Predicted Longitude and Latitude Information of the Four-Wheel Mobile Platform Using a Gated Recurrent Unit

Accurate prediction of user mobility patterns is essential for location-based services and intelligent transportation systems. In this study, we propose a sequence modeling framework that utilizes Gated Recurrent Units (GRUs) to predict future geographic coordinates (latitude and longitude) from user trajectory data stored in CSV format. By constructing input sequences of past GPS positions and training the GRU network to estimate the next position, we achieve robust trajectory forecasting performance. Experimental evaluation demonstrates that the GRU-based approach consistently yields higher prediction accuracy than the conventional Long Short-Term Memory (LSTM) model under the same conditions. The results highlight the effectiveness of GRUs in handling sequential spatial data with reduced computational complexity, suggesting their suitability for real-time and resource-constrained location prediction tasks. The models are evaluated on real-world GPS trajectory data consisting of over 800 sequential location samples, using distance-based metrics including MAE, RMSE, Average Displacement Error (ADE), and Final Displacement Error (FDE) to assess prediction accuracy in meters. This study proposes an enhanced GRU model, representing a key innovation and the main contribution of our work. The primary contribution of this study lies not merely in comparing GRU and LSTM models, but in proposing an enhanced GRU architecture that integrates motion features and an attention mechanism for improved GPS trajectory prediction. Unlike prior studies focusing solely on model comparison, our approach demonstrates methodological advancements through attention-based feature weighting and validated performance in real-world autonomous vehicle experiments.