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This monograph presents a simple, innovative approach for the measurement and short-term prediction of highway travel times based on the fusion of inductive loop detector and toll ticket data. The methodology is generic and not technologically captive, allowing it to be easily generalized for other equivalent types of data. The book shows how Bayesian analysis can be used to obtain fused estimates that are more reliable than the original inputs, overcoming some of the drawbacks of travel-time estimations based on unique data sources. The developed methodology adds value and obtains the maximum (in terms of travel time estimation) from the available data, without recurrent and costly requirements for additional data. The application of the algorithms to empirical testing in the AP-7 toll highway in Barcelona proves that it is possible to develop an accurate real-time, travel-time information system on closed-toll highways with the existing surveillance equipment, suggesting that highway operators might provide their customers with such an added value with little additional investment in technology.
This book deals with the estimation of travel time in a very comprehensive and exhaustive way. Travel time information is and will continue to be one key indicator of the quality of service of a road network and a highly valued knowledge for drivers. Moreover, travel times are key inputs for comprehensive traffic management systems. All the above-mentioned aspects are covered in this book. The first chapters expound on the different types of travel time information that traffic management centers work with, their estimation, their utility and their dissemination. They also remark those aspects in which this information should be improved, especially considering future cooperative driving environments.Next, the book introduces and validates two new methodologies designed to improve current travel time information systems, which additionally have a high degree of applicability: since they use data from widely disseminated sources, they could be immediately implemented by many administrations without the need for large investments. Finally, travel times are addressed in the context of dynamic traffic management systems. The evolution of these systems in parallel with technological and communication advancements is thoroughly discussed. Special attention is paid to data analytics and models, including data-driven approaches, aimed at understanding and predicting travel patterns in urban scenarios. Additionally, the role of dynamic origin-to-destination matrices in these schemes is analyzed in detail.
Multisensor Data Fusion: From Algorithms and Architectural Design to Applications covers the contemporary theory and practice of multisensor data fusion, from fundamental concepts to cutting-edge techniques drawn from a broad array of disciplines. Featuring contributions from the world’s leading data fusion researchers and academicians, this authoritative book: Presents state-of-the-art advances in the design of multisensor data fusion algorithms, addressing issues related to the nature, location, and computational ability of the sensors Describes new materials and achievements in optimal fusion and multisensor filters Discusses the advantages and challenges associated with multisensor data fusion, from extended spatial and temporal coverage to imperfection and diversity in sensor technologies Explores the topology, communication structure, computational resources, fusion level, goals, and optimization of multisensor data fusion system architectures Showcases applications of multisensor data fusion in fields such as medicine, transportation's traffic, defense, and navigation Multisensor Data Fusion: From Algorithms and Architectural Design to Applications is a robust collection of modern multisensor data fusion methodologies. The book instills a deeper understanding of the basics of multisensor data fusion as well as a practical knowledge of the problems that can be faced during its execution.
The Rise of Smart Cities: Advanced Structural Sensing and Monitoring Systems provides engineers and researchers with a guide to the latest breakthroughs in the deployment of smart sensing and monitoring technologies. The book introduces readers to the latest innovations in the area of smart infrastructure-enabling technologies and howthey can be integrated into the planning and design of smart cities. With this book in hand, readers will find a valuable reference in terms of civil infrastructure health monitoring, advanced sensor network architectures, smart sensing materials, multifunctional material and structures, crowdsourced/social sensing, remote sensing and aerial sensing, and advanced computation in sensornetworks. - Reviews the latest development in smart structural health monitoring (SHM) systems - Introduces all major algorithms, with a focus on practical implementation - Includes real-world applications and case studies - Opens up a new horizon for robust structural sensing methods and their applications in smart cities
The second volume of the book series highlights works presented at the 2nd International Conference on Real Time Intelligent Systems, held in Casablanca on October 18-20, 2017​. The book offers a comprehensive, practical review of the state-of-the-art in designing and implementing real-time intelligent computing for the areas within the conference’s scope such as robotics, intelligent alert systems, IoT, remote access control, multi-agent systems, networking, mobile smart systems, crowdsourcing, broadband systems, cloud computing, streaming data and many other applications. Research in real-time computing supports decision making in dynamic environments. Some examples include ABS, FBW flight control, automatic air-conditioning, etc. Intelligent computing relies heavily on artificial intelligence (AI) to make computers act for humans. The authors are confident that the solutions discussed in this book will provide a unique source of information and inspiration for researchers working in AI, distributed coding algorithms or smart services and platforms, and for IT professionals, who can integrate the proposed methods into their practice.
