In collaboration with the U.S. Environmental Protection Agency and the U.S. Department of Energy, the National Renewable Energy Laboratory has conducted a national analysis of road grade characteristics experienced by U.S. medium- and heavy-duty trucks on controlled access highways. These characteristics have been developed using TomTom's commercially available street map and road grade report includes a detailed comparison of the TomTom national road grade database relative to a local road grade dataset generated by Southwest Research Institute and a national elevation dataset publically available from the U.S. Geological Survey. This analysis concluded that the TomTom national road grade database was a suitable source of road grade data for purposes of this study. Using the TomTom national road grade database, national statistics on road grade and hill distances were generated for the U.S. network of controlled access highways. These statistical distributions were then weighted using data provided by the U.S. Environmental Protection Agency on activity of medium- and heavy-duty trucks on controlled access highways. The national activity-weighted road grade and hill distance distributions were then used as targets for development of a handful of sample grade profiles potentially to be used in the U.S. Environmental Protection Agency's Greenhouse Gas Emissions Model certification tool as well as in dynamometer testing of medium- and heavy duty vehicles and their powertrains. Future work in this area may include the development of a highway drive cycle with transient speeds aligned to reflect real-world operation of medium- and heavy-duty vehicles on controlled access highways. Such a drive cycle would consider the correlation between vehicle speed and road grade as observed in on-road data.

Xiii ABSTRACT The goal of this project is to develop and evaluate powertrain test procedures that can accurately simulate real-world operating conditions, and to determine greenhouse gas (GHG) emissions of advanced medium- and heavy-duty engine and vehicle technologies. ORNL used their Vehicle System Integration Laboratory to evaluate test procedures on a stand-alone engine as well as two powertrains. Those components where subjected to various drive cycles and vehicle conditions to evaluate the validity of the results over a broad range of test conditions. Overall, more than 1000 tests were performed. The data are compiled and analyzed in this report.

Technologies and Approaches to Reducing the Fuel Consumption of Medium- and Heavy-Duty Vehicles evaluates various technologies and methods that could improve the fuel economy of medium- and heavy-duty vehicles, such as tractor-trailers, transit buses, and work trucks. The book also recommends approaches that federal agencies could use to regulate these vehicles' fuel consumption. Currently there are no fuel consumption standards for such vehicles, which account for about 26 percent of the transportation fuel used in the U.S. The miles-per-gallon measure used to regulate the fuel economy of passenger cars. is not appropriate for medium- and heavy-duty vehicles, which are designed above all to carry loads efficiently. Instead, any regulation of medium- and heavy-duty vehicles should use a metric that reflects the efficiency with which a vehicle moves goods or passengers, such as gallons per ton-mile, a unit that reflects the amount of fuel a vehicle would use to carry a ton of goods one mile. This is called load-specific fuel consumption (LSFC). The book estimates the improvements that various technologies could achieve over the next decade in seven vehicle types. For example, using advanced diesel engines in tractor-trailers could lower their fuel consumption by up to 20 percent by 2020, and improved aerodynamics could yield an 11 percent reduction. Hybrid powertrains could lower the fuel consumption of vehicles that stop frequently, such as garbage trucks and transit buses, by as much 35 percent in the same time frame.

The automotive industry appears close to substantial change engendered by “self-driving” technologies. This technology offers the possibility of significant benefits to social welfare—saving lives; reducing crashes, congestion, fuel consumption, and pollution; increasing mobility for the disabled; and ultimately improving land use. This report is intended as a guide for state and federal policymakers on the many issues that this technology raises.

Decision trees have become one of the most powerful and popular approaches in knowledge discovery and data mining; it is the science of exploring large and complex bodies of data in order to discover useful patterns. Decision tree learning continues to evolve over time. Existing methods are constantly being improved and new methods introduced.This 2nd Edition is dedicated entirely to the field of decision trees in data mining; to cover all aspects of this important technique, as well as improved or new methods and techniques developed after the publication of our first edition. In this new edition, all chapters have been revised and new topics brought in. New topics include Cost-Sensitive Active Learning, Learning with Uncertain and Imbalanced Data, Using Decision Trees beyond Classification Tasks, Privacy Preserving Decision Tree Learning, Lessons Learned from Comparative Studies, and Learning Decision Trees for Big Data. A walk-through guide to existing open-source data mining software is also included in this edition.This book invites readers to explore the many benefits in data mining that decision trees offer: