Evaluation and Refinement of Minnesota Queue Warning Systems


John Hourdos, Jake Robbennolt

March 2023

Report no. MnDOT 2023-05

This study evaluates the first and a second implementations of the MN-QWARN queue warning algorithm developed by Hourdos et al. (1). This algorithm was developed to detect specific crash prone conditions created by traffic oscillations (shockwaves) on freeway systems. The MN-QWARN system was specifically calibrated for the freeway studied in Hourdos et al. (1) and was moved to a new location with minimal calibration. This evaluation found that the right-side model had a detection rate of 25% and a false alarm rate of 36%. The left-side model had a detection rate of 64% and a false alarm rate of 23%. We also note high over-warning rates on both lanes. Based on these findings, we recommend recalibrating the MN-QWARN algorithm at this location to examine improvements in performance.

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