Research On The Algorithm Of Vehicle Weight-in-Motion System

With the rapid development of the transportation industry, the vehicles overload is coming from bad to worse. The vehicles overload not only destroys the roads'structure and the infrastructural facilities, but also affects the normal transportation, destroys the driving order and the traffic seriously, and causes a large number of traffic accidents. The important means to effectively restrain overloading is to charge by weight, and the key technology of charging by weight is weigh-in-motion(WIM).At present, the WIM systems were studied on the base of wheel load and axle load, this paper discussed the way of axle load. The influence of the dynamic load on the weighing accuracy was analyzed. The process method based on the dynamic load separation was proposed. The paper used empirical mode decomposition(EMD) and nonlinear curve-fitting methods to separate the dynamic load contained in WIM signal.In the WIM system, vehicle speed and the weighing accuracy are often conflicting in order to solve this problem, we took different data processing methods according to the different vehicle speed. We used EMD method to process the data sequence when the vehicle speed was less than 16km/h.But the EMD method exists serious end effect. The paper presented mirror continuation method, second endpoint mirror continuation method and extream value translation method to restrain the end effect of the EMD method. We used nonlinear curve-fitting method to process the data sequence when the vehicle speed was greater than 16km/h.The experimental result manifested:The EMD method which used to process the data sequence when the vehicle speed is less than 16km/h reached the standard of ASTM E1398-94 specification type IV WIM system. And its weighing accuracy is in 4%.The nonlinear curve-fitting method which used to process the data sequence when the vehicle speed is greater than 16km/h reached of ASTM E 1398-94 specification type IV WIM system. And its weighing accuracy is in 4%.