Identification method of Fake Toll-free Vehicles Based on ASPC-DA Model
To build an efficient circulation network for agricultural goods,the"green channel"policy was proposed.However,there are some vehicles disguised as"green vehicles"try to passing through the highway to avoid paying the toll.In this paper,we consider historical data of normal toll-free vehicle data and fake toll-free vehicle data as case data set.Data pre-processing and aug-mentation are conduct at first.Then we pre-train the model in stage ASPC-DA-Ⅰ based on the PyTorch deep learning framework,the recognition rate of which is acceptable.Thirdly,in the ASPC-DA-Ⅱ stage,we introduce self-pace learning to fine-tune the ASPC-DA model,the recognition rate of which is 92.69％.The recognition rate of the ASPC-DA model is far better than the existing methods.Finally,the results of sensitivity experiment show that the best training sample size of ASPC-DA model is 70％of the total sample size,the best optimal iteration upper limit is from 90 to 100 and,the best optimal termination condition threshold is from 10 5 to 10-4.
transportation planning and managementfake toll-free vehiclesmachine learningself-pace learningdata augmen-tation
NETL
NSTL
万方数据
