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Electric Vehicles (EVs) significantly reduce energy consumption and emissions from on-road operations and help create more sustainable transportation environment by reducing emissions from the entire well-to wheel energy cycle. Differences between hybrid electric vehicle (HEV), plug-in hybrid electric vehicle (PHEV), and battery electric vehicles (BEV) users is an important element in understanding potential impacts on travel demand and vehicle adoption, the fact that these vehicles may be adopted into households that undertake very different vehicle activities and energy usage patterns has not been a primary focus in the literature. This study differentiates between HEV, PHEV, and BEV users across three factors: owner household socio-demographic attributes, household daily travel patterns, and household energy usage profiles. The analyses examine factors that appear to influence users’ preferences towards specific EV types and how the selection of different EV types potentially relates to household socio demographics and daily travel patterns. The 2019 Puget Sound Regional Council travel survey data set serves as the main analytical dataset. Influential factors identified as significant through statistical approaches are employed as variables for developing a two-phase choice model for determining potential EV-purchasing households and their choice of specific EV type. As EVs continue to capture increasing market share over time, these research findings and the resulting vehicle type choice model are expected to significantly improve future travel demand model development, allowing activity-based travel demand models to assign specific vehicles to specific households and then to individual trips in planning scenario analysis.View the NCST Project Webpage

This white paper summarizes the development of MOVES-Matrix 3.0 based on EPA’s latest MOVES model known as MOVES3 (version 3.0.4). The research team updated the programs to account for changes in data structures and source sub-types and applied the same conceptual design used in MOVES-Matrix 2.0. The review of the MOVES3 and MOVES 2014b databases indicated a finer definition of the regions in terms of the unique combinations of fuel supply regions vs. Inspection/Maintenance (I/M) programs, with 40 fuel scenarios and 87 I/M scenarios in MOVES3 and 22 fuel scenarios and 84 I/M scenarios in MOVES 2014b. The increased number of fuel scenarios is due to the increased number of formulation regions and the one-to-many corresponding relationship between counties vs. fuel formulation regions by year. A total of 122 regions are defined in MOVES3 compared with 109 regions in MOVES 2014b, and the team anticipates at least 10% more running time to generate matrices for MOVES3, given the larger number of regions and the more complicated source type VSP/STP variables.View the NCST Project Webpage

Next War: Reimagining How We Fight Author: John Antal Publisher: Casemate Copyright Date: 2023 Hard/Softcover/Digital: Softcover, 240 pages ISBN-13: 9781636243351 Reviewed by: Michael A. Rodgers, Ed.D., Professor of Contract Management, [...]

Newcomers to Canada with low proficiency in English or French often face challenges in the workforce (Kustec, 2012). While language classes provide workplace language training, not all newcomers are able to attend face-to-face classes (Shaffir & Satzewich, 2010), suggesting a need for outside the classroom, occupation-specific language training. The use of technology has been shown to be advantageous for second language (L2) learning (Stockwell, 2007), especially when used outside the classroom (i.e., mobile-assisted language learning), as mobile technology affords learners greater control and flexibility over their own learning (Yang, 2013). This paper reports on a study investigating the development of a blended curriculum for L2 learners employed in customer service. A technology-mediated module was designed and developed within a task-based language teaching framework to provide workplace-linguistic support on mobile devices, enabling learners to access the language instruction they needed, when they needed it. The module contents and usability were assessed by high-beginner English proficiency newcomers employed in customer service (n=4) and their volunteer teachers (n=4). Results confirm the overall benefits of using language learning technology in providing instruction that meets participant language needs, ensuring opportunities for individualized training. Implications for designing, implementing, and researching technology-mediated modules are discussed.

