In accordance with the federally mandated Highway Safety Improvement Program (HSIP), every state is required to “develop and implement, on a continuous basis, a highway safety improvement program which has the overall objective of reducing the number and severity of crashes and decreasing the potential for crashes on all highways” (FHWA, 1979). The federal government via the HSIP provides a significant amount of funding that allows every state to improve the safety of their highway network. With such large amounts of federal funds involved, it becomes essential that state transportation agencies take appropriate measures to utilize these funds in the most cost effective manner. As part of this program, the Texas Department of Transportation (TxDOT) uses a formula known as the “Safety Improvement Index” (SII) for identification, ranking and selection of eligible projects. The SII is in essence used to rank potential projects by giving priority to projects that have a higher benefit-cost (B/C) ratio. Since the SII has not been updated within the last two decades, there is a need to determine whether the current formulation needs to be revised or updated. This concern has been reported in the literature. The objective of this thesis is to evaluate the SII in its current functional form and its usefulness to rank and prioritize projects for safety improvement. The evaluation procedure proposed in this thesis uses sensitivity analyses to study the effects of different input variables on the SII. The sensitivity analysis is performed with respect to five critical variables chosen on the basis of a literature review. The five variables studied are the Interest Rate, Removal of PDO Crashes, Crash Reduction Factors, Crash Rates, and Crash – Flow Relationship. The focus of the evaluation is to compare the ranking of projects with respect to changes in the value of these input variables. The ranking are evaluated using various statistical methods, such as the Spearman Rank Order Correlation Test and Kendall’s Tau Test. The results of the analysis indicated that, although changes in the value of input variables affect the SII output, the ranking of projects is usually not affected, with the exception of the crash reduction factor variable. Hence, the same projects will be selected for safety improvement, even if different values are used in the SII. Therefore, it is recommended that the current formulation of the SII and the value of input variables used in the formula be retained by TxDOT for prioritizing safety improvement projects. However, it is suggested to examine the accuracy and uncertainty associated with reduction factors, since in some cases they were found to affect the ranking of projects.

In accordance with the federally mandated Highway Safety Improvement Program (HSIP), every state is required to "develop and implement, on a continuous basis, a highway safety improvement program which has the overall objective of reducing the number and severity of crashes and decreasing the potential for crashes on all highways" (FHWA, 1979). The federal government via the HSIP provides a significant amount of funding that allows every state to improve the safety of their highway network. With such large amounts of federal funds involved, it becomes essential that state transportation agencies take appropriate measures to utilize these funds in the most cost effective manner. As part of this program, the Texas Department of Transportation (TxDOT) uses a formula known as the "Safety Improvement Index" (SII) for identification, ranking and selection of eligible projects. The SII is in essence used to rank potential projects by giving priority to projects that have a higher benefit-cost (B/C) ratio. Since the SII has not been updated within the last two decades, there is a need to determine whether the current formulation needs to be revised or updated. This concern has been reported in the literature. The objective of this thesis is to evaluate the SII in its current functional form and its usefulness to rank and prioritize projects for safety improvement. The evaluation procedure proposed in this thesis uses sensitivity analyses to study the effects of different input variables on the SII. The sensitivity analysis is performed with respect to five critical variables chosen on the basis of a literature review. The five variables studied are the Interest Rate, Removal of PDO Crashes, Crash Reduction Factors, Crash Rates, and Crash -- Flow Relationship. The focus of the evaluation is to compare the ranking of projects with respect to changes in the value of these input variables. The ranking are evaluated using various statistical methods, such as the Spearman Rank Order Correlation Test and Kendall's Tau Test. The results of the analysis indicated that, although changes in the value of input variables affect the SII output, the ranking of projects is usually not affected, with the exception of the crash reduction factor variable. Hence, the same projects will be selected for safety improvement, even if different values are used in the SII. Therefore, it is recommended that the current formulation of the SII and the value of input variables used in the formula be retained by TxDOT for prioritizing safety improvement projects. However, it is suggested to examine the accuracy and uncertainty associated with reduction factors, since in some cases they were found to affect the ranking of projects.

The service of mobile communications is customer-oriented. The service is intangible and filled with large amounts of variable. It causes the difficulty of evaluation on service quality. Therefore, understanding customer requirements and key factors of service to promote service quality, meet customer needs, retain customer loyalty and create profits is one of the important issues in mobile communications. The study mainly aimed to discuss the service quality of mobile telecommunications industry. According to the revised SERVQUAL measure table proposed by Parasuraman et al. (1991), the service quality questionnaire is designed by service characteristics of mobile telecommunications industry.The total number of the questionnaires was 162. Questionnaires were finished via web survey, discussing the influences on demographic variables and whether there were significant differences between customers¡¦ expectations and their authentic experiences. The Importance-Satisfaction model (I-s Model) and Improvement index (Ii) were also used as references to find out the priority to improve. Based on the survey consequence, there were significant differences between customers¡¦ expectations and their authentic experiences. There were 22 questions (23 questions of 5 dimensions) showing significant differences except for the good brand image of the enterprises. Therefore, the assumption that customers¡¦ expectations and their authentic experiences was significantly different in terms of service quality was partly tenable. When it comes to the expectations of quality, the results showed significant differences in tangibles (gender), responsiveness and assurance (monthly income); besides, there were also significant differences in responsiveness (age) and empathy (residence) when it comes to quality awareness. Consequently, the assumption is partly tenable. Moreover, customers¡¦ expectations are always higher than what enterprises can actually provide, which means these enterprises cannot satisfied with what customers¡¦ needs due to internal resources and social environment of the enterprises of mobile telecommunications industry. In order to improve these issues, Importance-Satisfaction model (I-S Model) and Improvement index (Ii) were used to make an improvement project listed in priority. The result shows the mobile network quality is the most important and also the most dissatisfied item for the customers. It could be provided to mobile communications as a reference to improve for promoting customer satisfaction and loyalty.

A measure of the quality of Voronoi tessellations resulting from various mesh generators founded on feature-driven models is introduced in this work. A planar tessellation covers an image with polygons of various shapes and sizes. Tessellations have potential utility due to their geometry and the opportunity to derive useful information from them for object recognition, image processing and classification. Problem domains including images are generally feature-endowed, non-random domains. Generators modeled otherwise may easily guarantee quality of meshes but certainly bear no reference to features of the meshed problem domain. They are therefore unsuitable in point pattern identification, characterization and subsequently the study of meshed regions. We therefore found generators on features of the problem domain. This provides a basis for element quality studies and improvement based on quality criteria. The resulting polygonal meshes tessellating an n-dimensional digital image into convex regions are of varying element qualities. Given several types of mesh generating sets, a measure of overall solution quality is introduced to determine their effectiveness. Given a tessellation of general and mixed shapes, this presents a challenge in quality improvement. The Centroidal Voronoi Tessellation (CVT) technique is developed for quality improvement and guarantees of mixed, general-shaped elements and to preserve the validity of the tessellations. Mesh quality indicators and entropies introduced are useful for pattern studies, analysis, recognition and assessing information. Computed features of tessellated spaces are explored for image information content assessment and cell processing to expose detail using information theoretic methods. Tessellated spaces also furnish information on pattern structure and organization through their quality distributions. Mathematical and theoretical results obtained from these spaces help in understanding Voronoi diagrams as well as for their successful applications. Voronoi diagrams expose neighbourhood relations between pattern units. Given this realization, the foundation of near sets is developed for further applications. / February 2017