Artificial Intelligence for Future Intelligent Transportation
Artificial Intelligence for Future Intelligent Transportation

Author: Rajesh Kumar Dhanaraj

Publisher: CRC Press

Published: 2024-01-09

Total Pages: 337

ISBN-13: 1000906833

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Emphasizing a sustainable and green approach, this new book presents an overview of state-of-the-art AI strategies for solving transportation challenges around the world, with a focus on traffic management, traffic safety, public transportation, urban mobility, and pollution mitigation. The book examines modern AI technologies such as IoT, cloud computing, machine learning, and neural networking in the context of fully automated transportation that meets current requirements. The volume provides an informative review of the difficulties and recent developments in smart mobility and transportation, encompassing technical, algorithmic, and social elements. The volume examines innovative service and platform concepts for future mobility. Artificial intelligence principles are examined as well as their implementation in modern hardware architecture. New machine learning issues for autonomous vehicles and fleets are investigated in the framework of artificial intelligence. In addition, the book investigates the human dynamics and social implications of future mobility concepts. Highlighting the research directions in this field, Artificial Intelligence for Future Intelligent Transportation: Smarter and Greener Infrastructure Design will be of value for researchers in cybersecurity, machine learning, data analysis, and artificial intelligence. Ethical hackers, students, and faculty will find useful information here as well.

Explainable Artificial Intelligence for Intelligent Transportation Systems
Explainable Artificial Intelligence for Intelligent Transportation Systems

Author: Amina Adadi

Publisher: CRC Press

Published: 2023-10-20

Total Pages: 328

ISBN-13: 1000968472

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Artificial Intelligence (AI) and Machine Learning (ML) are set to revolutionize all industries, and the Intelligent Transportation Systems (ITS) field is no exception. While ML, especially deep learning models, achieve great performance in terms of accuracy, the outcomes provided are not amenable to human scrutiny and can hardly be explained. This can be very problematic, especially for systems of a safety-critical nature such as transportation systems. Explainable AI (XAI) methods have been proposed to tackle this issue by producing human interpretable representations of machine learning models while maintaining performance. These methods hold the potential to increase public acceptance and trust in AI-based ITS. FEATURES: Provides the necessary background for newcomers to the field (both academics and interested practitioners) Presents a timely snapshot of explainable and interpretable models in ITS applications Discusses ethical, societal, and legal implications of adopting XAI in the context of ITS Identifies future research directions and open problems

Deep Learning and Big Data for Intelligent Transportation
Deep Learning and Big Data for Intelligent Transportation

Author: Khaled R. Ahmed

Publisher: Springer Nature

Published: 2021-04-10

Total Pages: 264

ISBN-13: 3030656616

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This book contributes to the progress towards intelligent transportation. It emphasizes new data management and machine learning approaches such as big data, deep learning and reinforcement learning. Deep learning and big data are very energetic and vital research topics of today’s technology. Road sensors, UAVs, GPS, CCTV and incident reports are sources of massive amount of data which are crucial to make serious traffic decisions. Herewith this substantial volume and velocity of data, it is challenging to build reliable prediction models based on machine learning methods and traditional relational database. Therefore, this book includes recent research works on big data, deep convolution networks and IoT-based smart solutions to limit the vehicle’s speed in a particular region, to support autonomous safe driving and to detect animals on roads for mitigating animal-vehicle accidents. This book serves broad readers including researchers, academicians, students and working professional in vehicles manufacturing, health and transportation departments and networking companies.

Smart Transportation
Smart Transportation

Author: Guido Dartmann

Publisher: CRC Press

Published: 2021-11-10

Total Pages: 224

ISBN-13: 1000405656

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The book provides a broad overview of the challenges and recent developments in the field of smart mobility and transportation, including technical, algorithmic and social aspects of smart mobility and transportation. It reviews new ideas for services and platforms for future mobility. New concepts of artificial intelligence and the implementation in new hardware architecture are discussed. In the context of artificial intelligence, new challenges of machine learning for autonomous vehicles and fleets are investigated. The book also investigates human factors and social questions of future mobility concepts. The goal of this book is to provide a holistic approach towards smart transportation. The book reviews new technologies such as the cloud, machine learning and communication for fully atomatized transport, catering to the needs of citizens. This will lead to complete change of concepts in transportion.