This study investigates the relative effectiveness of different teaching approaches on the learning of formulaic sequences. Three comparisons were made in this study: the effects of explicit teaching of formulaic sequences versus teaching embedded in traditional coursebook instruction, the effects of the degree of salience of the sequences in the coursebook on learning, and the effects of explicit teaching of formulaic sequences with context versus teaching without context. Sixty-nine formulaic sequences occurring in an English as a Foreign Language (EFL) coursebook were selected for the study. The participants were 60 low-proficiency university students majoring in technology in Vietnam. Participants were quasi-randomly assigned to one of three groups: control, no-context learning, and sentence-context learning. Learning was measured by two multiple-choice tests of receptive knowledge of form and meaning. Findings indicated that although explicit instruction was effective, the degree of salience in traditional coursebook instruction had no significant effects on learning formulaic sequences. Explicit teaching combined with incidental exposure to formulaic sequences in the coursebook was superior to the traditional coursebook instruction approach in the classroom setting. Furthermore, the results from explicit instruction with context sentences did not differ significantly from those of instruction without context. Explanations for the findings and pedagogical applications are offered.

As engineering programs have begun to infuse sustainability into their undergraduate curricula, assessment tools are needed to further inform these reform efforts. The goal of this project was to demonstrate the use of a new rubric to examine students' abilities to engage in sustainable design. The rubric includes 16 sustainable design criteria and rating scales to capture student performance and instructor/sponsor requirements. Three judges used the rubric to evaluate 40 capstone design projects completed between 2002 and 2011 by civil and environmental engineering seniors at a large, research intensive university. Results indicate that sustainable design performance in the capstone course changed little over 10 years, despite efforts to integrate sustainability into the curriculum. Students tended to address those criteria that were explicitly required of them by instructors or external project sponsors, which were related to social sustainability more than other sustainability dimensions. As a result, more explicit integration of sustainable design requirements into capstone courses may be needed to ensure that students understand the importance of, and are able to practice, developing designs in a sustainable manner.

The Multimedia Principle (Fletcher & Tobias, 2005) states that people learn better and comprehend more when words and pictures are presented together. The potential for English language learners to increase their comprehension of video through the use of captions, which graphically display the same language as the spoken dialogue, has been documented in previous research. However, studies have generally used short videos (Markham & Peter, 2003; Montero Perez, Peters, & Desmet, 2014) or videos designed for language learning (Chung, 1999) rather than episodes of L2 television programs that students are most likely to watch on their own outside of the classroom. The present study aimed to fill this gap by investigating the comprehension of 372 Japanese university students who watched ten 42-minute episodes of an American television program with and without captions. While viewing the episodes, the participants completed comprehension tests. Analysis indicated that although the participants who viewed the episodes with captions had comprehension scores that were slightly higher across all episodes, their scores were only significantly different for three of the ten episodes. The results revealed that captions are likely to aid comprehension when episodes are most difficult. Explanations for the findings and pedagogical applications are offered.

The U.S. Environmental Protection Agency identified the use of low rolling resistance (LRR) tires as an effective method of reducing vehicle fuel consumption, especially from heavyduty vehicles (HDV). LRR tires are important to HDV operations because fuel accounts for about 25% of operating costs, and improving fuel economy also reduces emissions of both greenhouse gases and oxides of nitrogen, a precursor to the formation of ozone, which is harmful to humans, plants, and animals. However, their adoption rate has been slow primarily due to performance uncertainties under real-world operating conditions. Previous mathematical models developed to help fleet operators estimate the impact of LRR tires on their operations have suffered from poor accuracy because they do not account for variable speed profiles in realworld HDV operations. Georgia Tech researchers have developed a new tool for fleet managers that better predicts the benefits of LRR tires under real-world conditions.View the NCST Project Webpage

Comprehension of many types of texts involves constructing meaning from text and pictures. However, research examining how second language (L2) learners process text and pictures and the relationship with comprehension is scarce. Thus, while verbal input is often presented in written and auditory modes simultaneously (i.e., audio of text with simultaneous reading of it), we do not know how the auditory input affects L2 adult learners' processing of text and pictures and its relation to comprehension. In the current study, L2 adult learners and native (L1) adults read and read while listening to an illustrated story while their eye movements were recorded. Immediately after reading, they completed a comprehension test. Results showed that the presence of auditory input allowed learners to spend more time looking at pictures and supported a better integration of text and pictures. No differences were observed between L2 and L1 readers' allocation of attention to text and pictures. Both reading conditions led to similar levels of comprehension. Processing time on the text was positively related to comprehension for L2 readers, while it was associated to lower comprehension for L1 readers. Processing time on images was positively related to comprehension only for L1 readers.

