RDE) tests. We proposed an artificial neural network (ANN) weight method to quantitatively evaluate the impact of route topography on RDE tests. Based on 37 256 data window samples of RDE tests in Chongqing, a factor analysis method was used to reduce data and eliminate information overlap between test boundaries. Additionally, a neural network model was also established to predict pollutant emissions and calculate the relative importance of input variables. The results show that route topography significantly affects CO₂ emissions, with its relative importance far exceeding that of other test boundaries. Moreover, the influence of the route topography cannot be ignored for CO, PN (particle number), and NO_x emissions, having an impact on vehicle driving emissions comparable to that of trip dynamics, especially under high-speed driving conditions. However, the existing regulatory emission standards seriously underestimate the impact of the route topography on vehicle driving emissions.