Artificial Intelligence In Highway Safety

Artificial Intelligence in Highway Safety provides cutting-edge advances in highway safety using AI. The author is a highway safety expert. He pursues highway safety within its contexts, while drawing attention to the predictive powers of AI techniques in solving complex problems for safety improvement. This book provides both theoretical and practical aspects of highway safety. Each chapter contains theory and its contexts in plain language with several real-life examples. It is suitable for anyone interested in highway safety and AI and it provides an illuminating and accessible introduction to this fast-growing research trend. Material supplementing the book can be found at https://github.com/subasish/AI_in_HighwaySafety. It offers a variety of supplemental materials, including data sets and R codes.

In recent years the applications of advanced information technologies in the field of transportation have affected both road infrastructures and vehicle technologies. The development of advanced transport telematics systems and the implementation of a new generation of technological options in the transport environment have had a significant impact on improved traffic management, efficiency and safety. This volume contains contributions from scientific and academic centres which have been active in this field of research and provides an overview of applications of AI technology in the field of traffic control and management. The topics covered are: -- current status of AI in transport -- AI applications in traffic engineering -- in-vehicle AI

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.

This monograph provides a comprehensive overview of methods for searching, evaluating, and optimizing highway location and alignments using genetic algorithms (GAs), a powerful Artificial Intelligence (AI) technique. It presents a two-level programming structure to deal with the effects of varying highway location on traffic level changes in surrounding road networks within the highway location search and alignment optimization process. In addition, the proposed method evaluates environmental impacts as well as all relevant highway costs associated with its construction, operation, and maintenance. The monograph first covers various search methods, relevant cost functions, constraints, computational efficiency, and solution quality issues arising from optimizing the highway alignment optimization (HAO) problem. It then focuses on applications of a special-purpose GA in the HAO problem where numerous highway alignments are generated and evaluated, and finally the best ones are selected based on costs, traffic impacts, safety, energy, and environmental considerations. A review of other promising optimization methods for the HAO problem is also provided in this monograph.

This open-access-book synthesizes a supportive developer checklist considering sustainable Team and agile Project Management in the challenge of Artificial Intelligence and limits of image recognition. The study bases on technical, ethical, and legal requirements with examples concerning autonomous vehicles. As the first of its kind, it analyzes all reported car accidents state wide (1.28 million) over a 10-year period. Integrating of highly sensitive international court rulings and growing consumer expectations make this book a helpful guide for product and team development from initial concept until market launch. The author Thomas Winkle (Prof. Dr.-Ing., MBA Communication & Leadership) is a multiple author in best-selling Springer books such as "Autonomous Driving: Technical, Legal and Social Aspects" or the "Handbook of Driver Assistance Systems". His work bases on three decades in sustainable team consulting as employee and researcher in the legal departments of three car manufacturers as well as a professor at IU International University and TU Munich. Thomas Winkle received the Volkswagen research award for his significant Human-Centered Design into the development of the Automatic Emergency Brake. He was responsible to prepare the ADAS Code of Practice. As consultant at international courts, he links Artificial Intelligence, Ethics, Sustainable Agile Management, Mindful Communication and Law using Autonomous Vehicles example definition requirements.

Highway Safety Analytics and Modeling comprehensively covers the key elements needed to make effective transportation engineering and policy decisions based on highway safety data analysis in a single. reference. The book includes all aspects of the decision-making process, from collecting and assembling data to developing models and evaluating analysis results. It discusses the challenges of working with crash and naturalistic data, identifies problems and proposes well-researched methods to solve them. Finally, the book examines the nuances associated with safety data analysis and shows how to best use the information to develop countermeasures, policies, and programs to reduce the frequency and severity of traffic crashes. Complements the Highway Safety Manual by the American Association of State Highway and Transportation Officials Provides examples and case studies for most models and methods Includes learning aids such as online data, examples and solutions to problems

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.