Theories of multimedia learning suggest that learners can form better referential connections when verbal and visual materials are presented simultaneously. Furthermore, the addition of auditory input in reading-while-listening conditions benefits performance on a variety of linguistic tasks. However, little research has been conducted on the processing of multimedia input (written text and images) with and without accompanying audio. Eye movements were recorded during young L2 learners' (N = 30) processing of a multimedia story text in reading-only and reading-while-listening conditions to investigate looking patterns and their relationship with comprehension using a multiple-choice comprehension test. Analysis of the eye-movement data showed that the presence of audio in reading-while-listening conditions allowed learners to look at the image more often. Processing time on text was related to lower levels of comprehension, whereas processing time on images was positively related to comprehension.

Since heavy-duty truck operations can significantly affect traffic congestion, especially on road grade, the creation of exclusive lanes for trucks has been viewed as a potential alternative to reduce congestion delay, fuel consumption, and emissions. However, few studies have rigorously evaluated the effectiveness of truck-only lanes in achieving these benefits. This study demonstrates a model framework that combines a microscopic traffic simulation with emissions and microscale dispersion models to quantify the potential impacts of truck-only lanes on fuel consumption, emissions, and near-road pollutant concentrations. As a case study, the framework was used to evaluate a proposed $2 billion project to construct 40-miles of truck-only lanes on Interstate 75 (I-75) between Atlanta and Macon, Georgia (USA).The findings of this study suggest that truck-only lanes could significantly improve the traffic flow, and reduce energy, emissions, and pollutant concentrations. The research team expects that the extensive simulation results of this study help to understand the performance of truck-only lanes on a large-scale network with a heavy mixture of truck and general purpose lane traffic. The methodology and framework developed in this study can be effectively and efficiently applied to a wide variety of scenarios to evaluate the environmental impacts of other transportation projects under various conditions.View the NCST Project Webpage

The cost of fuel represents a major portion of the costs of operating on-road heavy-duty vehicles (HDV). Over the next couple of decades, the total energy demand from the HDV sector will likely increase due to forecasted growth in freight demand in many global markets, including the United States, and much of this energy will continue to be provided by fossil fuels. Therefore, carbon dioxide emissions from the HDV sector are also expected to increase in the absence of effective mitigating measures to reduce the sectors reliance on fossil fuels. Along with other fuel-saving technologies, the United States Environmental Protection Agency identified the use of Low Rolling Resistance (LRR) tires as an effective method of reducing fuel consumption. It is estimated that LRR tires can improve fuel economy in HDV by about 10 percent. However, adoption of LRR faces many barriers and the most fundamental of these barriers relate to potential performance uncertainties under real-world operating conditions. Previous published decision support tools developed to help fleet operators and other stakeholders estimate the fuel-savings from LRR tires have been found to have limited accuracy due to inherent transient speed profiles in real-world operating cycles.In this study, we develop a tool to predict the fleet-wide fuel-saving benefits from low rolling resistance tires. Unlike previous studies, the developed tool is applicable to both stabilized speed operations and transient speed operations. The tool is based on empirical models that estimate the fuel consumption contribution from tires as a function of vehicle payload, aerodynamic drag, road grade, duration of acceleration, duration of deceleration and, and road facility type (freeway, major arterial, and minor arterial/local road). We limited the scope of the developed tool to tractor-trailers in the U.S. heavy-duty vehicle market, because the United States has the second largest HDV market in the world and tractor-trailers account for the largest share of the market. The tool was developed with data generated by simulating real-world heavy-duty vehicle operating cycles with Autonomie®, the state-of-the-art model for automotive control-system design, and simulating vehicle energy consumption and performance. Autonomie® is a preferred vehicle simulation tool of the United States Department of Energy.The primary purpose of the Tool to Predict Fleet-Wide Heavy-Duty Vehicle Fuel-Saving from Low Rolling Resistance Tires is to assist fleet operators, regulatory agencies, and policy analysts in assessing the fuel consumption savings from low rolling resistance tires. To facilitate ease-of-use by stakeholders, the statistical empirical models are embedded in a Microsoft Excel® spreadsheet. Fleet managers can customize the tool to their specific fleet and the tool is designed to inform fleet operators about the benefits and costs of making low rolling resistance tire investments. In addition to fuel consumption estimates, the spreadsheet tool further estimates related emission reductions. In the future, this tool can be extended to other vehicle segments. The spreadsheet algorithms can also be developed into a web-based computer program in the future to facilitate online use of the tool.The HDV Low Rolling Resistance Tire Fuel and Emission Reduction Calculator is available to download as a spreadsheet tool here: http://transportation.ce.gatech.edu/node/95View the NCST Project Webpage

