The past couple of decades have witnessed an increasing amount of diversity in alternative intersection (AI) designs to handle an ever-increasing traffic demand. Therefore, the need for reliable analysis tools to assess current intersection operations and to predict future performance is of crucial importance. This paper assesses the accuracy of macroscopic and microscopic simulation tools to describe the operational performance of two AI designs (continuous flow intersections and offset T-intersections). Drones were employed to simultaneously capture the input and output variables to ensure that the observed outputs and performance metrics were produced by the observed inputs. Saturation flow rate, queues, and traffic signal timing data were considered as the primary model calibration data, and time-in-system, which consists of control delay at each intersection and extra travel time because of rerouting, was employed for model validation. Data collection results show that field-measured saturation flow rates at the two study sites were lower than their defaults in the Highway Capacity Manual guidance for signalized intersections. This will probably generate lower movement capacities than are currently being assumed in simulation modeling tools. Model validation results show that for the two AI designs, the tested analytical models tended to overstate the field travel times, especially for turning movements affected by upstream platooning, and particularly under high-demand conditions. This was mainly because of: (a) not accounting for initial and final queues; and (b) ignoring varying platoon flow rates at a downstream signal. By comparison, microsimulation performed better at the study sites.
Samandar et al. (2026) studied this question.