Screening of Illegal Vehicles on Highways Based on LightGBM + MLP Research

The safety of road transportation has been significantly impacted by the frequent use of illegal vehicles. This paper attempts to solve the investigation problems involved in the illegal operation crackdown. With the highway traffic flow data, this paper identifies practical business features of illegal vehicles and conducts in-depth mining of illegal behavior to extract more fine-grained feature information. Subsequently, machine learning LightGBM and deep learning MLP are used to construct the recognition model of illegal vehicles, and the five-fold cross-validation training method is used to ensure the stability of the model. Considering the large differences between the two models, based on the rank method, a fusion model is developed to improve model accuracy. This study contributes to uncovering the features of illegal vehicles and assisting the traffic police in protecting the legitimate rights of the people.