Hybrid Intelligent Models for Autonomous Mobility and Traffic Prediction
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The rapid evolution of autonomous mobility and intelligent transportation systems (ITS) necessitates robust and accurate traffic prediction models. This chapter explores the application of hybrid intelligent systems to address the complexities of autonomous mobility and traffic forecasting. We propose a novel hybrid deep learning framework that integrates Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Convolutional Neural Networks (CNN) with an attention mechanism to enhance prediction accuracy. The proposed model is evaluated on a simulated traffic dataset, demonstrating superior performance compared to standalone LSTM and GRU models. The chapter provides a comprehensive overview of the methodology, from data preprocessing and feature engineering to model implementation and evaluation. The results and discussion section offers a detailed analysis of the model’s performance, highlighting the benefits of the hybrid approach in capturing complex temporal and spatial traffic patterns. The chapter concludes with a summary of the key findings and a discussion of future research directions in the field of intelligent transportation systems.
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Principles of Hybrid Intelligent Systems