The Fuel and Emissions Calculator (FEC) is an operating-mode-based, life-cycle energy and emissions modeling tool developed by Georgia Institute of Technology researchers. The primary purpose of the FEC is to assist fleet owners and managers, regulatory agencies, and policy analysts in assessing the energy and emissions impacts of fleet alternatives. The FEC’s modeling approach estimates emissions as a function of engine load, which in turn is a function of vehicle operating parameters, allowing modelers to account for local on-road operating mode conditions as model inputs. The functional modules are embedded in an Excel spreadsheet platform for all current model versions. The open platform allows users to see all input data and every calculation, which makes the model transparent and accessible for most users. With FEC Version 2.0 and Version 3.0, the research team also created an online Python version of the model. The Python version enhances model performance and provides functionality for advanced users who may wish to link the FEC with other modeling tools, such as travel demand or simulation models.The first Fuel and Emissions Calculator (Version 1.0), known as ‘FEC for transit fleets,’ was originally developed by Georgia Tech researchers in 2013-2014 for transit bus, shuttle bus, and rail systems (ORNL and Georgia Tech, 2014). The first major update of the model was finished in 2016 (FEC Version 2.0). FEC Version 2.0 expanded the initial FEC to include a heavy-duty truck calculator and an online Python version of the transit fleet model (transit bus and shuttle bus). Version 2.0 also significantly improved the model performance by incorporating the latest emission rates, advancing the methodologies, and fixing some errors. The research team completed the next major updates in 2018, releasing FEC Version 3.0. Version 3.0 is the “FEC for all transportation modes”, with mode-specific calculators for light-duty passenger vehicles, buses, heavy-duty trucks, and rail. This report first summarizes the FEC Version model’s main features in Version 3.0. The generic methodology that is applied to all transportation modes is introduced in Chapter 2, which includes modules for scenario setting, energy consumption, on-road emission rates, life-cycle assessment, and cost-effectiveness. The model specifications for individual transportation modes are introduced in Chapter 3, where case study examples are provided to help users prepare customized analysis of their own fleets. The key considerations for establishing the online FEC are discussed in Chapter 4. Ongoing work to verify model elements and calibrate the FEC is introduced in Chapter 5. Current research achievements and future work to update and improve the FEC are provided in the final Chapter.View the NCST Project Webpage

The MOtor Vehicle Emission Simulator (MOVES) model was developed by the U.S. Environmental Protection Agency (USEPA) to estimate emissions from on-road and off-road vehicles in the United States. The MOVES model represents a significant improvement over the older MOBILE series of modes, primarily because emission rates are now truly modal in nature. Emission rates are now a function of power surrogates, which depend on speed and acceleration. Traffic simulation model outputs and smartphone GPS data can provide second-by-second vehicle activity data in time and space, including vehicle speed and acceleration. Coupling high-resolution vehicle activity data with appropriate MOVES emission rates further advances research efforts designed to assess the environmental impacts of transportation design and operation strategies. However, the MOVES interface is complicated, and the structure of input variables and algorithms involved in running MOVES to assess operational improvements makes analyses cumbersome and time consuming. The MOVES interface also makes it difficult to assess complicated transportation networks and to undertake analyses of large-scale systems that are dynamic in nature.The MOVES-Matrix system developed by the research team can be used to perform emissions modeling activities in a fraction of the time it takes to perform even one single individual MOVES run. The MOVES-Matrix approach involves running the MOVES model iteratively, across all potential input variable combinations, and using the resulting multidimensional array of pre-run MOVES outputs in emissions modeling. The research team configured MOVES to run on a distributed computing cluster, obtaining MOVES energy consumption and emission rate outputs for each vehicle class, model year, and operating condition, by calendar year, fuel composition (summer, winter, and transition fuels), local Inspection/Maintenance (I/M) program, meteorology, and other variables of interest. The team ran MOVES 146,853 times to generate the on-road emission rate matrices for Atlanta. More than 90 billion emission rates populate the primary output matrix, but implementation tools developed by the team generate matrix subsets for specific applications to speed up the analytical processes. In 2017-2018, the team developed MOVES-Matrix 2.0, which now integrates engine start, soak, evaporative, and truck hoteling emissions. The resulting emission rate matrices allow users to link emission rates to assess big data projects (such as regional emissions for emission inventory development) and to support near-real-time evaluations of changes in emissions for large, dynamic transportation systems. In the case study applications performed by the team, emission rate generation with MOVES-Matrix is 200-times faster than using the batch mode of MOVES graphic user interface in the same computer environment and the process predicts exactly the same emissions result.View the NCST Project Webpage

The goal of this research is to develop a tool that can help students and the public better understand the total cost of owning and operating a personal vehicle over the lifetime of that vehicle, from vehicle purchase to vehicle disposal.  Lifetime vehicle operating cost consists of the vehicle purchase cost, as well as costs that are less obvious to the user (e.g., financing, fuel, maintenance, insurance, etc.).  Underestimating the hidden costs embedded in lifetime vehicle operating cost can lead people to choose vehicles that are unaffordable for their financial situations.  Hence, educating transportation planners, students, and the public about lifetime vehicle operating costs is important.To provide a more personalized calculation approach, the National Center for Sustainable Transportation (NCST) research team developed the Lifetime Vehicle Ownership and Operating Cost Calculator.  The NCST website provides users with data entry flexibility and a clear, intuitive, and interactive user-interface through which users can better understand their vehicle ownership costs.  Vehicle ownership costs are calculated using lookup data and user input for such elements as: purchase price, down payment, interest rates, loan term, annual insurance, maintenance, tire, smog check, parking, tolls, etc.  All lookup data can be modified as desired by the user.The NCST website is designed to provide students and the public with a tool that can be used in research and also at any educational level.  Users can calculate the cent/mile and total cost of vehicle ownership over the life of the vehicle, with cost estimates for each component specifically identified so that users can weigh the impact of each cost element in their vehicle purchase and use decisions.  From an educational perspective, this tool can be used as an instructional aid to specific courses.  For example, the tool has been used in the urban transportation planning course at Georgia Tech, where the students are asked to compare different vehicle choices based on a real-life case study.  In addition, the NCST team plans to introduce this tool to high school consumer economics classes to help students make reasonable vehicle purchase decisions and to assess vehicle operating costs over entire vehicle life cycles.The Lifetime Vehicle Ownership and Operating Cost Calculator is available at http://costcalculator.ce.gatech.edu/.View the NCST Project Webpage

