A hybrid deep learning model for human mobility prediction

Abstract Accurately modeling human mobility is essential for understanding traffic congestion, disease transmission, commodity flow and social dynamics. Traditional models such as the gravity model and intervening opportunity model provide foundational insights but often overlook fine-grained geographic factors. In this study, we propose a novel hybrid deep learning framework, FlowSynthNet (FSN) model, to improve the accuracy of human mobility prediction. Our model integrates one-dimensional convolutional neural networks, Transformer-based attention mechanisms, and LightGBM to capture local spatial features, global dependencies and non-linear interactions. Evaluated on mobility datasets from Beijing, New York and Los Angeles, FSN model consistently outperforms classical models and machine learning baselines. Furthermore, feature interpretation reveals how specific geographic elements influence mobility flows. Our results highlight the effectiveness of combining geographic semantics with deep learning for more precise and interpretable human mobility modeling.