AbstractRamps are a critical component of an interchange, which seek maximum driver attention to satisfy the deceleration and acceleration requirements. To improve safety and articulate significant safety schemes, it is important to analyze driver behavior on ramp interchanges at a microscopic level. Existing studies on highway geometry are limited to horizontal curves, and only a few studies were found to deal with ramp interchanges. In this study, a model based on support vector regression (SVR) was developed to estimate lateral acceleration (La) experienced by the drivers on diagonal, loop, and semidirect ramps of service interchanges. To establish these models, the continuous lateral acceleration profiles for 83 drivers were collected using an instrumented vehicle. The developed SVR models exhibited higher accuracy, measured by the values of coefficient of determination and the root-mean square error. Further, a sensitivity analysis was performed to measure the relative importance of input features. The results revealed that ramp curvature and ramp length are the two most significant variables that impact lateral acceleration on diagonal and semidirect ramps. However, for loop ramp connectors, operating speed (V85) (85th percentile speed) displayed the highest association with lateral acceleration. The models developed in this study can be used as a tool to estimate the lateral acceleration experienced by the drivers during the design process, thus enhancing the further understanding of driver safety on ramp interchanges.