The Fuel and Emissions Calculator (FEC) is an operating-mode-based, life-cycle emissions modeling tool developed by the Georgia Institute of Technology researchers. The primary purpose of the FEC is to assist fleet owners and managers, regulatory agencies, and policy analysts in assessing the energy and emissions impacts of fleet alternatives. The FEC’s modeling approach estimates emissions as a function of engine load, which in turn is a function of vehicle service parameters, allowing modelers to account for local on-road operating mode conditions as model inputs. The functional modules are embedded in an Excel spreadsheet platform for all current model versions. The open platform allows users to see all input data and every calculation, which makes the model transparent and accessible for most users. With Version 2.0 of the model, an online Python version of the model has also been developed. The Python version enhances model performance, and provides functionality for advanced users who may wish to link the FEC with other modeling tools, such as travel demand or simulation models. The first Fuel and Emissions Calculator (Version 1.0), known as ‘FEC for transit fleets,’ was originally developed by Georgia Tech researchers in 2013-2014 for transit bus, shuttle bus and rail systems (ORNL and Georgia Tech, 2014). This report first summarizes the FEC Version 2.0 model’s main features. The generic methodology that is applied to all transportation modes is introduced in Chapter 2, which includes modules for scenario setting, energy consumption, onroad emission rates, life-cycle assessment, and cost-effectiveness. The model specifications for individual transportation modes are introduced in Chapter 3, and case study examples are provided to help users prepare customized analysis for their own fleets. The key considerations for establishing the online FEC are discussed in Chapter 4. Current research achievements and ongoing work to update and improve the FEC are provided in the final Chapter.View the NCST Project Webpage

The Motor Vehicle Emission Simulator (MOVES) model was developed by the US Environmental Protection Agency (USEPA) to estimate emissions from on-road and off-road vehicles in the United States. The MOVES model represents a significant improvement over the older MOBILE series of modes, primarily because emission rates are now much more modal in nature, representing emissions as a function of power surrogates, which depend on speed and acceleration. Traffic simulation model outputs and smartphone GPS data can provide second by-second vehicle activity data in time and space, including vehicle speed and acceleration data. Coupling high-resolution vehicle activity data with appropriate MOVES emission rates further advances research efforts designed to assess the environmental impacts of transportation design and operation strategies. However, the MOVES interface is complicated and the structure of input variables and algorithms involved in running MOVES to assess operational improvements makes analyses cumbersome and time consuming. The MOVES interface also makes it difficult to assess complicated transportation networks and to undertake analyses of large scale systems that are dynamic in nature. The MOVES-Matrix system developed by the research team can be used to perform emissions modeling activities in a fraction of the time compared to running MOVES. The approach involves running the MOVES model iteratively, across all potential input variable combination, and using the resulting multidimensional array of pre-run MOVES outputs in emissions modeling.View the NCST Project Webpage

Eco-driving has significant potential to reduce fuel consumption and emissions from transit operations. Analyses were conducted of 68 thousand miles of real-world operations data from 26 buses, collected from local transit service provided by the Metropolitan Atlanta Rapid Transit Authority (MARTA), and express bus service provided by the Georgia Regional Transportation Authority (GRTA). The analysis utilized second-by-second operations data collected via global positioning system (GPS) devices from buses operated by these transit agencies. The researchers simulated the implementation of transit eco-driving strategies, based on the modal emissions modeling framework employed by the Motor Vehicle Emission Simulator (MOVES) designed to reduce engine load and emissions. This algorithm seeks to minimize fuel consumption by limiting instantaneous vehicle specific power (VSP), while maintaining average speed and conserving total distance. Fuel consumption and fuel-cycle emissions were compared across the monitored driving cycles and their modified eco-driving cycles.The savings from eco-driving were also compared against expected fuel and emissions reductions via conversion of the transit fleets to compressed natural gas (CNG), which is another popular fuel conservation strategy. The transit eco-driving strategy showed a 5% reduction in fuel consumption and fuel cycle greenhouse gas (GHG) emissions for MARTA’s 508-bus fleet (~35% diesel/65% CNG), and a 7% reduction in fuel consumption for GRTA’s 166-bus diesel fleet. The fuel savings translate to about 300,000 gallons of diesel fuel equivalent per year for MARTA and 55,000 gallons of diesel per year for GRTA. Eco-driving was also shown to reduce fuel use and emissions for CNG fleets. Eco-driving training can readily be implemented if speed/acceleration activity is monitored. Because eco-driving does not require significant capital investment it is a potentially very cost-effective strategy for local and express bus transit operations.View the NCST Project Webpage

