Driving too fast is one of the major causes that lead to road crashes. A new speed enforcement management method based on autonomous vehicle technologies has the potential to enhance the speed limit compliance and improve traffic safety. This paper investigates the performance of this method in the detection stage under different scenarios. 27 scenarios are generated using microscopic simulation in VISSIM to collect performance data of this method. Analysis of variance (ANOVA) is used to examine the performance difference between scenarios, including the detectable distance, the number of lanes, the speed of the measuring vehicle, the flow of the traffic, the desired average speed of the traffic, and the desired speed variance of the traffic. As a result, the influences of the factors on the performance of the method are distinguished. The detectable distance, the speed of the measuring vehicle and the flow of traffic have non-linear effects on the number of detected speeding vehicles. The measuring vehicle can interact with more speeding vehicles when the average speed of the traffic is high and the speed variance is small.