In the development of technology for smart cities, the installation and deployment of electronic motor vehicle registration identification have attracted great attention in terms of smart transportation in recent years. Vehicle velocity measurement is one of the fundamental data collection efforts for motor vehicles. The velocity detection using electronic registration identification of motor vehicles is constrained by the detection algorithm, the material of the automobile windshield, the placement of the decals, the installation method of the signal reader, and the angle of the antenna. The software and hardware for electronic motor vehicle registration identification produced in the standard manner cannot meet the accuracy of velocity detection for all scenarios. Based on the actual application requirements, we propose a calibration method for the numerical output of the automobile velocity detector based on edge computing of the optimized multiple reader/writer velocity values and based on a particle swarm-optimized radial basis function (RBF) neural network. The proposed method was tested on a two-way eight-lane road, and the test results showed that it can effectively improve the accuracy of velocity detection using electronic registration identification of motor vehicles. Compared with the actual velocity, 87.12% of all the data samples had an error less than 5%, and 91.76% of the data samples for vehicles in the center lane had an error less than 5%. By calibrating the electronic vehicle velocity based on the registration identification, the accuracy of velocity detection in different application environments can be improved. Moreover, the method can establish an accurate foundation for application in traffic flow management, environmental protection, traffic congestion fee collection, and special vehicle traffic management.

This study is published on COMPLEXITY 2020(2020):1-13.