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Traffic microsimulation is regularly used to develop traffic signal control (TSC) algorithms, yet moving from simulation to the field requires confidence in the results and mature understanding of the uncertainty propagation. This work aims to understand the under-reported relationship between simulation inputs and the variance in output metrics commonly used for signal control optimization. Specifically, the impact that fleet composition, intelligent driver model, lane-changing model, and the corresponding inter-driver distribution of those parameters have on delay, fuel consumption, and travel time were ranked using Sobol global sensitivity analysis. The results are presented from 98,304 total simulation evaluations of a volume-calibrated three-intersection SUMO model. Acceleration, deceleration, and headway parameters in the intelligent driver model were shown to be important for fuel consumption, delay and travel time, in addition to the adherence of the drivers to the speed limit, the variance in preferred speed between drivers, and impatience. The results also unsurprisingly show the overwhelming importance of fleet composition for fuel consumption. Additionally, the work presents car-following and lane-change model parameter bounds that result in realistic simulation. The results from this study point to the critical factors that must be determined for any individual region of study and can be used to guide for real-world data collection to support additional calibration efforts. In the future, after calibration of key parameters, the overall uncertainty in network-wide performance metrics can be reduced and provide increased confidence to conclusions drawn from microsimulation studies.

A Global Sensitivity Analysis of Traffic Microsimulation Input Parameters on Performance Metrics