Driving Green Transportation System Through Artificial Intelligence And Automation
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Driving Green Transportation System Through Artificial Intelligence and Automation
This book is designed to help transportation professionals and construction experts to develop and implement successful smart systems, leveraging the current trends, equipment, and advanced technologies to drive the green transportation system development. Artificial intelligence (AI) is a new direction that has opened a revolution in technology and smart applications, and it is also the basis for creating a green environment in the net-zero era. Therefore, machines, devices, self-driving car, and robots controlled by artificial intelligence-based systems are now the model of a smart transportation ecosystem for which all these technologies are referred to as "green" industries. In past years, the idea of making a green environment has been existing and moving on the society 5.0 being as a country strategy, and today, AI technology continues its development on this prototype. Nowadays, AI has begun actions to resemble a person in a real sense, and the idea of human-liked robotics put forward by scientists has started to be realized and will probably complete its development as living machines in the near future. AI has many subsystems and application in various industries, some of which have automation more accurately and are more integrated in modern industries. This book also targets a mixed audience of specialists, analysts, engineers, scholars, researchers, academics, professionals, and students from different communities to share and contribute new ideas, methodologies, technologies, approaches, models, frameworks, theories, and practices to resolve the challenging issues associated with the leveraging of AI and Industrial Internet of Things (IIoT) in green transportation ecosystem.
Machine Learning and Robotics in Urban Planning and Management
The integration of advanced technologies has led to drastic changes in the field of urban planning and management. When using large amounts of data from numerous sources, machine learning models can mimic past scenarios which predict future events. Urban planners use these predictions when making infrastructure or administrative decisions geared towards a highly efficient and sustainable urban environment. Machine learning algorithms can reduce the wait times at intersections, stop-and-go traffic, and overall congestion by adjusting signal timings on a real-time basis according to live density of vehicles. Conventional bottlenecks are identified and possible route changes proposed to generate improved traffic flow across regions. Further research may continue encouraging urban planning and management innovation. Machine Learning and Robotics in Urban Planning and Management explores the integration of machine learning and robotics technology in urban and regional development. It examines solutions for traffic management, infrastructure improvements, and prediction models using intelligent technology. This book covers topics such as neural networks, smart cities, and transportation systems, and is a useful resource for urban developers, business owners, scientists, civil engineers, academicians, and researchers.
AI's Role in Enhanced Automotive Safety
Artificial intelligence (AI) plays a transformative role in enhancing automotive safety, revolutionizing how vehicles prevent accidents and protect passengers. By integrating advanced sensors, real-time data analysis, and machine learning algorithms, AI enables cars to detect hazards, predict potential collisions, and respond fast. From driver-assistance features like automatic emergency braking and lane assistance, to the development of fully autonomous vehicles, AI reshapes the landscape of road safety. As technology evolves, AI's role in minimizing human error and improving safe, smart transportation begs further exploration. AI's Role in Enhanced Automotive Safety explores AI-driven advancements in automotive safety, highlights possible obstacles to widespread adoption, and offers policy suggestions. It examines the possible impacts of AI-driven technology on vehicle safety. This book covers topics such as deep learning, neural networks, and sensor technology, and is a useful resource computer, civil, and mechanical engineers, automotive business owners, urban developers, academicians, researchers, and data scientists.