One of the main requirements of designing perpetual pavements is to determine the endurance limit of Hot Mix Asphalt (HMA). The purpose of this study was to validate the endurance limit for HMA using laboratory beam fatigue tests. A mathematical procedure was developed to determine the endurance limit of HMA due to healing that occurs during the rest periods between loading cycles. Relating healing to endurance limit makes this procedure unique compared to previous research projects that investigated these concepts separately. An extensive laboratory testing program, including 468 beam tests, was conducted according to AASHTO T321-03 test procedure. Six factors that affect the fatigue response of HMA were evaluated: binder type, binder content, air voids, test temperature, rest period and applied strain. The endurance limit was determined when no accumulated damage occurred indicating complete healing. Based on the test results, a first generation predictive model was developed to relate stiffness ratio to material properties. A second generation stiffness ratio model was also developed by replacing four factors (binder type, binder content, air voids, and temperature) with the initial stiffness of the mixture, which is a basic material property. The model also accounts for the nonlinear effects of the rest period and the applied strain on the healing and endurance limit. A third generation model was then developed by incorporation the number of loading cycles at different locations along the fatigue degradation curve for each test in order to account for the nonlinearity between stiffness ratio and loading cycles. In addition to predicting endurance limit, the model has the ability to predict the number of cycles to failure at any rest period and stiffness combination. The model was used to predict fatigue relationship curves for tests with rest period and determining the K1, K2, and K3 fatigue cracking coefficients. The three generation models predicted close endurance limit values ranging from 22 to 204 micro strains. After developing the third generation stiffness ratio model, the predicted endurance limit values were integrated in the strain-Nf fatigue relationships as a step toward incorporating the endurance limit in the MEPDG software. The results of this study can be used to design perpetual pavements that can sustain a large number of loads if traffic volumes and vehicle weights are controlled.

The activity-based approach to travel demand analysis and modeling, which has been developed over the past 30 years, has received tremendous success in transportation planning and policy analysis issues, capturing the multi-way joint relationships among socio-demographic, economic, land use characteristics, activity participation, and travel behavior. The development of synthesizing population with an array of socio-demographic and socio-economic attributes has drawn remarkable attention due to privacy and cost constraints in collecting and disclosing full scale data. Although, there has been enormous progress in producing synthetic population, there has been less progress in the development of population evolution modeling arena to forecast future year population. The objective of this dissertation is to develop a well-structured full-fledged demographic evolution modeling system, capturing migration dynamics and evolution of person level attributes, introducing the concept of new household formations and apprehending the dynamics of household level long-term choices over time. A comprehensive study has been conducted on demography, sociology, anthropology, economics and transportation engineering area to better understand the dynamics of evolutionary activities over time and their impacts in travel behavior. This dissertation describes the methodology and the conceptual framework, and the development of model components. Demographic, socio-economic, and land use data from American Community Survey, National Household Travel Survey, Census PUMS, United States Time Series Economic Dynamic data and United States Center for Disease Control and Prevention have been used in this research. The entire modeling system has been implemented and coded using programming language to develop the population evolution module named `PopEvol' into a computer simulation environment. The module then has been demonstrated for a portion of Maricopa County area in Arizona to predict the milestone year population to check the accuracy of forecasting. The module has also been used to evolve the base year population for next 15 years and the evolutionary trend has been investigated.

Institutions of higher education, particularly those with large student enrollments, constitute special generators that contribute in a variety of ways to the travel demand in a region. Despite the importance of university population travel characteristics in understanding and modeling activity-travel patterns and mode choice behavior in a region, such populations remain under-studied. As metropolitan planning organizations continue to improve their regional travel models by incorporating processes and parameters specific to major regional special generators, university population travel characteristics need to be measured and special submodels that capture their behavior need to be developed. The research presented herein begins by documenting the design and administration of a comprehensive university student online travel and mode use survey that was administered at Arizona State University (ASU) in the Greater Phoenix region of Arizona. The dissertation research offers a detailed statistical analysis of student travel behavior for different student market segments. A framework is then presented for incorporating university student travel into a regional travel demand model. The application of the framework to the ASU student population is documented in detail. A comprehensive university student submodel was estimated and calibrated for integration with the full regional travel model system. Finally, student attitudes toward travel are analyzed and used as explanatory factors in multinomial logit models of mode choice. This analysis presents an examination of the extent to which attitudes play a role in explaining mode choice behavior of university students in an urban setting. The research provides evidence that student travel patterns vary substantially from those of the rest of the population, and should therefore be considered separately when forecasting travel demand and formulating transport policy in areas where universities are major contributors to regional travel.