Virginia Tech Transportation Institute

Abstract. A new inventory of air pollutant emissions in Asia in the year 2006 is developed to support the Intercontinental Chemical Transport Experiment-Phase B (INTEX-B) funded by the National Aeronautics and Space Administration (NASA). Emissions are estimated for all major anthropogenic sources, excluding biomass burning. We estimate total Asian anthropogenic emissions in the year 2006 as follows: 47.1 Tg SO2, 36.7 Tg NOx, 298.2 Tg CO, 54.6 Tg NMVOC, 29.2 Tg PM10, 22.2 Tg PM2.5, 2.97 Tg BC, and 6.57 Tg OC. We emphasize emissions from China because they dominate the Asia pollutant outflow to the Pacific and the increase of emissions from China since 2000 is of great concern. We have implemented a series of improved methodologies to gain a better understanding of emissions from China, including a detailed technology-based approach, a dynamic methodology representing rapid technology renewal, critical examination of energy statistics, and a new scheme of NMVOC speciation for model-ready emissions. We estimate China's anthropogenic emissions in the year 2006 to be as follows: 31.0 Tg SO2, 20.8 Tg NOx, 166.9 Tg CO, 23.2 Tg NMVOC, 18.2 Tg PM10, 13.3 Tg PM2.5, 1.8 Tg BC, and 3.2 Tg OC. We have also estimated 2001 emissions for China using the same methodology and found that all species show an increasing trend during 2001–2006: 36% increase for SO2, 55% for NOx, 18% for CO, 29% for VOC, 13% for PM10, and 14% for PM2.5, BC, and OC. Emissions are gridded at a resolution of 30 min×30 min and can be accessed at our web site (http://mic.greenresource.cn/intex-b2006).

Solid evidence of virtual reality's benefits has graduated from impressive visual demonstrations to producing results in practical applications. Further, a realistic experience is no longer immersion's sole asset. Empirical studies show that various components of immersion provide other benefits - full immersion is not always necessary. The goal of immersive virtual environments (VEs) was to let the user experience a computer-generated world as if it were real - producing a sense of presence, or "being there," in the user's mind.

BACKGROUND: Distracted driving attributable to the performance of secondary tasks is a major cause of motor vehicle crashes both among teenagers who are novice drivers and among adults who are experienced drivers. METHODS: We conducted two studies on the relationship between the performance of secondary tasks, including cell-phone use, and the risk of crashes and near-crashes. To facilitate objective assessment, accelerometers, cameras, global positioning systems, and other sensors were installed in the vehicles of 42 newly licensed drivers (16.3 to 17.0 years of age) and 109 adults with more driving experience. RESULTS: During the study periods, 167 crashes and near-crashes among novice drivers and 518 crashes and near-crashes among experienced drivers were identified. The risk of a crash or near-crash among novice drivers increased significantly if they were dialing a cell phone (odds ratio, 8.32; 95% confidence interval [CI], 2.83 to 24.42), reaching for a cell phone (odds ratio, 7.05; 95% CI, 2.64 to 18.83), sending or receiving text messages (odds ratio, 3.87; 95% CI, 1.62 to 9.25), reaching for an object other than a cell phone (odds ratio, 8.00; 95% CI, 3.67 to 17.50), looking at a roadside object (odds ratio, 3.90; 95% CI, 1.72 to 8.81), or eating (odds ratio, 2.99; 95% CI, 1.30 to 6.91). Among experienced drivers, dialing a cell phone was associated with a significantly increased risk of a crash or near-crash (odds ratio, 2.49; 95% CI, 1.38 to 4.54); the risk associated with texting or accessing the Internet was not assessed in this population. The prevalence of high-risk attention to secondary tasks increased over time among novice drivers but not among experienced drivers. CONCLUSIONS: The risk of a crash or near-crash among novice drivers increased with the performance of many secondary tasks, including texting and dialing cell phones. (Funded by the Eunice Kennedy Shriver National Institute of Child Health and Human Development and the National Highway Traffic Safety Administration.).