This book contains an abundance of numerical analyses based on significant data sets, illustrating importance of environmentally friendly solutions requiring transport networks to be redesigned or clean zones to be implemented. What kind of steps should be taken to redesign transport network? How to evaluate efficiency or flexibility of transport system and city logistics? What factors can be taken into account in the process of optimizing the functioning of public transport or paid parking zones? How to optimize supply chains (including last mile delivering and routing problem)? Which of the multi-criteria methods should be applied to support decision making processes while tackling problems of global transport systems? Answers to these and many other questions can be found in this book.With regard to the research results discussed and the selected solutions applied, the book entitled "Decision support methods in modern transportation systems and networks" primarily addresses the needs of three target groups: · Scientists and researchers (ITS field) · Local authorities (responsible for the transport systems at the urban and regional level) · Representatives of business (traffic strategy management) and industry (manufacturers of ITS components).
This book is a printed edition of the Special Issue "Advances in Multi-Sensor Information Fusion: Theory and Applications 2017" that was published in Sensors
As transport networks become more congested, there is a growing need to adopt policies that manage demand and make full use of existing assets. Advances in information technology are now such that intelligent transportation systems (ITS) offer real potential to meet this challenge by monitoring current conditions, predicting what might happen in the future, and providing the means to manage transport proactively and on an area-wide basis. Modeling and Simulation of Intelligent Transportation Systems provides engineers, professionals, and researchers an intuitive appreciation for ITS theory, related sensor technologies, and other practical applications, including traffic management, safety, design optimization, and sustainability. Provides the theory and practical applications of Intelligent Transport Theory which will be helpful as highway construction recedes as a sustainable long-term solution. Includes several case studies that illustrate the concepts presented throughout.
Mobility Patterns, Big Data and Transport Analytics provides a guide to the new analytical framework and its relation to big data, focusing on capturing, predicting, visualizing and controlling mobility patterns - a key aspect of transportation modeling. The book features prominent international experts who provide overviews on new analytical frameworks, applications and concepts in mobility analysis and transportation systems. Users will find a detailed, mobility 'structural' analysis and a look at the extensive behavioral characteristics of transport, observability requirements and limitations for realistic transportation applications and transportation systems analysis that are related to complex processes and phenomena. This book bridges the gap between big data, data science, and transportation systems analysis with a study of big data's impact on mobility and an introduction to the tools necessary to apply new techniques. The book covers in detail, mobility 'structural' analysis (and its dynamics), the extensive behavioral characteristics of transport, observability requirements and limitations for realistic transportation applications, and transportation systems analysis related to complex processes and phenomena. The book bridges the gap between big data, data science, and Transportation Systems Analysis with a study of big data's impact on mobility, and an introduction to the tools necessary to apply new techniques. - Guides readers through the paradigm-shifting opportunities and challenges of handling Big Data in transportation modeling and analytics - Covers current analytical innovations focused on capturing, predicting, visualizing, and controlling mobility patterns, while discussing future trends - Delivers an introduction to transportation-related information advances, providing a benchmark reference by world-leading experts in the field - Captures and manages mobility patterns, covering multiple purposes and alternative transport modes, in a multi-disciplinary approach - Companion website features videos showing the analyses performed, as well as test codes and data-sets, allowing readers to recreate the presented analyses and apply the highlighted techniques to their own data
Data-Driven Solutions to Transportation Problems explores the fundamental principle of analyzing different types of transportation-related data using methodologies such as the data fusion model, the big data mining approach, computer vision-enabled traffic sensing data analysis, and machine learning. The book examines the state-of-the-art in data-enabled methodologies, technologies and applications in transportation. Readers will learn how to solve problems relating to energy efficiency under connected vehicle environments, urban travel behavior, trajectory data-based travel pattern identification, public transportation analysis, traffic signal control efficiency, optimizing traffic networks network, and much more. - Synthesizes the newest developments in data-driven transportation science - Includes case studies and examples in each chapter that illustrate the application of methodologies and technologies employed - Useful for both theoretical and technically-oriented researchers