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Driving cycles are acting as the basis of the evaluation of the vehicle performance from air quality point of view, such as fuel consumption or pollutant emission, especially in emission modeling and emission estimation. The original definition of the driving cycle, or drive schedule, given by U.S. Environmental Protection Agency (EPA), is basically a speed-time trajectory which is able to describe the general driving characteristics and driving patterns. Therefore, the development of drive cycles requires a large amount of real data to realize such “generalization”. Then, with such the eligible data collected, it leads to the development of modeling, from traffic modeling to emission modeling, especially for those pollutant emissions which have the public concern. In this study, focused on heavy duty diesel vehicles (HDDVs), the estimations of the common emissions are being made based on the Texas specific drive cycles, in second-by-second form, collected and generated from five local metropolitan areas, including Houston, Austin, San Antonio, Dallas-Fort Worth and El Paso. First of all, the accurate Global Positioning System (GPS) logging technique is applied for data collection in order to collect not only the moving data but also the relevant geographical information, such as location and roadway, for further analysis. Then, during the progress of data cleaning and data processing, some modifications are made subjectively to improve the deficits of the general methodologies developed by EPA. Afterwards, the specific drive cycles are presented in the format of operating mode distributions, which are also the main part of the input during the emission estimation in Motor Vehicle Emission Simulator (MOVES). Along with all the Texas specific inputs prepared, both the rates and amount of studied emissions are estimated through MOVES. A further comparison is made between the emission rates of default analysis and local analysis to verify the accuracy of MOVES at project level. It is found that the default estimation made by MOVES is accurate for mid-speed cases, at magnitude level. Significant differences happened in low-speed cases and high-speed cases, in which it shows the importance to develop the local drive cycles when estimating the emission rates regionally.

Uncertainty has a large effect on projects in the oil and gas industry, because most aspects of project evaluation rely on estimates. Industry routinely underestimates uncertainty, often significantly. The tendency to underestimate uncertainty is nearly universal. The cost associated with underestimating uncertainty, or overconfidence, can be substantial. Studies have shown that moderate overconfidence and optimism can result in expected portfolio disappointment of more than 30%. It has been shown that uncertainty can be assessed more reliably through look-backs and calibration, i.e., comparing actual results to probabilistic predictions over time. While many recognize the importance of look-backs, calibration is seldom practiced in industry. I believe a primary reason for this is lack of systematic processes and software for calibration. The primary development of my research is a database application that provides a way to track probabilistic estimates and their reliability over time. The Brier score and its components, mainly calibration, are used for evaluating reliability. The system is general in the types of estimates and forecasts that it can monitor, including production, reserves, time, costs, and even quarterly earnings. Forecasts may be assessed visually, using calibration charts, and quantitatively, using the Brier score. The calibration information can be used to modify probabilistic estimation and forecasting processes as needed to be more reliable. Historical data may be used to externally adjust future forecasts so they are better calibrated. Three experiments with historical data sets of predicted vs. actual quantities, e.g., drilling costs and reserves, are presented and demonstrate that external adjustment of probabilistic forecasts improve future estimates. Consistent application of this approach and database application over time should improve probabilistic forecasts, resulting in improved company and industry performance.

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