The technologies and practices that have enabled the recent boom in shale gas production have also brought attention to the environmental impacts of its use. It has been debated whether the fugitive methane emissions during natural gas production and transmission outweigh the lower carbon dioxide emissions during combustion when compared to coal and petroleum. Using the current state of knowledge of methane emissions from shale gas, conventional natural gas, coal, and petroleum, we estimated up-to-date life-cycle greenhouse gas emissions. In addition, we developed distribution functions for key parameters in each pathway to examine uncertainty and identify data gaps such as methane emissions from shale gas well completions and conventional natural gas liquid unloadings that need to be further addressed. Our base case results show that shale gas life-cycle emissions are 6% lower than conventional natural gas, 23% lower than gasoline, and 33% lower than coal. However, the range in values for shale and conventional gas overlap, so there is a statistical uncertainty whether shale gas emissions are indeed lower than conventional gas. Moreover, this life-cycle analysis, among other work in this area, provides insight on critical stages that the natural gas industry and government agencies can work together on to reduce the greenhouse gas footprint of natural gas.

Since the United States began a programme to develop ethanol as a transportation fuel, its use has increased from 175 million gallons in 1980 to 4.9 billion gallons in 2006. Virtually all of the ethanol used for transportation has been produced from corn. During the period of fuel ethanol growth, corn farming productivity has increased dramatically, and energy use in ethanol plants has been reduced by almost by half. The majority of corn ethanol plants are powered by natural gas. However, as natural gas prices have skyrocketed over the last several years, efforts have been made to further reduce the energy used in ethanol plants or to switch from natural gas to other fuels, such as coal and wood chips. In this paper, we examine nine corn ethanol plant types—categorized according to the type of process fuels employed, use of combined heat and power, and production of wet distiller grains and solubles. We found that these ethanol plant types can have distinctly different energy and greenhouse gas emission effects on a full fuel-cycle basis. In particular, greenhouse gas emission impacts can vary significantly—from a 3% increase if coal is the process fuel to a 52% reduction if wood chips are used. Our results show that, in order to achieve energy and greenhouse gas emission benefits, researchers need to closely examine and differentiate among the types of plants used to produce corn ethanol so that corn ethanol production would move towards a more sustainable path.

This paper addresses the environmental burdens (energy consumption and air emissions, including greenhouse gases, GHGs) of the material production, assembly, and recycling of automotive lithium-ion batteries in hybrid electric, plug-in hybrid electric, and battery electric vehicles (BEV) that use LiMn(2)O(4) cathode material. In this analysis, we calculated the energy consumed and air emissions generated when recovering LiMn(2)O(4), aluminum, and copper in three recycling processes (hydrometallurgical, intermediate physical, and direct physical recycling) and examined the effect(s) of closed-loop recycling on environmental impacts of battery production. We aimed to develop a U.S.-specific analysis of lithium-ion battery production and in particular sought to resolve literature discrepancies concerning energy consumed during battery assembly. Our analysis takes a process-level (versus a top-down) approach. For a battery used in a BEV, we estimated cradle-to-gate energy and GHG emissions of 75 MJ/kg battery and 5.1 kg CO(2)e/kg battery, respectively. Battery assembly consumes only 6% of this total energy. These results are significantly less than reported in studies that take a top-down approach. We further estimate that direct physical recycling of LiMn(2)O(4), aluminum, and copper in a closed-loop scenario can reduce energy consumption during material production by up to 48%.

This paper adopts different supervised learning methods from the field of machine learning to develop multiclass classifiers that identify the transportation mode, including driving a car, riding a bicycle, riding a bus, walking, and running. Methods that were considered include K-nearest neighbor, support vector machines (SVMs), and tree-based models that comprise a single decision tree, bagging, and random forest (RF) methods. For training and validating purposes, data were obtained from smartphone sensors, including accelerometer, gyroscope, and rotation vector sensors. K-fold cross-validation as well as out-of-bag error was used for model selection and validation purposes. Several features were created from which a subset was identified through the minimum redundancy maximum relevance method. Data obtained from the smartphone sensors were found to provide important information to distinguish between transportation modes. The performance of different methods was evaluated and compared. The RF and SVM methods were found to produce the best performance. Furthermore, an effort was made to develop a new additional feature that entails creating a combination of other features by adopting a simulated annealing algorithm and a random forest method.

