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Full Description
This book explores and highlights the latest techniques and applications of artificial intelligence (AI) and fuzzy logic in the development of intelligent transportation systems (ITS). It offers a detailed examination of how to leverage these technologies to solve complex transportation challenges, such as traffic congestion, route optimization, and autonomous vehicle navigation. Artificial Intelligence and Fuzzy Logic for Next Generation Intelligent Transportation employs several novel approaches and special design features. It includes case studies and real-world examples illustrating the practical applications of the technologies under review. Furthermore, it integrates interdisciplinary perspectives, combining insights from computer science, engineering, urban planning, and environmental science. Each chapter is self-contained, providing both theoretical background and practical insights, making it accessible to a broad audience. Additionally, this book features contributions from leading experts in the field, ensuring that the content is both current and relevant.
Presents advanced methodologies in AI and fuzzy logic, specifically designed to enhance ITSs
Offers a detailed examination of how technologies can be leveraged to solve complex transportation challenges, such as traffic congestion, route optimization, and autonomous vehicle navigation
Bridges the gap between theoretical research and practical implementation, demonstrating how emerging technologies can be applied to real-world problems
Includes theoretical foundations, algorithmic developments, and practical case studies specifically related to AI in the context of smart transportation
Discusses topics such as predictive weather impact on traffic management, enhancing road safety with AI and fuzzy logic, and smart parking systems in urban areas
It is written for researchers, engineers, and practitioners in the field, as well as policymakers and academic scholars interested in the future of transportation.
Contents
1. Intelligent transportation systems: Architectures, applications, and future directions. 2. Artificial intelligence and fuzzy logic for next-generation intelligent transportation systems. 3. AI algorithms in transportation: An overview. 4. Transforming traditional traffic systems: Applications of artificial intelligence, fuzzy logic, and IoT in next-generation transportation. 5. AI-enhanced traffic management: AI-driven traffic intelligence and cloud-based VCC (Vehicular Cloud Computing) for smarter mobility. 6. Enhancing public transportation efficiency: Integrating fuzzy logic and AI. 7. Traffic flow prediction and optimization. 8. An XGBoost-based method for predicting urban transport modes in the rabat area. 9. AI and fuzzy logic in traffic accident prediction and prevention. 10. Enhancing road safety through artificial intelligence and fuzzy logic-based intelligent systems. 11. The transformative impact of autonomous vehicles in urban transport: Robotaxis. 12. Autonomous vehicles and intelligent transportation. 13. Intelligent time-delay compensation in lateral control of autonomous vehicles using smith predictor and LMI techniques. 14. Advanced intelligent control strategies for enhanced lateral dynamics of autonomous vehicles. 15. Adaptive sliding mode control approach for lateral dynamics control of an autonomous vehicle. 16. Securing transportation systems: Integrating zero trust architecture and artificial intelligence through resource-based view and institutional theory. 17. Decarbonization pathways in transportation systems: A machine learning approach for developing countries. 18. Towards industry 5.0: Data-driven sustainable transportation for carbon footprint reduction.
