MAOTIN: A High-Accuracy and -Efficiency Network for Overloaded Truck Identification With GPS Trajectories

Overloaded truck transportation poses a significant threat to road safety. However, mainstream methods for overloaded truck identification suffer from low efficiency, limited monitoring range, and high costs, posing obstacles to curbing overloaded transportation. To tackle these challenges, this study proposes a novel approach called Multi-Aspect Overloaded Truck High-Precision Identification Network (MAOTIN), which achieves accurate identification by analyzing truck trajectory data comprehensively. MAOTIN comprises three efficient and specific modules: overloaded feature identification module (OFIM), trajectory similarity identification module (TSIM), and decision fusion module (DFM). Initially, the trajectory representation method was designed to transform the multidimensional spatiotemporal truck trajectory data into images, aggregating overload features while reducing dimensionality. Subsequently, OFIM and TSIM are developed to comprehensively identify overloaded behavior from typical overloaded features and overloaded trajectories similarity features. Finally, DFM adaptively and fully fuses feature images of different properties to obtain the ultimate identification result. To evaluate the performance of MAOTIN, this study conducted experiments on the constructed overloaded truck dataset. The results indicated that the model achieves an accuracy of 96.4% and 316 FPS, surpassing mainstream image classification networks. Moreover, MAOTIN performed a precision of 97.3% on practical application testing and assisted traffic police in apprehending 7591 overloaded trucks in one quarter, demonstrating its high practical value. Hence, MAOTIN enables accurate, rapid, and comprehensive identification of overloaded trucks and can be effectively applied to the overloaded identification tasks in urban traffic management.