Capitalizing on Drone Videos to Calibrate Simulation Models for Signalized Intersections and Roundabouts

Abstract

Simulation is an indispensable tool for the assessment of highway-related capital investments and operational changes. Model calibration, a challenging task in any simulation study, is a crucial step. The model’s robustness, accuracy, and quality are directly dependent on it. Many parameters exist, and field observations are often lacking to aid in their correct specification. Recently, videos from drones have created a uniquely powerful way to aid this process. Observations of the inputs (demand), outputs (vehicles processed), processing rates (e.g., saturation flow rates), and performance results (times in system, queue dynamics, and delays) are all available simultaneously. For signalized intersections, only the signal timing events are missing, and those data can be obtained from signal timing logs. This paper illustrates how modeling teams can use drone data to calibrate model parameters pertaining to intersection operation. It shows how saturation flow rates can be adjusted for signalized intersections so that queue dynamics and delays can be matched. For roundabouts, it illustrates how critical gaps and move-up times can be adjusted to match field observations of performance. Three real-world settings with associated drone data are used as case study examples.