Buch, Englisch, 236 Seiten, Format (B × H): 156 mm x 234 mm
Transforming Mobility and Infrastructure
Buch, Englisch, 236 Seiten, Format (B × H): 156 mm x 234 mm
ISBN: 978-1-041-07186-0
Verlag: Taylor & Francis Ltd
Artificial Intelligence (A) is transforming transportation by enabling intelligent, adaptive, and data-driven mobility systems capable of addressing growing urbanization and sustainability challenges. By leveraging machine learning, computer vision, natural language processing, and advanced data analytics, AI enhances traffic prediction, autonomous navigation, safety monitoring, and infrastructure management. These technologies allow transportation networks to respond in real time to changing conditions, reduce congestion and emissions, improve road safety, and optimize resource utilization. AI also plays a central role in integrating vehicles, users, and infrastructure into cohesive smart mobility ecosystems. As transportation systems evolve toward automation, connectivity, and sustainability, AI emerges as a foundational enabler for resilient, efficient, and future-ready transportation networks worldwide.
The book offers a comprehensive and forward-looking examination of AI’s expanding role in modern transportation. The book spans foundational principles and advanced innovations, beginning with intelligent transportation systems and progressing to specialized areas such as AI-driven driving analytics, smart traffic control, and augmented-reality-based safety navigation. It further explores emerging frontiers including autonomous vehicle intelligence, edge-AI frameworks, 6G-enabled mobility, quantum computing, generative AI for infrastructure design, and robust vehicle-to-vehicle communication systems. By integrating theory, applications, and future trends, the book presents a holistic vision of how AI can build safer, smarter, and more sustainable transportation ecosystems while supporting the broader evolution of smart cities.
This book is designed for researchers, engineers, industry professionals, urban planners, policymakers, and advanced students engaged in transportation, artificial intelligence, and smart city development. It serves as both a practical reference and a visionary guide for those seeking to understand, design, and deploy AI-enabled transportation solutions, while supporting informed decision-making and innovation in global mobility systems.
Zielgruppe
Academic and Postgraduate
Autoren/Hrsg.
Fachgebiete
- Technische Wissenschaften Technik Allgemein Mess- und Automatisierungstechnik
- Mathematik | Informatik EDV | Informatik Technische Informatik
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
- Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion
- Mathematik | Informatik EDV | Informatik Daten / Datenbanken
- Mathematik | Informatik EDV | Informatik EDV & Informatik Allgemein Soziale und ethische Aspekte der EDV
Weitere Infos & Material
Preface. 1. Role of Artificial Intelligence in Transportation Systems. 2. Intelligent Transportation Systems: A Comprehensive Review of Technologies, Applications, and Future Trends. 3. Role of AI in Traffic Safety and Prevention of Accidents. 4. Artificial Intelligence for Measuring Transportation Comfort: A Comprehensive Review. 5. AI-powered Driving Analytics: Understanding Driver Behavior through Data. 6. Smart Vision: Powering Vehicle Detection with Deep Learning. 7. Quantum Computing and Communication for Intelligent Transportation System Networks: Exploring Qubit-based Innovations in Cryptography and Optimization Frontiers. 8. Generative AI-based Framework for Road Infrastructure Design in Intelligent Transportation Systems. 9. Neural Network–driven Driver Wellness Analytics: Predicting Mental Fatigue through Whole-Body Vibration Patterns and Psychosocial Health Monitoring. 10. AR and AI for Driver Assistance and Navigation. 11. The Invisible Highway: Integrating Artificial Intelligence with 6G Next-Generation Mobility. 12. Development of Machine Intelligence for Self-driving Vehicles Through Video Capturing. 13. Harnessing Edge AI for Real-Time Telematics: A Framework for Low-Latency Vehicle Data Analytics. 14. Quantum Machine Learning for Next-Gen Wireless: Fundamentals and Path Ahead. 15. AI Foundations for Reliable V2V Communication.




