What is Vehicle Infrastructure Integration Vehicle infrastructure integration (VII) is an initiative fostering research and application development for a series of technologies directly linking road vehicles to their physical surroundings, first and foremost to improve road safety. The technology draws on several disciplines, including transport engineering, electrical engineering, automotive engineering, and computer science. VII specifically covers road transport, although similar technologies are in place or under development for other modes of transport. Planes, for example, use ground-based beacons for automated guidance, allowing the autopilot to fly the plane without human intervention. In highway engineering, improving the safety of a roadway can enhance overall efficiency. VII targets to improve both safety and efficiency. How you will benefit (I) Insights, and validations about the following topics: Chapter 1: Vehicle Infrastructure Integration Chapter 2: Intelligent Transportation System Chapter 3: Dedicated Short-Range Communications Chapter 4: Vehicular Communication Systems Chapter 5: Vehicular Ad Hoc Network Chapter 6: Research and Innovative Technology Administration Chapter 7: Connected Car Chapter 8: Intelligent Speed Assistance Chapter 9: GNSS Road Pricing Chapter 10: Safety of Cycling Infrastructure (II) Answering the public top questions about vehicle infrastructure integration. (III) Real world examples for the usage of vehicle infrastructure integration in many fields. Who this book is for Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Vehicle Infrastructure Integration.

Many EU cities are experiencing increasing problems with their water pipeline infrastructure. The cost of replacing these old, worn-out systems, if left to deteriorate beyond repair, is astronomical and clearly beyond the resources of many communities. Replacement, however, is not the only choice as many of these systems can be rehabilitated at 30 to 70 percent of the cost of replacement. Accordingly, resources are now increasingly being allocated to address pipeline rehabilitation management issues. Due to the emphasis on sustainable management, risk-based approaches for the rehabilitation management of the water supply network need to be developed. Rehabilitation decisions should be based, interalia, on inspection and evaluation of the pipeline conditions. Yet, utilities cannot locate a number of their old pipes and current inspection technologies typically do not provide the needed detailed information on pipeline damage. The objectives of this book are to describe the research work carried out in the framework of WATERPIPE project aiming: To develop a novel, high-resolution imaging ground penetrating radar for the detection of pipes, leaks and damages and the imaging of the damaged region and evaluate it at a test site To produce an integrated system that will contain the equipment in "1" and a Decision Support System (DSS) for the rehabilitation management of the underground water pipelines that will use input from the inspections to assess, probabilistically, the time-dependent leakage and structural reliability of the pipelines and a risk-based methodology for rehabilitation decisions that considers the overall risk, including financial, social and environmental criteria To field test the equipment and the DSS

Decision making arises when we wish to select the best possible course of action from a set of alternatives. With advancements of the digital technologies, it is easy, and almost instantaneous, to gather a large volume of information and/or data pertaining to a problem that we want to solve. For instance, the world-wi- web is perhaps the primary source of information and/or data that we often turn to when we face a decision making problem. However, the information and/or data that we obtain from the real world often are complex, and comprise various kinds of noise. Besides, real-world information and/or data often are incomplete and ambiguous, owing to uncertainties of the environments. All these make decision making a challenging task. To cope with the challenges of decision making, - searchers have designed and developed a variety of decision support systems to provide assistance in human decision making processes. The main aim of this book is to provide a small collection of techniques stemmed from artificial intelligence, as well as other complementary methodo- gies, that are useful for the design and development of intelligent decision support systems. Application examples of how these intelligent decision support systems can be utilized to help tackle a variety of real-world problems in different - mains, e. g. business, management, manufacturing, transportation and food ind- tries, and biomedicine, are also presented. A total of twenty chapters, which can be broadly divided into two parts, i. e.