A New Approach to Reduce the Position Data of Moving Objects Using Neural Network

ABSTRACT In this study, an ANN‐based Global Navigation Satellite System (GNSS) location data reduction approach was proposed. As GNSS location data becomes more common, efficient data reduction techniques are needed to reduce transmission, storage, and processing costs. This involves selecting key points from the original trajectory to maintain integrity, eliminating redundancy, and lowering transmission and storage expenses. In this study, we proposed a new method for reducing GNSS location data in both online and offline settings, utilizing an ANN trained with a mathematically generated dataset. ANN has not been used in data reduction in the literature. The approach involves training the ANN with a window size of 3 and a threshold value of 10°, followed by using the trained model for data reduction. Experimental results show that the ANN achieves a reduction rate of around 59.18% compared to the original trajectory. Notably, the ANN yields a significantly lower RMSE compared to a mathematical method, particularly in areas requiring precision. Despite the slightly slower computation times, the ANN remains suitable for real‐time applications, demonstrating its efficacy for GNSS location data reduction. Our study highlights its online capability, reasonable reduction rates, and low RMSE values, distinguishing it from existing literature. This method shows potential for scenarios where balancing reduction rates with data quality is crucial.