Abstract: In recent years, the road transportation industry has witnessed significant advancements. While there have been improvements in vehicle transport efficiency and cargo capacity, there is a paramount need to enhance the safety management of transport vehicles by monitoring their routes and conditions in real time. Current vehicle terminal calibration systems exhibit significant positioning errors and sluggish information updates. This paper introduces a developed calibration system for satellite positioning of road transport vehicles. Through simultaneous measurement of displacement and speed data from both the calibration system and the vehicle terminal at varying speeds, the positioning discrepancies of the vehicle terminal were discerned. An innovative correction method, integrating genetic algorithms with a BP neural network, was proposed to rectify these errors. By comparing positioning data pre and post-correction, we observed maximum error reductions of 82.79%, 87.95%, and 89.55% respectively. Experimental outcomes affirm the efficacy of the BP neural network-based positioning error model, demonstrating substantial error correction capabilities.