In the development of technology for smart cities, the installation and deployment of electronic motor vehicle registration\nidentification have attracted great attention in terms of smart transportation in recent years. Vehicle velocity measurement is one\nof the fundamental data collection efforts for motor vehicles. The velocity detection using electronic registration identification of\nmotor vehicles is constrained by the detection algorithm, the material of the automobile windshield, the placement of the decals,\nthe installation method of the signal reader, and the angle of the antenna. The software and hardware for electronic motor vehicle\nregistration identification produced in the standard manner cannot meet the accuracy of velocity detection for all scenarios. Based\non the actual application requirements, we propose a calibration method for the numerical output of the automobile velocity\ndetector based on edge computing of the optimized multiple reader/writer velocity values and based on a particle swarmoptimized\nradial basis function (RBF) neural network. The proposed method was tested on a two-way eight-lane road, and the test\nresults showed that it can effectively improve the accuracy of velocity detection using electronic registration identification of\nmotor vehicles.
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