Automatic identification of violations in driver training based on geofence and geospatial analysis

To further improve the regulatory efficiency of the driver training industry and promote the development of “Internet + supervision” in the driver training industry, an off-site supervision method for the driver training industry based on geofence technology and geospatial analysis methods is studied. This method aims to automatically identify and comprehensively supervise whether training vehicles operate in accordance with specified routes and times. Through spatiotemporal matching and spatial mapping of multi-source heterogeneous data such as trajectory data of training vehicles from driving training institutions and geofence data, a multi-source dataset for industry supervision is established. Using the Shapely geospatial analysis library, based on the DE-9IM model, and combined with the multi-source data infrastructure, real-time supervision of training vehicles and automatic identification of violations are realized. The results show that the off-site supervision method proposed in this study can achieve precise supervision of the driving training industry, with a supervision accuracy rate as high as 99.87%. The identification results can serve as an important basis for relevant industry regulatory and law enforcement departments to carry out off-site supervision and early warning in the industry, and promote the intelligent transformation of off-site supervision in the driving training industry.