Abstract
Urban mobility prediction is crucial for optimizing resource allocation, managing transportation systems, and planning urban development. We propose a novel framework, GeoTemporal LSTM (GT-LSTM), designed to address the intricate spatiotemporal dynamics of urban environments. GT-LSTM integrates temporal dependencies with geographic information through a multi-modal approach that combines attention mechanisms and Recurrent Neural Networks (RNNs). This method allows the model to focus on relevant spatial features while capturing sequential relationships in time-series data. The approach uses attention mechanisms to dynamically weight geographic features and LSTM layers to model temporal patterns, resulting in enhanced predictive accuracy. Evaluations using a real-world multi-modal urban transportation dataset demonstrate the performance of GT-LSTM, with significant reductions of 15% in Mean Absolute Percentage Error (MAPE) and 20% in Root Mean Square Error (RMSE) compared to traditional methods. The model also shows substantial improvements over traditional techniques, including Convolutional LSTM and Graph Convolutional Networks. The effectiveness of GT-LSTM in capturing both spatial and temporal dynamics highlights its potential for real-time urban mobility prediction and provides valuable insights for urban planners, policymakers, and transportation authorities to improve decision-making and system efficiency.