Enhanced deep learning approach for high-accuracy mobility coordinate prediction

Abstract

Accurate prediction of mobility coordinates (x and y) is essential for effective transportation planning, urban development, and mobile network optimization. This study presents Tri-Sequence Temporal Network (TriSeqNet), an innovative architecture that synergizes the capabilities of bidirectional long short-term memory (BiLSTM), residual gated recurrent units (Residual GRU), and temporal convolutional networks (TCN) to concurrently predict x and y coordinates. Our approach outperforms existing methods by leveraging the combined strengths of these advanced neural network models. The performance of TriSeqNet is evaluated using traditional metrics such as mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE), as well as the coefficient of determination (R²) and explained variance (EV). This comprehensive evaluation framework demonstrates the robustness and accuracy of the proposed model in various predictive scenarios.