Calibration of Vehicle and Driver Characteristics in VISSIM and ANN- based Sensitivity Analysis

Abstract

Traffic- flow modeling using microsimulation approaches facilitates the study of bottlenecks and assists in the analysis of traffic- flow characteristics, the movement of individual vehicles, and in the study of vehicle and driver characteristics. The present study focuses on performing investigations on assessing the infuence of vehicle and driver characteristics on accurate prediction of traffic volumes in Mangalore city road network. The multi- stage frst- level of calibrations were performed starting with default values of vehicle and driver characteristics followed by testing of various combinations. The accuracy of predicting simulated volumes was measured using GEH- statistic. An ANN- based sensitivity analysis was performed to find the relative importance of vehicle and driver characteristics, which revealed that the average standstill distance, minimum look- ahead distance, and the desired speed: lower bounds for speed distributions were highly sensitive. The second- level of calibrations were performed by fne- tuning these three characteristics in three stages and the fnal VISSIM model was validated.