Deep Learning and Quaternion Optimization for Sensor-Driven Traffic Management in MANET-Based Intelligent Transportation

Abstract

The increasing complexity of intelligent transportation systems (ITS) requires advanced traffic and time control optimization techniques capable of handling dynamic, large-scale, and real-time environments. This paper presents a comprehensive review of deep learning and optimization approaches in sensor-driven transmission control systems integrated with Mobile Ad Hoc Networks (MANET), Quaternion neural networks, Generative Adversarial Networks (GANs), and bio-inspired Kookaburra optimization algorithms. Sensor-driven systems enable real-time data collection, while MANET facilitates decentralized communication among vehicles and infrastructure. Recent studies (2020–2023) highlight the effectiveness of deep learning models such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Graph Neural Networks (GNNs) in capturing complex spatiotemporal traffic patterns. GNN-based approaches, in particular, have emerged as state-of-the-art solutions for traffic forecasting due to their ability to model graph-structured road networks . Furthermore, hybrid models combining GNNs with optimization and reinforcement learning demonstrate improved traffic prediction accuracy and system adaptability . Quaternion neural networks enhance multidimensional feature representation, while GANs improve robustness by generating synthetic traffic scenarios. Kookaburra optimization provides efficient convergence and resource allocation. This review analyzes these approaches, compares their performance, and identifies research gaps for future intelligent traffic systems.