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An intelligent transportation system (ITS) offers considerable opportunities for increasing the safety, efficiency, and predictability of traffic flow and reducing vehicle emissions. Sensors (or detectors) enable the effective gathering of arterial and controlled-access highway information in support of automatic incident detection, active transportation and demand management, traffic-adaptive signal control, and ramp and freeway metering and dispatching of emergency response providers. As traffic flow sensors are integrated with big data sources such as connected and cooperative vehicles, and cell phones and other Bluetooth-enabled devices, more accurate and timely traffic flow information can be obtained. The book examines the roles of traffic management centers that serve cities, counties, and other regions, and the collocation issues that ensue when multiple agencies share the same space. It describes sensor applications and data requirements for several ITS strategies; sensor technologies; sensor installation, initialization, and field-testing procedures; and alternate sources of traffic flow data. The book addresses concerns related to the introduction of automated and connected vehicles, and the benefits that systems engineering and national ITS architectures in the US, Europe, Japan, and elsewhere bring to ITS. Sensor and data fusion benefits to traffic management are described, while the Bayesian and Dempster–Shafer approaches to data fusion are discussed in more detail. ITS Sensors and Architectures for Traffic Management and Connected Vehicles suits the needs of personnel in transportation institutes and highway agencies, and students in undergraduate or graduate transportation engineering courses.
This exciting new volume covers the most up-to-date advances, theories, and practical applications for non-motorized transportation (NMT) systems, geographic information system-based transportation systems, and signal processing for urban transportation systems. This book will allow readers to readers to identify traffic and transport problems in cities and to study mass transportation systems, and modes of transportation and their characteristics, focusing on transportation infrastructure which includes green bays, control stations, mitigation buildings, separator lanes, and safety islands. From this, readers will be able to study urban public transport systems and gain some background into intelligent transportation and telemetric systems, including techniques for designing transport telemetric systems and applying them to urban transportation. Applications include advanced traffic management systems, advanced traveler information systems, advanced vehicle control systems, commercial vehicle operational management, advanced public transportation systems, electronic payment systems, advanced urban transportation, security and safety systems, and urban traffic control. From this, an artificial Intelligence-based transportation system design using genetic algorithms and neural networks is discussed, to show applications in designs. These models and their studies are further extended in signal processing systems and geographic information systems (GIS) to improve transportation system design, and to apply this to the design of non-motorized transportation models, while ensuring pedestrian safety. All these models are further analyzed for environmental impact assessment, which include structural audits, analysis of site selection procedure, baseline conditions and major concerns, green building and its advantages, the description of potential environmental effects, and many more interesting topics.
This report serves as a comprehensive guide to traffic signal timing and documents the tasks completed in association with its development. The focus of this document is on traffic signal control principles, practices, and procedures. It describes the relationship between traffic signal timing and transportation policy and addresses maintenance and operations of traffic signals. It represents a synthesis of traffic signal timing concepts and their application and focuses on the use of detection, related timing parameters, and resulting effects to users at the intersection. It discusses advanced topics briefly to raise awareness related to their use and application. The purpose of the Signal Timing Manual is to provide direction and guidance to managers, supervisors, and practitioners based on sound practice to proactively and comprehensively improve signal timing. The outcome of properly training staff and proactively operating and maintaining traffic signals is signal timing that reduces congestion and fuel consumption ultimately improving our quality of life and the air we breathe. This manual provides an easy-to-use concise, practical and modular guide on signal timing. The elements of signal timing from policy and funding considerations to timing plan development, assessment, and maintenance are covered in the manual. The manual is the culmination of research into practices across North America and serves as a reference for a range of practitioners, from those involved in the day to day management, operation and maintenance of traffic signals to those that plan, design, operate and maintain these systems.
