A data-driven approach to calibrate microsimulation models based on the degree of saturation at signalized intersections

Abstract

Microscopic traffic simulation is considered as a reliable tool in transportation planning and management. Rational solutions from such simulations are contingent upon how well the simulation software is calibrated and validated to replicate real-world road network scenarios. Most of the existing calibration and validation efforts are normally based on the comparative analysis between the built-in attributes of VISSIM and the real-world scenarios using measures of effectiveness (MOEs). VISSIM attributes such as the volume-to-capacity ratios, vehicle delay, and queue lengths, are normally used during the validation process of signalized intersections. However, validating VISSIM based on a non-inbuilt attribute is yet to be explored. This paper proposes a step-by-step procedure for calibrating signalized intersections in VISSIM based on a measurable variable, which is the degree of saturation. The approach was applied to a case study of four signalized intersections in Miami, Florida. The methodology utilized real-world vehicle trajectory data to determine the optimal values of VISSIM car-following parameters required for calibration. Statistical results revealed that both the saturation headways obtained from VISSIM and the saturation headways observed in the field follow the same distribution. The results signify that VISSIM could be calibrated using a non-inbuilt attribute, and moreover generates accurate data compared to the field measurements.