With AI advancements eliciting imminent changes to our transport systems, this enlightening Handbook presents essential research on this evolution of the transportation sector. It focuses on not only urban planning, but relevant themes in law and ethics to form a unified resource on the practicality of AI use.

An authoritative, up-to-date survey of the state of the art in artificial intelligence, written for non-specialists.

In today’s developing world, international trade is a field that is rapidly growing. Within this economic market, traders need to implement new approaches in order to satisfy consumers’ rising demands. Due to the high level of competition, merchants have focused on developing new transportation and logistics strategies. In order to execute effective transportation tactics, decision makers need to know the fundamentals, current developments, and future trends of intercontinental transportation. The Handbook of Research on the Applications of International Transportation and Logistics for World Trade provides emerging research exploring the effective and productive solutions to global transportation and logistics by applying fundamental and in-depth knowledge together with current applications and future aspects. Featuring coverage on a broad range of topics such as international regulations, inventory management, and distribution networks, this book is ideally designed for logistics authorities, trading companies, logistics operators, transportation specialists, government officials, managers, policymakers, researchers, academicians, and students.

This volume tackles a quickly-evolving field of inquiry, mapping the existing discourse as part of a general attempt to place current developments in historical context; at the same time, breaking new ground in taking on novel subjects and pursuing fresh approaches. The term "A.I." is used to refer to a broad range of phenomena, from machine learning and data mining to artificial general intelligence. The recent advent of more sophisticated AI systems, which function with partial or full autonomy and are capable of tasks which require learning and 'intelligence', presents difficult ethical questions, and has drawn concerns from many quarters about individual and societal welfare, democratic decision-making, moral agency, and the prevention of harm. This work ranges from explorations of normative constraints on specific applications of machine learning algorithms today-in everyday medical practice, for instance-to reflections on the (potential) status of AI as a form of consciousness with attendant rights and duties and, more generally still, on the conceptual terms and frameworks necessarily to understand tasks requiring intelligence, whether "human" or "A.I."

The rail-based transit system is a popular public transportation option, not just with members of the public but also with policy makers looking to install a form of convenient and rapid travel. Even for moving bulk freight long distances, a rail-based system is the most sustainable transportation system currently available. The Handbook of Research on Emerging Innovations in Rail Transportation Engineering presents the latest research on next-generation public transportation infrastructures. Emphasizing a diverse set of topics related to rail-based transportation such as funding issues, policy design, traffic planning and forecasting, and engineering solutions, this comprehensive publication is an essential resource for transportation planners, engineers, policymakers, and graduate-level engineering students interested in uncovering research-based solutions, recommendations, and examples of modern rail transportation systems.

This book discusses transport systems and the implementation of related public policy - a relevant topic with contemporary traffic congestion, environmental intrusion, transport safety, and budget issues. It is a resource for both experienced researchers and those new to the field.

Technology is moving at an exponential pace in this era of computational intelligence. Machine learning has emerged as one of the most promising tools used to challenge and think beyond current limitations. This handbook will provide readers with a leading edge to improving their products and processes through optimal and smarter machine learning techniques. This handbook focuses on new machine learning developments that can lead to newly developed applications. It uses a predictive and futuristic approach, which makes machine learning a promising tool for processes and sustainable solutions. It also promotes newer algorithms that are more efficient and reliable for new dimensions in discovering other applications, and then goes on to discuss the potential in making better use of machines in order to ensure optimal prediction, execution, and decision-making. Individuals looking for machine learning-based knowledge will find interest in this handbook. The readership ranges from undergraduate students of engineering and allied courses to researchers, professionals, and application designers.