Applied Computing for Intelligent Transportation Systems

Erscheinungsjahr 2027
ISBN: 978-0-443-33378-1
Verlag: Elsevier Science

Buch, Englisch, 290 Seiten, Format (B × H): 152 mm x 229 mm, Gewicht: 449 g

Applied Computing for Intelligent Transportation Systems delves into the dynamic intersection where computer science meets transportation engineering. Its comprehensive coverage of cutting-edge ITS technologies offers sought-after research insights into the most effective integration strategies achievable through applied computing-informed methodologies.

This timely, interdisciplinary exploration of the ever-progressing solutions that are contributing to the transformation of transportation systems is accompanied by sustainability-minded considerations, which are at the forefront of road- and transit-based best practices worldwide. The volume also includes practical case studies and real-world examples, showcasing success stories and lessons learnt that facilitate understanding of the feasibility of implementation in smart-city projects.

With its aim of helping to bridge the gap between the theory and practice of enhancing ITS and the infrastructure that supports them, the book proves to be a valuable resource for readers in academia as well as industry.

Autoren/Hrsg.

George, Shiju

Santra, Ajit Kumar

Joseph, Asha

Fachgebiete

• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik
• Mathematik | Informatik EDV | Informatik Informatik
• Sozialwissenschaften Sport | Tourismus | Freizeit Verkehr: Ratgeber, Sachbuch
• Technische Wissenschaften Verkehrstechnik | Transportgewerbe Verkehrstechnologie: Allgemeines

Inhaltsverzeichnis

1. Introduction to Intelligent Transportation Systems (ITS)

Part I. Foundations
2. Traffic Management and Control
3. Advanced Traffic Modeling and Communication
4. Intelligent Traffic Routing

Part II. Cutting-edge Technologies

5. Automated Vehicles (AVs)
6. Data Analytics for ITS

Part III. Enhancing System Efficiency
7. Predictive Maintenance and Infrastructure Monitoring
8. Emergency Response Systems

Part IV. Addressing Environmental Concerns, Ensuring Security, and Looking Ahead
9. Environmental Monitoring and Sustainability
10. Cybersecurity in ITS
11. Future Trends and Innovations in ITS
12. Conclusion

Über Autor(innen)

George, Shiju
Dr. George received a PhD in Computational Analytics from the Vellore Institute of Technology, Vellore, India, in 2022 and has been teaching at various engineering institutions since 2003. He is also a member of IEEE and the International Association of Engineers. His primary research interests include intelligent transportation systems, autonomous vehicles, artificial intelligence/big data/machine learning/deep learning applied to transportation engineering, computational intelligence, data analytics and soft computing.

Joseph, Asha
Dr. Joseph received a PhD in Operating System Security from the Vellore Institute of Technology, Vellore, India, in 2020 and has been teaching at the Amal Jyothi College of Engineering since 2021. Her previous professional experience includes some lecturer roles and a stint (2005-2010) as a software engineer in industry, at Compaq (TX, USA) and Silver Lining Tech (Bangalore, India). She is a senior member of IEEE, a member of ISTE, NCSSS, and ACM. Her research interests include smart operating systems, artificial intelligence, machine learning, computational intelligence, deep learning, big data, data science, and digital forensics.

Santra, Ajit Kumar
Dr. Santra received a PhD in Operations Research with Computer Applications from the Indian Institute of Technology, Kharagpur, India, in 1981. Over the course of his career, he has gained experience both in the industry sector (steel research) and in academic teaching. He is a lifetime member of the Computer Society of India, the Indian Society for Technical Education, and the Indian Science Congress Association. His research interests include machine learning, image processing, neural networks, applied artificial intelligence, soft computing, predictive modeling, and computational learning theory.