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Development of a fuel economy model for light-duty and heavy-duty vehicles is part of the Texas Department of Transportation’s “Estimating Texas Motor Vehicle Operating Costs” project. A literature review for models that could be used to predict the fuel economy of light-duty and heavy-duty vehicles resulted in selection of coastdown coefficients to simulate the combined effects of aerodynamic drag and tire rolling resistance. For light-duty vehicles, advantage can be taken of the modeling data provided by the United States Environmental Protection Agency (EPA) for adjusting chassis dynamometers to allow accurate determination of emissions and fuel economy so that compliance with emissions standards and Corporate Average Fuel Economy (CAFE) regulations can be assessed. Initially, EPA provided vehicle-specific data that were relevant to a physics-based model of the forces at the tire-road interface. Due to some limitations of these model parameters, EPA now provides three vehicle-specific coefficients obtained from vehicle coastdown data. These coefficients can be related back to the original physics-based model of the forces at the tire-road interface, but not in a manner that allows the original modeling parameters to be extracted from the coastdown coefficients. Nevertheless, as long as the operation of a light-duty vehicle does not involve extreme acceleration or deceleration transients, the coefficients available from the EPA can be used to accurately predict fuel economy. Manufacturers of heavy-duty vehicles are not required to meet any sort of CAFE standards, and the engines used in heavy-duty vehicles, rather than the vehicles themselves, are tested (using an engine dynamometer) to determine compliance with emissions standards. Therefore, EPA provides no data that could be useful for predicting the fuel economy of heavy-duty vehicles. Therefore, it is necessary to perform heavyduty coastdown tests in order to predict fuel economy, and use these tests to develop vehicle-specific coefficients for the force at the tire-road interface. Given these coefficients, the fuel economy of a heavy-duty vehicle can be calculated for any driving schedule. The heavy-duty vehicle model developed for this project is limited to pre-2007 calendar year heavy-duty vehicles due to the adverse effects of emissions components that were necessary to comply with emissions standards that went into effect January 2007. / text

Aircraft with advanced wing geometry, like the flying wing or blended wing body configuration, seems to be the seed candidate of future aircraft. Compared with conventional aircraft, there are significant aerodynamic performance improvements because of its highly integrated wing and fuselage configuration. On the other hand, due to its tailless configuration, the stability characteristics are not as good as conventional aircraft. The research aims to compare the aerodynamic and stability characteristics of conventional, flying wing and blended wing body aircraft. Based on the same requirement—250 passenger capability and 7,500 nautical miles range, three different configurations—conventional, flying wing and blended wing body options were provided to make direct comparison. The research contains four parts. In the first part, the aerodynamic characteristics were compared using empirical equation ESDU datasheet and Vortex-Lattice Method based AVL software. In the second part, combined with the aerodynamic data and output mass data from other team member, the stability characteristics were analysed. The stability comparison contains longitudinal, lateral-directional static stability and dynamic stability. In the third part, several geometry parameters were varied to investigate the influence on the aerodynamic and stability characteristics of blended wing body configuration. In the last part, a special case has been explored in an attempt to improve the static stability by changing geometry parameters. The process shows that the design of blended wing body is really complex since the closely coupling of several parameters.

Research has identified the importance of academic vocabulary (e.g., Corson, 1997; Gardner, 2013; Hsueh-chao & Nation, 2000). In turn, many researchers have focused on identifying the most frequent and salient words present in academic texts across registers and presenting these words in lists, such as The Academic Word List (AWL) (Coxhead, 2000). Gardner and Davies (2014), recognizing the limitations of the AWL, have developed a new list known as The Academic Vocabulary List (AVL). This present study examines the appearance of the 570 AWL word families and the top 570 AVL word families in the Academic Textbook Corpus (ATC) – a 1.9-million-word corpus created from three middle school, three high school, and three college level textbooks from the disciplines of American history, mathematics, and physical sciences. The study determined (1) word families from both the AWL and the AVL found in the ATC, (2) words families unique to the AWL in the ATC, (3) word families unique to the AVL in the ATC, and (4) characteristic differences between the AWL and AVL unique word families. The results suggest that the AWL and AVL capture high frequency academic word families that are salient across a variety of academic disciplines and grade levels, but the AVL provides a greater number of unique frequent core academic word families.