OBJECTIVE: The objective of this paper was to outline an explanatory framework for understanding effects of cognitive load on driving performance and to review the existing experimental literature in the light of this framework. BACKGROUND: Although there is general consensus that taking the eyes off the forward roadway significantly impairs most aspects of driving, the effects of primarily cognitively loading tasks on driving performance are not well understood. METHOD: Based on existing models of driver attention, an explanatory framework was outlined. This framework can be summarized in terms of the cognitive control hypothesis: Cognitive load selectively impairs driving subtasks that rely on cognitive control but leaves automatic performance unaffected. An extensive literature review was conducted wherein existing results were reinterpreted based on the proposed framework. RESULTS: It was demonstrated that the general pattern of experimental results reported in the literature aligns well with the cognitive control hypothesis and that several apparent discrepancies between studies can be reconciled based on the proposed framework. More specifically, performance on nonpracticed or inherently variable tasks, relying on cognitive control, is consistently impaired by cognitive load, whereas the performance on automatized (well-practiced and consistently mapped) tasks is unaffected and sometimes even improved. CONCLUSION: Effects of cognitive load on driving are strongly selective and task dependent. APPLICATION: The present results have important implications for the generalization of results obtained from experimental studies to real-world driving. The proposed framework can also serve to guide future research on the potential causal role of cognitive load in real-world crashes.

Naturalistic driving is an innovative method for investigating driver behavior and traffic safety. However, as the number of crashes observed in naturalistic driving studies is typically small, crash surrogates are needed. A study evaluated the use of near crashes as a surrogate measure for assessment of the safety impact of driver behaviors and other risk factors. Two metrics, the precision and bias of risk estimation, were used to assess whether near crashes could be combined with crashes. The principles and exact conditions for improved precision and unbiased estimation were proposed and applied to data from the 100-Car Naturalistic Driving Study. The analyses indicated that a positive relationship exists between the frequencies of contributing factors for crashes and for near crashes. The study also indicated that analyses based on combined crash and near-crash data consistently underestimate the risk of contributing factors compared to use of crash data alone. At the same time, the precision of the estimation will increase. This consistent pattern allows investigators to identify true high-risk behaviors while qualitatively assessing potential bias. In summary, the study concluded that the use of near crashes as a crash surrogate provides definite benefit when naturalistic studies are not large enough to generate sufficient numbers of crashes for statistical analysis.

We estimated the effects on per-vehicle-mile fuel-cycle petroleum use, greenhouse gas (GHG) emissions, and energy use of using ethanol blended with gasoline in a mid-size passenger car, compared with the effects of using gasoline in the same car. Our analysis includes petroleum use, energy use, and emissions associated with chemicals manufacturing, farming of corn and biomass, ethanol production, and ethanol combustion for ethanol; and petroleum use, energy use, and emissions associated with petroleum recovery, petroleum refining, and gasoline combustion for gasoline. For corn-based ethanol, the key factors in determining energy and emissions impacts include energy and chemical usage intensity of corn farming, energy intensity of the ethanol plant, and the method used to estimate energy and emissions credits for co-products of corn ethanol. The key factors in determining the impacts of cellulosic ethanol are energy and chemical usage intensity of biomass farming, ethanol yield per dry ton of biomass, and electricity credits in cellulosic ethanol plants. The results of our fuel-cycle analysis for fuel ethanol are listed below. Note that, in the first half of this summary, the reductions cited are per-vehicle-mile traveled using the specified ethanol/gasoline blend instead of conventional (not reformulated) gasoline. The second half of the summary presents estimated changes per gallon of ethanol used in ethanol blends. GHG emissions are global warming potential (GWP)-weighted, carbon dioxide (CO2)-equivalent emissions of CO2, methane (CH4), and nitrous oxide (N2O).

OBJECTIVE: This article provides a review of empirical studies of automated vehicle takeovers and driver modeling to identify influential factors and their impacts on takeover performance and suggest driver models that can capture them. BACKGROUND: Significant safety issues remain in automated-to-manual transitions of vehicle control. Developing models and computer simulations of automated vehicle control transitions may help designers mitigate these issues, but only if accurate models are used. Selecting accurate models requires estimating the impact of factors that influence takeovers. METHOD: Articles describing automated vehicle takeovers or driver modeling research were identified through a systematic approach. Inclusion criteria were used to identify relevant studies and models of braking, steering, and the complete takeover process for further review. RESULTS: The reviewed studies on automated vehicle takeovers identified several factors that significantly influence takeover time and post-takeover control. Drivers were found to respond similarly between manual emergencies and automated takeovers, albeit with a delay. The findings suggest that existing braking and steering models for manual driving may be applicable to modeling automated vehicle takeovers. CONCLUSION: Time budget, repeated exposure to takeovers, silent failures, and handheld secondary tasks significantly influence takeover time. These factors in addition to takeover request modality, driving environment, non-handheld secondary tasks, level of automation, trust, fatigue, and alcohol significantly impact post-takeover control. Models that capture these effects through evidence accumulation were identified as promising directions for future work. APPLICATION: Stakeholders interested in driver behavior during automated vehicle takeovers may use this article to identify starting points for their work.

Despite an abundant use of the term “Out of the loop” (OOTL) in the context of automated driving and human factors research, there is currently a lack of consensus on its precise definition, how it can be measured, and the practical implications of being in or out of the loop during automated driving. The main objective of this paper is to consider the above issues, with the goal of achieving a shared understanding of the OOTL concept between academics and practitioners. To this end, the paper reviews existing definitions of OOTL and outlines a set of concepts, which, based on the human factors and driver behaviour literature, could serve as the basis for a commonly-agreed definition. Following a series of working group meetings between representatives from academia, research institutions and industrial partners across Europe, North America, and Japan, we suggest a precise definition of being in, out, and on the loop in the driving context. These definitions are linked directly to whether or not the driver is in physical control of the vehicle, and also the degree of situation monitoring required and afforded by the driver. A consideration of how this definition can be operationalized and measured in empirical studies is then provided, and the paper concludes with a short overview of the implications of this definition for the development of automated driving functions.

As the automotive industry moves toward the car of the future, technology companies are developing cutting-edge systems, in vehicle and out, that aim to make driving safer, more pleasant, and more convenient. While we are already seeing some successful video-based augmented reality (AR) auxiliary displays (e.g., center-mounted backup aid systems), the application opportunities of optical see-through AR as presented on a drivers' windshield are yet to be fully tapped; nor are the visual perceptual and attention challenges fully understood. As we race to field AR applications in transportation, we should first consider the perceptual and distraction issues that are known in both the AR and transportation communities, with a focus on the unique and intersecting aspects for driving applications. This paper describes the some opportunities and driver challenges associated with AR applications in the automotive domain. We first present a basic research space to assist in these inquiries, which delineates head-mounted from heads-up and center-mounted displays; video from optical see-through displays; and world-fixed from screen-fixed AR graphics. We then address benefits of AR related to primary, secondary, and tertiary driver tasks as well as driver perception and cognition challenges inherent in automotive AR systems.

Algae biomass is an attractive biofuel feedstock when grown with high productivity on marginal land. Hydrothermal liquefaction (HTL) produces more oil from algae than lipid extraction (LE) does because protein and carbohydrates are converted, in part, to oil. Since nitrogen in the algae biomass is incorporated into the HTL oil, and since lipid extracted algae for generating heat and electricity are not co-produced by HTL, there are questions regarding implications for emissions and energy use. We studied the HTL and LE pathways for renewable diesel (RD) production by modeling all essential operations from nutrient manufacturing through fuel use. Our objective was to identify the key relationships affecting HTL energy consumption and emissions. LE, with identical upstream growth model and consistent hydroprocessing model, served as reference. HTL used 1.8 fold less algae than did LE but required 5.2 times more ammonia when nitrogen incorporated in the HTL oil was treated as lost. HTL RD had life cycle emissions of 31,000 gCO2 equivalent (gCO2e) compared to 21,500 gCO2e for LE based RD per million BTU of RD produced. Greenhouse gas (GHG) emissions increased when yields exceeded 0.4 g HTL oil/g algae because insufficient carbon was left for biogas generation. Key variables in the analysis were the HTL oil yield, the hydrogen demand during upgrading, and the nitrogen content of the HTL oil. Future work requires better data for upgrading renewable oils to RD and requires consideration of nitrogen recycling during upgrading.

This report documents the development and use of the most recent version (Version 1.5) of the Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation (GREET) model. The model, developed in a spreadsheet format, estimates the full fuel-cycle emissions and energy associated with various transportation fuels and advanced vehicle technologies for light-duty vehicles. The model calculates fuel-cycle emissions of five criteria pollutants (volatile organic compounds, carbon monoxide, nitrogen oxides, particulate matter with diameters of 10 micrometers or less, and sulfur oxides) and three greenhouse gases (carbon dioxide, methane, and nitrous oxide). The model also calculates total energy consumption, fossil fuel consumption, and petroleum consumption when various transportation fuels are used. The GREET model includes the following cycles: petroleum to conventional gasoline, reformulated gasoline, conventional diesel, reformulated diesel, liquefied petroleum gas, and electricity via residual oil; natural gas to compressed natural gas, liquefied natural gas, liquefied petroleum gas, methanol, Fischer-Tropsch diesel, dimethyl ether, hydrogen, and electricity; coal to electricity; uranium to electricity; renewable energy (hydropower, solar energy, and wind) to electricity; corn, woody biomass, and herbaceous biomass to ethanol; soybeans to biodiesel; flared gas to methanol, dimethyl ether, and Fischer-Tropsch diesel; and landfill gases to methanol. This report also presents the results of the analysis of fuel-cycle energy use and emissions associated with alternative transportation fuels and advanced vehicle technologies to be applied to passenger cars and light-duty trucks.

We present an image processing algorithm customized for high-speed, real-time inspection of pavement cracking. In the algorithm, a pavement image is divided into grid cells of 8×8 pixels, and each cell is classified as a noncrack or crack cell using the grayscale information of the border pixels. Whether a crack cell can be regarded as a basic element (or seed) depends on its contrast to the neighboring cells. A number of crack seeds can be called a crack cluster if they fall on a linear string. A crack cluster corresponds to a dark strip in the original image that may or may not be a section of a real crack. Additional conditions to verify a crack cluster include the requirements in the contrast, width, and length of the strip. If verified crack clusters are oriented in similar directions, they will be joined to become one crack. Because many operations are performed on crack seeds rather than on the original image, crack detection can be executed simultaneously when the frame grabber is forming a new image, permitting real-time, online pavement surveys. The trial test results show a good repeatability and accuracy when multiple surveys were conducted at different driving conditions.

Different techniques for measuring pavement surface macrotexture and their application in pavement management are discussed. The main applications of surface macrotexture are to measure the frictional properties of the pavement surface and to detect hot-mix asphalt (HMA) construction segregation or nonuniformity. Since surface macro-texture can be measured quite efficiently using noncontact technologies and provides important information regarding pavement safety and HMA construction quality, this parameter may be included in the quality assurance or control procedures. Correlations between different measuring devices were investigated utilizing different HMA wearing surfaces. Excellent correlation was found between the circular track meter and sand patch measurements. In addition, the macrotexture determined using a laser profiler correlates well with that determined with sand patch measurements. Consistent with previous studies, it was found that the skid number gradient with speed is inversely proportional to the pavement macrotexture. However, there was a noticeable difference in speed dependency when smooth and ribbed tires were used. Oscillations in the percent normalized gradient with time due to seasonal variations were also observed. Macrotexture measurements hold great promise as tools to detect and quantify segregation for quality assurance purposes. A standard construction specification was proposed in a recent NCHRP study. However, the equation proposed for computing the nonsegregated estimated (mean) texture depth could not be applied to the mixes studied. An alternative equation has been proposed, which estimates the surface macrotexture using the mix nominal maximum size and voids in the mineral aggregate. The study was based on the mixes used at the Virginia Smart Road. Further investigation using other mixes is recommended.

