The assessment of the exposure to road traffic noise pollution and of associated health conditions is usually based on energy-average noise levels. However, the number of noise events to which an individual is exposed has proven essential to the prediction of annoyance and sleep disturbance. Unfortunately, no standard method has been adopted for the counting of noise events. To address this shortcoming, Brown and De Coensel designed, in 2018, a generalised algorithm for the detection of road traffic noise events. The authors evaluated the performance of this algorithm for multiple sets of input parameters, but the setup employed for this testing was simplistic. The present study thus aims to benchmark the proposed parameter sets for the noise event detection algorithm in a controlled but realistic environment, consisting of a calibrated microscopic traffic simulation in the entire city of Tartu, Estonia, which includes interrupted traffic conditions and urban infrastructure. The performance assessment of a parameter set is shown to be highly dependent on context, i.e., location and time of day, making definitive, universally applicable conclusions unrealistic. Rather, this study enables comprehensive insights that guide the selection of adapted parameter sets for various traffic situations, including the number of parameter sets, suitable detection thresholds, and recommended time gaps to implement.