The implementation of automatic transit data collection via Automatic Vehicle Location (AVL) and Automatic Passenger Counting (APC) systems provides an opportunity to create large, detailed datasets of transit operations. These datasets are valuable because they provide an opportunity to evaluate and optimize transit operations using methods that were previously infeasible and without the need for expensive manual data collection. This thesis develops a methodology to utilize data collected by typical AVL/APC system installations in order to (a) develop advanced performance measures to quantify schedule adherence and (b) automatically determine the causes of poor schedule adherence. The methodology addresses the difficulty that many small to medium sized transit agencies have in utilizing the data being collected by proposing a methodology that can be automated, thereby reducing resource and expertise requirements and allowing the data to be more effectively utilized. The ultimate output of the proposed methodology includes the following: 1. A ranked list of routes by direction (for a given time period) that identifies routes with the poorest schedule adherence performance. 2. Performance measures within any given route, direction, and time period that identify which timepoints are contributing most to poor schedule adherence. 3. Statistics indicating identified causes of poor schedule adherence at individual timepoints. 4. A visualization aid to be used in conjunction with the cause statistics generated in Step 3 in order to develop an effective strategy for improving schedule adherence issues. With this information, transit agencies will be able to act proactively to improve their transit system, rather than wait until they discover problems on their own or hear complaints from passengers and drivers. The methodology is tested and demonstrated through application to AVL/APC system data from Grand River Transit, a public transit agency serving Waterloo Region in Ontario, Canada.

The implementation of automatic transit data collection via Automatic Vehicle Location (AVL) and Automatic Passenger Counting (APC) systems provides an opportunity to create large, detailed datasets of transit operations. These datasets are valuable because they provide an opportunity to evaluate and optimize transit operations using methods that were previously infeasible and without the need for expensive manual data collection. This thesis develops a methodology to utilize data collected by typical AVL/APC system installations in order to (a) develop advanced performance measures to quantify schedule adherence and (b) automatically determine the causes of poor schedule adherence. The methodology addresses the difficulty that many small to medium sized transit agencies have in utilizing the data being collected by proposing a methodology that can be automated, thereby reducing resource and expertise requirements and allowing the data to be more effectively utilized. The ultimate output of the proposed methodology includes the following: 1. A ranked list of routes by direction (for a given time period) that identifies routes with the poorest schedule adherence performance. 2. Performance measures within any given route, direction, and time period that identify which timepoints are contributing most to poor schedule adherence. 3. Statistics indicating identified causes of poor schedule adherence at individual timepoints. 4. A visualization aid to be used in conjunction with the cause statistics generated in Step 3 in order to develop an effective strategy for improving schedule adherence issues. With this information, transit agencies will be able to act proactively to improve their transit system, rather than wait until they discover problems on their own or hear complaints from passengers and drivers. The methodology is tested and demonstrated through application to AVL/APC system data from Grand River Transit, a public transit agency serving Waterloo Region in Ontario, Canada.

