How can the Bureau of Transportation Statistics (BTS), the nation's newest federal statistical agency, contribute to the work of the U.S. Department of Transportation (USDOT)? What is the appropriate role for such an agency as a part of a major department? BTS was authorized in 1991 by the Intermodal Surface Transportation Efficiency Act (ISTEA) in recognition of the need for more and better data for transportation officials at local, state, and federal levels. While the USDOT has many long-standing data collection programs for particular transportation modes (highways, rail, air, etc.), it has never had a statistical agency with a mandate to improve the quality and relevance of transportation data for important system-wide, cross-modal analyses of the nation's transportation system. This book examines how BTS can provide statistical leadership for USDOT, define and maintain quality standards for transportation data, and improve data documentation. It considers BTS's role in developing national transportation indicators, coordinating data collection throughout USDOT, filling gaps, identifying user needs, and developing analysis programs for transportation data. Anyone concerned with having high-quality, relevant transportation indicators and other data available for policy planning, evaluation, and research will be interested in this book, as will students of effective government.

How can the Bureau of Transportation Statistics (BTS), the nation's newest federal statistical agency, contribute to the work of the U.S. Department of Transportation (USDOT)? What is the appropriate role for such an agency as a part of a major department? BTS was authorized in 1991 by the Intermodal Surface Transportation Efficiency Act (ISTEA) in recognition of the need for more and better data for transportation officials at local, state, and federal levels. While the USDOT has many long-standing data collection programs for particular transportation modes (highways, rail, air, etc.), it has never had a statistical agency with a mandate to improve the quality and relevance of transportation data for important system-wide, cross-modal analyses of the nation's transportation system. This book examines how BTS can provide statistical leadership for USDOT, define and maintain quality standards for transportation data, and improve data documentation. It considers BTS's role in developing national transportation indicators, coordinating data collection throughout USDOT, filling gaps, identifying user needs, and developing analysis programs for transportation data. Anyone concerned with having high-quality, relevant transportation indicators and other data available for policy planning, evaluation, and research will be interested in this book, as will students of effective government.

America's economy and lifestyles have been shaped by the low prices and availability of energy. In the last decade, however, the prices of oil, natural gas, and coal have increased dramatically, leaving consumers and the industrial and service sectors looking for ways to reduce energy use. To achieve greater energy efficiency, we need technology, more informed consumers and producers, and investments in more energy-efficient industrial processes, businesses, residences, and transportation. As part of the America's Energy Future project, Real Prospects for Energy Efficiency in the United States examines the potential for reducing energy demand through improving efficiency by using existing technologies, technologies developed but not yet utilized widely, and prospective technologies. The book evaluates technologies based on their estimated times to initial commercial deployment, and provides an analysis of costs, barriers, and research needs. This quantitative characterization of technologies will guide policy makers toward planning the future of energy use in America. This book will also have much to offer to industry leaders, investors, environmentalists, and others looking for a practical diagnosis of energy efficiency possibilities.

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

Transportation Statistics Annual Report is a summary of the state of the transportation system and its consequences, the quality of statistics used to characterize the transportation system, and planned efforts by the U.S. Department of Transportation's Bureau of Transportation Statistics (BTS) to improve the quality of the statistics. BTS was established by Congress to compile, analyze, and make accessible information on the nation's transportation system; to collect information on intermodal transportation and other related areas as needed; and to enhance the quality and effectiveness of the Department of Transportation's statistical programs through research, the development of guidelines, and the promotion of improvements in data acquisition and use.

This report presents guidance to assist decision makers and data practitioners at state departments of transportation (DOTs) in evaluating and improving the value of their data for decision making and their data-management practices. Agency practitioners rely on a wide range of data to support decision making about policy choices, infrastructure investments, and other agency functions. The self-assessment process described here may also be useful to metropolitan planning organizations (MPOs).