The Future of Intelligent Transport Systems
The Future of Intelligent Transport Systems

Author: George J. Dimitrakopoulos

Publisher: Elsevier

Published: 2020-03-27

Total Pages: 272

ISBN-13: 0128182814

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The Future of Intelligent Transport Systems considers ITS from three perspectives: users, business models and regulation/policy. Topics cover in-vehicle applications, such as autonomous driving, vehicle-to-vehicle/vehicle-to-infrastructure communication, and related applications, such as personalized mobility. The book also examines ITS technology enablers, such as sensing technologies, wireless communication, computational technology, user behavior as part of the transportation chain, financial models that influence ITS, regulations, policies and standards affecting ITS, and the future of ITS applications. Users will find a holistic approach to the most recent technological advances and the future spectrum of mobility. Systematically presents the whole spectrum of next generation Intelligent Transport Systems (ITS) technologies Integrates coverage of personalized mobility and digital assistants, big data analytics and autonomous driving Includes end-of-chapter, open-ended questions that trigger thinking on the technological, managerial and regulatory aspects of ITS

Smart Transportation
Smart Transportation

Author: Guido Dartmann

Publisher: CRC Press

Published: 2021-11-10

Total Pages: 256

ISBN-13: 100040563X

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The book provides a broad overview of the challenges and recent developments in the field of smart mobility and transportation, including technical, algorithmic and social aspects of smart mobility and transportation. It reviews new ideas for services and platforms for future mobility. New concepts of artificial intelligence and the implementation in new hardware architecture are discussed. In the context of artificial intelligence, new challenges of machine learning for autonomous vehicles and fleets are investigated. The book also investigates human factors and social questions of future mobility concepts. The goal of this book is to provide a holistic approach towards smart transportation. The book reviews new technologies such as the cloud, machine learning and communication for fully atomatized transport, catering to the needs of citizens. This will lead to complete change of concepts in transportion.

Explainable AI for Intelligent Transportation Systems
Explainable AI for Intelligent Transportation Systems

Author: Amina Adadi

Publisher:

Published: 2024

Total Pages: 0

ISBN-13: 9781032348537

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"Artificial Intelligence (AI) and Machine Learning (ML) are set to revolutionize all industries, and the Intelligent Transportation Systems (ITS) field is no exception. While ML, especially deep learning models, achieve great performance in terms of accuracy, the outcomes provided are not amenable to human scrutiny and can be hardly explained. This can be very problematic especially for systems of a safety-critical nature such as transportation systems. Explainable AI methods have been proposed to tackle this issue by producing human interpretable representations of machine learning models while maintaining performance. These methods hold the potential to increase public acceptance and trust in AI-based ITS. Examining explainable AI in the field of ITS, this book has the following key features: provides the necessary background for newcomers to the field (both academics and interested partitioners). presents a timely snapshot of explainable and interpretable models in ITS applications. discusses ethical, societal, and legal implications of adopting XAI in the context of ITS. identifies future research directions and open problems"--

Explainable Artificial Intelligence for Intelligent Transportation Systems
Explainable Artificial Intelligence for Intelligent Transportation Systems

Author: Loveleen Gaur

Publisher: Springer Nature

Published: 2022-08-08

Total Pages: 103

ISBN-13: 3031096444

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Transportation typically entails crucial “life-death” choices, delegating crucial decisions to an AI algorithm without any explanation poses a serious threat. Hence, explainability and responsible AI is crucial in the context of intelligent transportation. In Intelligence Transportation System (ITS) implementations such as traffic management systems and autonomous driving applications, AI-based control mechanisms are gaining prominence. Explainable artificial intelligence for intelligent transportation system tackling certain challenges in the field of autonomous vehicle, traffic management system, data integration and analytics and monitor the surrounding environment. The book discusses and inform researchers on explainable Intelligent Transportation system. It also discusses prospective methods and techniques for enabling the interpretability of transportation systems. The book further focuses on ethical considerations apart from technical considerations.

Computational Intelligence for Sustainable Transportation and Mobility
Computational Intelligence for Sustainable Transportation and Mobility

Author: Deepak Gupta; Suresh

Publisher:

Published: 2021-12-16

Total Pages: 150

ISBN-13: 9781681089454

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New technologies and computing methodologies are now used to address the existing issues of urban traffic systems. The development of computational intelligence methods such as machine learning and deep learning, enables engineers to find innovative solutions to guide traffic in order to reduce transportation and mobility problems in urban areas. This volume, Computational Intelligence for Sustainable Transportation and Mobility, presents several computing models for intelligent transportation systems, which may hold the key to achieving sustainable development goals by optimizing traffic flow and minimizing associated risks. The book begins with the basic computational Intelligence techniques for traffic systems and explains its applications in vehicular traffic prediction, model optimization, behavior analysis, traffic density estimation, and more. The main objectives of this book are to present novel techniques developed, new technologies and computational intelligence for sustainable mobility and transportation solutions, as well as giving an understanding of some Industry 4.0 trends. Readers will learn how to apply computational intelligence techniques such as multiagent systems (MAS), whale optimization, artificial Intelligence (AI), deep neural networks (DNNs) so that they can to develop algorithms, models, and approaches for sustainable transportation operations. Key Features: - Provides an overview of machine learning models and their optimization for intelligent transportation systems in urban areas - Covers classification of traffic behavior - Demonstrates the application of artificial immune system algorithms for traffic prediction - Covers traffic density estimation using deep learning models - Covers Fog and edge computing for intelligent transportation systems - Gives an IoT and Industry 4.0 perspective about intelligent transportation systems to readers - Presents a current perspective on an urban hyperloop system for India