Automatic Vehicle Location (AVL) and Automatic Passenger Counting (APC) systems can be powerful tools for transit agencies to archive large, detailed quantities of transit operations data. Managing data quality is an important first step for exploiting these rich datasets. This thesis presents an automated quality assurance (QA) methodology that identifies unreliable archived AVL/APC data. The approach is based on expected travel and passenger activity patterns derived from the data. It is assumed that standard passenger balancing and schedule matching algorithms are applied to the raw AVL/APC data along with any existing automatic validation programs. The proposed QA methodology is intended to provide transit agencies with a supplementary tool to manage data quality that complements, but does not replace, conventional processing routines (that can be vendor-specific and less transparent). The proposed QA methodology endeavours to flag invalid data as “suspect” and valid data as “non-suspect”. There are three stages: i) the first stage screens data that demonstrate a violation of physical constraints; ii) the second stage looks for data that represent outliers; and iii) the third stage evaluates whether the outlier data can be accounted for with valid or invalid pattern. Stop-level tests are mathematically defined for each stage; however data is filtered at the trip-level. Data that do not violate any physical constraints and do not represent any outliers are considered valid trip data. Outlier trips that may be accounted for with a valid outlier pattern are also considered valid. The remaining trip data is considered suspect. The methodology is applied to a sample set of AVL/APC data from Grand River Transit in the Region of Waterloo, Ontario, Canada. The sample data consist of 4-month’s data from September to December of 2008; it is comprised of 612,000 stop-level records representing 25,012 trips. The results show 14% of the trip-level data is flagged as suspect for the sample dataset. The output is further dissected by: reviewing which tests most contribute to the set of suspect trips; confirming the pattern assumptions for the valid outlier cases; and comparing the sample data by various traits before and after the QA methodology is applied. The latter task is meant to recognize characteristics that may contribute to higher or lower quality data. Analysis shows that the largest portion of suspect trips, for this sample set, suggests the need for improved passenger balancing algorithms or greater accuracy of the APC equipment. The assumptions for valid outlier case patterns were confirmed to be reasonable. It was found that poor schedule data contributes to poorer quality in AVL-APC data. An examination of data distribution by vehicle showed that usage and the portion of suspect data varied substantially between vehicles. This information can be useful in the development of maintenance plans and sampling plans (when combined with information of data distribution by route). A sensitivity analysis was conducted along with an impact analysis on downstream data uses. The model was found to be sensitive to three of the ten user-defined parameters. The impact of the QA procedure on network-level measures of performance (MOPs) was not found to be significant, however the impact was shown to be more substantial for route-specific MOPs.

The travel times of public transit systems that operate on mixed use right-of-ways are often dictated by the delays experienced at signalized intersections. When these delays become large and/or highly variable, transit quality degrades and agency operating costs increase. A number of transit priority measures can be applied, including transit signal priority or queue jump lanes. However, it is necessary that a process of prioritizing intersections for priority treatment be conducted so as to ensure the greatest return on investment is achieved. This thesis proposes and demonstrates a methodology to determine the distribution of stopped delays experienced by transit vehicles at signalized intersections using archived AVL (automated vehicle location) and APC (automated passenger counting) data. This methodology is calibrated and validated using queue length and bus unscheduled stopped delay data measured at a field site. Results show the proposed methodology is of sufficient accuracy to be used in practice for prioritizing signalized intersections for priority treatment. On the condition that a sample of the transit vehicle fleet is equipped with an AVL/APC system, the proposed methodology can be automatically implemented using the archived AVL/APC data and therefore avoid the need to conduct dedicated data collection surveys. The proposed methodology can provide estimates of (1) the maximum extent of the queue; and (2) measures of the distribution of stopped delays experienced by transit vehicles (e.g. mean, standard deviation, 90th percentile, etc.) caused by the downstream traffic signal. These measures can be produced separately for different analysis periods (e.g. different times of the day; days of the week; and time of the year) and can be compiled separately for different transit routes. These outputs can then be used to identify and prioritize signalized intersections as candidates for transit signal priority measures. The proposed method is suitable for application to most transit AVL/APC databases and is demonstrated using data from Grand River Transit, the public transit service provider in the Region of Waterloo, Ontario Canada.