During this study a methodology was developed to project growth trends of the motor vehicle population and associated oil demand and carbon dioxide (CO 2 ) emissions in China through 2050. In particular, the numbers of highway vehicles, motorcycles, and rural vehicles were projected under three scenarios of vehicle growth by following different patterns of motor vehicle growth in Europe and Asia. Projections showed that by 2030 China could have more highway vehicles than the United States has today. Three scenarios of vehicle fuel economy were also developed on the basis of current and future policy efforts to reduce vehicle fuel consumption in China and in developed countries. With the vehicle population projections and potential vehicle fuel economy data, it was projected that in 2050 China's on-road vehicles could consume approximately 614 million to 1,016 million metric tons of oil (or 12.4 million to 20.6 million barrels per day) and emit 1.9 billion to 3.2 billion metric tons (or 2.1 billion to 3.5 billion tons) of CO 2 each year. Although these projections by no means imply what will happen in the Chinese transportation sector by 2050, they do demonstrate that an uncontained growth in motor vehicles and only incremental efforts to improve fuel economy will certainly result in severe consequences for oil use and CO 2 emissions in China.

This study characterized driver behavior and established a foundation for defining functional performance requirements associated with a Limited Ability Autonomous Driving System (LAADS) – a system capable of automated steering and speed/headway maintenance tasks on freeways, but does not relieve drivers of all driving tasks. The research was designed to examine and reveal potential issues associated with the use of semi-autonomous systems, exploring impacts on willingness to engage in secondary non-driving related tasks, and driver allocation of visual attention while operating under LAADS (ACC and Lane Centering). Results found meaningful differences in the allocation of visual attention across ACC and LAADS driving under situations where drivers were engaged in a secondary task. Overall findings suggest that given a rudimentary, but reliable, LAADS system (one which does not monitor or otherwise restrict behavior) drivers are likely to increase the frequency of secondary task interactions, and engage in risky tasks that are likely to increase extended glances away from the forward roadway.

Background: Driver distraction is a major contributing factor to crashes, which are the leading cause of death for the US population under 35 years of age. The prevalence of secondary-task engagement and its impacts on distraction and crashes may vary substantially by driver age. Methods: Driving performance and behaviour data were collected continuously using multiple cameras and sensors in situ for 3542 participant drivers recruited for up to 3 years for the Second Strategic Highway Research Program Naturalistic Driving Study. Secondary-task engagement at the onset of crashes and during normal driving segments was identified from videos. A case-cohort approach was used to estimate the crash odds ratios associated with, and the prevalence of, secondary tasks for four age groups: 16-20, 21-29, 30-64 and 65-98 years of age. Only severe crashes (property damage and higher severity) were included in the analysis. Results: Secondary-task-induced distraction posed a consistently higher threat for drivers younger than 30 and above 65 when compared with middle-aged drivers, although senior drivers engaged in secondary tasks much less frequently than their younger counterparts. Secondary tasks with high visual-manual demand (e.g. visual-manual tasks performed on cell phones) affected drivers of all ages. Certain secondary tasks, such as operation of in-vehicle devices and talking/singing, increased the risk for only certain age groups. Conclusions: Teenaged, young adult drivers and senior drivers are more adversely impacted by secondary-task engagement than middle-aged drivers. Visual-manual distractions impact drivers of all ages, whereas cognitive distraction may have a larger impact on young drivers.