Florida limestone can be challenging to recover during coring operations, as the rock generally is soft, porous, and often highly weathered. Low recoveries coupled with poor rock quality are common in Florida, which limits the data available for geotechnical design. However, the low recoveries and poor rock quality may be attributable to coring techniques and not the rock's in situ condition. This paper explores integrating measuring while drilling (MWD) into standard coring procedures to provide in situ strength assessment and optimize core recoveries and rock quality to improve rock mass characterization. Six drilling parameters were monitored during the research, and a controlled drilling environment was developed to investigate each monitored parameter's effects. Variable drill bit configurations were also explored to investigate the effects of bit geometry. Interdependent relationships between the drilling parameters were discovered and a new concept of operating within optimal drilling parameter ranges based on these relationships is introduced. The coring practices developed in the controlled environment were then tested in natural Florida limestone. It was concluded that operating within the optimal ranges allows in situ strength assessment and improves core recoveries and rock quality. Key words: measuring while drilling, specific energy, core recovery, rock quality, Florida limestone. Il peut etre difficile de recuperer le calcaire de Floride au cours des operations de carottage, puisque la roche est generalement molle, poreuse et generalement tres alteree. Les faibles taux de recuperation et la mauvaise qualite de la roche sont des phenomenes frequents en Floride, limitant ainsi les donnees dont on dispose pour la conception geotechnique. Toutefois, les faibles taux de recuperation et la mauvaise qualite de la roche sont sans doute attribuables aux techniques de carottage et non a l'etat de la roche in situ. Le present document explore l'integration de la mesure en cours de forage (<< MWD >>) dans les procedures de carottage standard afin de proceder a une evaluation de la resistance in situ et d'optimiser les recuperations de carottes et la qualite de la roche en vue d'ameliorer la caracterisation de la masse rocheuse. Au cours de la recherche, six parametres de forage ont ete surveilles et un environnement de forage controle a ete mis au point pour analyser les effets de chaque parametre surveille. Des configurations variables de trepans ont egalement ete etudiees pour analyser les effets de la geometrie des trepans. On a decouvert des rapports d'interdependance entre les parametres de forage et on a introduit un nouveau concept de fonctionnement dans des plages de parametres de forage optimales introduites dans ces rapports. Par la suite, on a mis a l'essai les pratiques de carottage elaborees dans l'environnement controle dans du calcaire naturel de la Floride. La conclusion a ete etablie selon laquelle l'exploitation dans les plages optimales permet d'evaluer la resistance in situ et d'ameliorer la recuperation des carottes et la qualite de la roche. [Traduit par la Redaction] Mots-cles : mesure en cours de forage, energie specifique, recuperation de carotte, qualite de la roche, calcaire de Floride., Introduction Rock coring within the state of Florida often is challenging because of the soft-porous nature of the bedrock and the high degree of variability and weathering that is found [...]

The National Center for Sustainable Transportation (NCST) seeks to ensure that transportation professionals have the necessary knowledge and skills to design, operate, and maintain sustainable transportation systems. This report provides a structured review of existing sustainable transportation courses to serve as foundation for the development of NCST model curriculum and to support course development elsewhere. This report provides an inventory of transportation-related courses and course concepts. The report also surveys models and modeling support tools, current research topics, and industry job descriptions to identify room for growth in sustainable transportation curricula.View the NCST's Curriculum & Courses