Automatic Vehicle Location (AVL) and Automatic Passenger Counting (APC) systems can be powerful tools for transit agencies to archive large, detailed quantities of transit operations data. Managing data quality is an important first step for exploiting these rich datasets. This thesis presents an automated quality assurance (QA) methodology that identifies unreliable archived AVL/APC data. The approach is based on expected travel and passenger activity patterns derived from the data. It is assumed that standard passenger balancing and schedule matching algorithms are applied to the raw AVL/APC data along with any existing automatic validation programs. The proposed QA methodology is intended to provide transit agencies with a supplementary tool to manage data quality that complements, but does not replace, conventional processing routines (that can be vendor-specific and less transparent). The proposed QA methodology endeavours to flag invalid data as “suspect” and valid data as “non-suspect”. There are three stages: i) the first stage screens data that demonstrate a violation of physical constraints; ii) the second stage looks for data that represent outliers; and iii) the third stage evaluates whether the outlier data can be accounted for with valid or invalid pattern. Stop-level tests are mathematically defined for each stage; however data is filtered at the trip-level. Data that do not violate any physical constraints and do not represent any outliers are considered valid trip data. Outlier trips that may be accounted for with a valid outlier pattern are also considered valid. The remaining trip data is considered suspect. The methodology is applied to a sample set of AVL/APC data from Grand River Transit in the Region of Waterloo, Ontario, Canada. The sample data consist of 4-month’s data from September to December of 2008; it is comprised of 612,000 stop-level records representing 25,012 trips. The results show 14% of the trip-level data is flagged as suspect for the sample dataset. The output is further dissected by: reviewing which tests most contribute to the set of suspect trips; confirming the pattern assumptions for the valid outlier cases; and comparing the sample data by various traits before and after the QA methodology is applied. The latter task is meant to recognize characteristics that may contribute to higher or lower quality data. Analysis shows that the largest portion of suspect trips, for this sample set, suggests the need for improved passenger balancing algorithms or greater accuracy of the APC equipment. The assumptions for valid outlier case patterns were confirmed to be reasonable. It was found that poor schedule data contributes to poorer quality in AVL-APC data. An examination of data distribution by vehicle showed that usage and the portion of suspect data varied substantially between vehicles. This information can be useful in the development of maintenance plans and sampling plans (when combined with information of data distribution by route). A sensitivity analysis was conducted along with an impact analysis on downstream data uses. The model was found to be sensitive to three of the ten user-defined parameters. The impact of the QA procedure on network-level measures of performance (MOPs) was not found to be significant, however the impact was shown to be more substantial for route-specific MOPs.

The travel times of public transit systems that operate on mixed use right-of-ways are often dictated by the delays experienced at signalized intersections. When these delays become large and/or highly variable, transit quality degrades and agency operating costs increase. A number of transit priority measures can be applied, including transit signal priority or queue jump lanes. However, it is necessary that a process of prioritizing intersections for priority treatment be conducted so as to ensure the greatest return on investment is achieved. This thesis proposes and demonstrates a methodology to determine the distribution of stopped delays experienced by transit vehicles at signalized intersections using archived AVL (automated vehicle location) and APC (automated passenger counting) data. This methodology is calibrated and validated using queue length and bus unscheduled stopped delay data measured at a field site. Results show the proposed methodology is of sufficient accuracy to be used in practice for prioritizing signalized intersections for priority treatment. On the condition that a sample of the transit vehicle fleet is equipped with an AVL/APC system, the proposed methodology can be automatically implemented using the archived AVL/APC data and therefore avoid the need to conduct dedicated data collection surveys. The proposed methodology can provide estimates of (1) the maximum extent of the queue; and (2) measures of the distribution of stopped delays experienced by transit vehicles (e.g. mean, standard deviation, 90th percentile, etc.) caused by the downstream traffic signal. These measures can be produced separately for different analysis periods (e.g. different times of the day; days of the week; and time of the year) and can be compiled separately for different transit routes. These outputs can then be used to identify and prioritize signalized intersections as candidates for transit signal priority measures. The proposed method is suitable for application to most transit AVL/APC databases and is demonstrated using data from Grand River Transit, the public transit service provider in the Region of Waterloo, Ontario Canada.