An essential book on the applications of AI and digital twin technology in the smart manufacturing sector. In the rapidly evolving landscape of modern manufacturing, the integration of cutting-edge technologies has become imperative for businesses to remain competitive and adaptive. Among these technologies, Artificial Intelligence (AI) stands out as a transformative force, revolutionizing traditional manufacturing processes and making the way for the era of smart manufacturing. At the heart of this technological revolution lies the concept of the Digital Twin—an innovative approach that bridges the physical and digital realms of manufacturing. By creating a virtual representation of physical assets, processes, and systems, organizations can gain unprecedented insights, optimize operations, and enhance decision-making capabilities. This timely book explores the convergence of AI and Digital Twin technologies to empower smart manufacturing initiatives. Through a comprehensive examination of principles, methodologies, and practical applications, it explains the transformative potential of AI-enabled Digital Twins across various facets of the manufacturing lifecycle. From design and prototyping to production and maintenance, AI-enabled Digital Twins offer multifaceted advantages that redefine traditional paradigms. By leveraging AI algorithms for data analysis, predictive modeling, and autonomous optimization, manufacturers can achieve unparalleled levels of efficiency, quality, and agility. This book explains how AI enhances the capabilities of Digital Twins by creating a powerful tool that can optimize production processes, improve product quality, and streamline operations. Note that the Digital Twin in this context is a virtual representation of a physical manufacturing system, including machines, processes, and products. It continuously collects real-time data from sensors and other sources, allowing it to mirror the physical system’s behavior and performance. What sets this Digital Twin apart is the incorporation of AI algorithms and machine learning techniques that enable it to analyze and predict outcomes, recommend improvements, and autonomously make adjustments to enhance manufacturing efficiency. This book outlines essential elements, like real-time monitoring of machines, predictive analytics of machines and data, optimization of the resources, quality control of the product, resource management, decision support (timely or quickly accurate decisions). Moreover, this book elucidates the symbiotic relationship between AI and Digital Twins, highlighting how AI augments the capabilities of Digital Twins by infusing them with intelligence, adaptability, and autonomy. Hence, this book promises to enhance competitiveness, reduce operational costs, and facilitate innovation in the manufacturing industry. By harnessing AI’s capabilities in conjunction with Digital Twins, manufacturers can achieve a more agile and responsive production environment, ultimately driving the evolution of smart factories and Industry 4.0/5.0. Audience This book has a wide audience in computer science, artificial intelligence, and manufacturing engineering, as well as engineers in a variety of industrial manufacturing industries. It will also appeal to economists and policymakers working on the circular economy, clean tech investors, industrial decision-makers, and environmental professionals.
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The approaches to computer vision have undergone a long journey in recent years, but still, innovations are continuing with leverage increases in computing power, new data availability, and new ways to leverage machine-learning algorithms. As a branch of artificial intelligence (AI), computer vision brings meaningful information from images and videos. Such innovations help communicators to run better campaigns, amplify messages further, and stand out in a noisy, crowded marketplace. Investigations in Pattern Recognition and Computer Vision for Industry 4.0 provides a holistic discussion of the new practical applications and use cases of computer vision and communications. Covering topics such as social media filters, mobile computer vision, and AI-powered image editing, this book is ideal for academicians, researchers, postgraduate students, professional data analysts, research and development centers, organizations dealing with healthcare informatics, and IT firms.
This contributed volume covers all relevant aspects of road vehicle automation including societal impacts, legal matters, and technology innovation from the perspectives of a multitude of public and private actors. It is based on an expert workshop organized by the Transportation Research Board at Stanford University in July 2013. The target audience primarily comprises academic researchers, but the book may also be of interest to practitioners and professionals. Higher levels of road vehicle automation are considered beneficial for road safety, energy efficiency, productivity, convenience and social inclusion. The necessary key technologies in the fields of object-recognition systems, data processing and infrastructure communication have been consistently developed over the recent years and are mostly available on the market today. However, there is still a need for substantial research and development, e.g. with interactive maps, data processing, functional safety and the fusion of different data sources. Driven by stakeholders in the IT industry, intensive efforts to accelerate the introduction of road vehicle automation are currently underway.