- All Subjects: Transportation--Planning
- Genre: Academic theses
- Creators: Kaloush, Kamil
- Member of: Theses and Dissertations
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.
Real-time information systems are being used widely around the world to mitigate the adverse impacts of congestion and events that contribute to network delay. It is important that transportation modeling tools be able to accurately model the impacts of real-time information provision. Such planning tools allow the simulation of the impacts of various real-time information systems, and the design of traveler information systems that can minimize impacts of congestion and network disruptions. Such modeling tools would also be helpful in planning emergency response services as well as evacuation scenarios in the event of a natural disaster. Transportation modeling tools currently in use are quite limited in their ability to model the impacts of real-time information provision on travel demand and route choices. This dissertation research focuses on enhancing a previously developed integrated transportation modeling system dubbed SimTRAVEL (Simulator of Transport, Routes, Activities, Vehicles, Emissions, and Land) to incorporate capabilities that allow the simulation of the impacts of real-time traveler information systems on activity-travel demand. The first enhancement made to the SimTRAVEL framework involves the ability to reflect the effects of providing information on prevailing (as opposed to historical) network conditions on activity-travel behavior choices. In addition, the model system is enhanced to accommodate multiple user information classes (pre-trip and enroute) simultaneously. The second major contribution involves advancing the methodological framework to model enroute decision making processes where a traveler may alter his or her travel choices (such as destination choice) while enroute to an intended destination. Travelers who are provided up-to-date network information may choose to alter their destination in response to congested conditions, or completely abandon and reschedule an activity that offers some degree of flexibility. In this dissertation research, the model framework is developed and an illustrative demonstration of the capabilities of the enhanced model system is provided using a subregion of the Greater Phoenix metropolitan area in Arizona. The results show that the model is able to simulate adjustments in travel choices that may result from the introduction of real-time traveler information. The efficacy of the integrated travel model system is also demonstrated through the application of the enhanced model system to evaluate transportation policy scenarios.
The objective of the research is to test the use of 3D printed thermoplastic to produce fixtures which affix instrumentation to asphalt concrete samples used for Simple Performance Testing (SPT). The testing is done as part of materials characterization to obtain properties that will help in future pavement designs. Currently, these fixtures (mounting studs) are made of expensive brass and cumbersome to clean with or without chemicals.
Three types of thermoplastics were utilized to assess the effect of temperature and applied stress on the performance of the 3D printed studs. Asphalt concrete samples fitted with thermoplastic studs were tested according to AASHTO & ASTM standards. The thermoplastics tested are: Polylactic acid (PLA), the most common 3D printing material; Acrylonitrile Butadiene Styrene (ABS), a typical 3D printing material which is less rigid than PLA and has a higher melting temperature; Polycarbonate (PC), a strong, high temperature 3D printing material.
A high traffic volume Marshal mix design from the City of Phoenix was obtained and adapted to a Superpave mix design methodology. The mix design is dense-graded with nominal maximum aggregate size of ¾” inch and a PG 70-10 binder. Samples were fabricated and the following tests were performed: Dynamic Modulus |E*| conducted at five temperatures and six frequencies; Flow Number conducted at a high temperature of 50°C, and axial cyclic fatigue test at a moderate temperature of 18°C.
The results from SPT for each 3D printed material were compared to results using brass mounting studs. Validation or rejection of the concept was determined from statistical analysis on the mean and variance of collected SPT test data.
The concept of using 3D printed thermoplastic for mounting stud fabrication is a promising option; however, the concept should be verified with more extensive research using a variety of asphalt mixes and operators to ensure no bias in the repeatability and reproducibility of test results. The Polycarbonate (PC) had a stronger layer bonding than ABS and PLA while printing. It was recommended for follow up studies.
Second, this research investigates lane-specific traffic behavior through empirical analysis and statistical modeling of lane flow distribution. Lane-specific traffic behavior is also an important component in evaluating freeway performance and has a significant impact in the mechanism of queue evolution, particularly around merges, and bottleneck discharge rate. In this research, site-specific linear LFD trends of three-lane congested freeways were investigated and modeled. A large-scale data collection process was implemented to systematically characterize the effects of several traffic and geometric features of freeways in the occurrence of between-site LFD variations. Also, an innovative three-stage modeling framework was used to model LFD behavior using multiple logistic regression to describe between-site LFD variations and Dirichlet regression to model recurrent combinations of linear LFD trends. This novel approach is able to represent both between and within site variations of LFD trends better, while accounting for the unit-sum constraint and distribution assumptions inherent of proportions data. Results revealed that proximity to freeway merges, a site’s level of congestion, and the presence of HOV lanes are significant factors that influence site-specific recurrent LFD behavior.
Findings from this work significantly improve the state-of-the-art knowledge on merging and lane-specific traffic behavior, which can help to improve traffic operations and reduce traffic congestion in freeways.