An improved framework for dynamic origin-destination (O-D) matrix estimation

This dissertation aims to improve the performance of existing assignment-based dynamic origin-destination (O-D) matrix estimation models to successfully apply Intelligent Transportation Systems (ITS) strategies for the purposes of traffic congestion relief and dynamic traffic assignment (DTA) in transportation network modeling. The methodology framework has two advantages over the existing assignment-based dynamic O-D matrix estimation models. First, it combines an initial O-D estimation model into the estimation process to provide a high confidence level of initial input for the dynamic O-D estimation model, which has the potential to improve the final estimation results and reduce the associated computation time. Second, the proposed methodology framework can automatically convert traffic volume deviation to traffic density deviation in the objective function under congested traffic conditions. Traffic density is a better indicator for traffic demand than traffic volume under congested traffic condition, thus the conversion can contribute to improving the estimation performance. The proposed method indicates a better performance than a typical assignment-based estimation model (Zhou et al., 2003) in several case studies. In the case study for I-95 in Miami-Dade County, Florida, the proposed method produces a good result in seven iterations, with a root mean square percentage error (RMSPE) of 0.010 for traffic volume and a RMSPE of 0.283 for speed. In contrast, Zhou's model requires 50 iterations to obtain a RMSPE of 0.023 for volume and a RMSPE of 0.285 for speed. In the case study for Jacksonville, Florida, the proposed method reaches a convergent solution in 16 iterations with a RMSPE of 0.045 for volume and a RMSPE of 0.110 for speed, while Zhou's model needs 10 iterations to obtain the best solution, with a RMSPE of 0.168 for volume and a RMSPE of 0.179 for speed. The successful application of the proposed methodology framework to real road networks demonstrates its ability to provide results both with satisfactory accuracy and within a reasonable time, thus establishing its potential usefulness to support dynamic traffic assignment modeling, ITS systems, and other strategies.

Towards simulation and emulation of large-scale computer networks

Developing analytical models that can accurately describe behaviors of Internet-scale networks is difficult. This is due, in part, to the heterogeneous structure, immense size and rapidly changing properties of today's networks. The lack of analytical models makes large-scale network simulation an indispensable tool for studying immense networks. However, large-scale network simulation has not been commonly used to study networks of Internet-scale. This can be attributed to three factors: 1) current large-scale network simulators are geared towards simulation research and not network research, 2) the memory required to execute an Internet-scale model is exorbitant, and 3) large-scale network models are difficult to validate. This dissertation tackles each of these problems. ^ First, this work presents a method for automatically enabling real-time interaction, monitoring, and control of large-scale network models. Network researchers need tools that allow them to focus on creating realistic models and conducting experiments. However, this should not increase the complexity of developing a large-scale network simulator. This work presents a systematic approach to separating the concerns of running large-scale network models on parallel computers and the user facing concerns of configuring and interacting with large-scale network models. ^ Second, this work deals with reducing memory consumption of network models. As network models become larger, so does the amount of memory needed to simulate them. This work presents a comprehensive approach to exploiting structural duplications in network models to dramatically reduce the memory required to execute large-scale network experiments. ^ Lastly, this work addresses the issue of validating large-scale simulations by integrating real protocols and applications into the simulation. With an emulation extension, a network simulator operating in real-time can run together with real-world distributed applications and services. As such, real-time network simulation not only alleviates the burden of developing separate models for applications in simulation, but as real systems are included in the network model, it also increases the confidence level of network simulation. This work presents a scalable and flexible framework to integrate real-world applications with real-time simulation.^

Employer participation in Florida school -to -work initiatives

This study used a cross-sectional design and descriptive research methodology to assess the characteristics and practices of employers participating in Florida school-to-work (STW) initiatives, the factors influencing their participation, and their STW partnerships with schools. The study also examined the relationships between employer characteristics and participation. ^ A sampling frame of 15,202 employers from the 28 Florida STW regions was constructed. Data were collected via a mail survey with a random sample of employers, using a researcher-designed questionnaire. Data were analyzed using descriptive statistics, correlational analysis, and analysis of variance. ^ At the 95% confidence level, it was estimated that the mean establishment size in the population is between 25 and 51 employees and that employers have been involved in STW partnerships between five and seven years. ^ The study revealed broad-based employer participation in three of four areas: Working with Students, Working with Educators, and Internal Company Practices Supporting STW. A fourth area, Building a System, showed generally low participation. Data indicate that workforce needs are important incentives to employer STW participation beyond their desire to contribute to education or the community. Data also indicate that lack of information on STW is the greatest barrier to employer participation. ^ Sample employers have more positive perceptions of the value of their partnerships with schools than of the quality of the partnerships. Ninety-four percent agreed that students are better prepared for work and careers as a result of the partnership's activities. More than half agreed that a sense of trust and good communication exist between educators and employer partners. ^ Employer variables found to have a significant, positive relationship with participation include size (coefficient of determination r2 = .116), years in STW partnerships (r2 = .128), and perceptions of partnership quality (r2 = .092) and value (r2 = .112). ^ A major conclusion based on the findings of this study is that employer participation is optimized in initiatives that achieve important STW outcomes for students and build long-term relationships between employers and schools. Another that in Florida, the STW Opportunities Act of 1994 has not resulted in substantially greater employer involvement in building a STW system. ^

Effect of Pavement-Vehicle Interaction on Highway Fuel Consumption and Emission

Vehicle fuel consumption and emission are two important effectiveness measurements of sustainable transportation development. Pavement plays an essential role in goals of fuel economy improvement and greenhouse gas (GHG) emission reduction. The main objective of this dissertation study is to experimentally investigate the effect of pavement-vehicle interaction (PVI) on vehicle fuel consumption under highway driving conditions. The goal is to provide a better understanding on the role of pavement in the green transportation initiates. Four study phases are carried out. The first phase involves a preliminary field investigation to detect the fuel consumption differences between paired flexible-rigid pavement sections with repeat measurements. The second phase continues the field investigation by a more detailed and comprehensive experimental design and independently investigates the effect of pavement type on vehicle fuel consumption. The third study phase calibrates the HDM-IV fuel consumption model with data collected in the second field phase. The purpose is to understand how pavement deflection affects vehicle fuel consumption from a mechanistic approach. The last phase applies the calibrated HDM-IV model to Florida’s interstate network and estimates the total annual fuel consumption and CO2 emissions on different scenarios. The potential annual fuel savings and emission reductions are derived based on the estimation results. Statistical results from the two field studies both show fuel savings on rigid pavement compared to flexible pavement with the test conditions specified. The savings derived from the first phase are 2.50% for the passenger car at 112km/h, and 4.04% for 18-wheel tractor-trailer at 93km/h. The savings resulted from the second phase are 2.25% and 2.22% for passenger car at 93km/h and 112km/h, and 3.57% and 3.15% for the 6-wheel medium-duty truck at 89km/h and 105km/h. All savings are statistically significant at 95% Confidence Level (C.L.). From the calibrated HDM-IV model, one unit of pavement deflection (1mm) on flexible pavement can cause an excess fuel consumption by 0.234-0.311 L/100km for the passenger car and by 1.123-1.277 L/100km for the truck. The effect is more evident at lower highway speed than at higher highway speed. From the network level estimation, approximately 40 million gallons of fuel (combined gasoline and diesel) and 0.39 million tons of CO2 emission can be saved/reduced annually if all Florida’s interstate flexible pavement are converted to rigid pavement with the same roughness levels. Moreover, each 1-mile of flexible-rigid conversion can result in a reduction of 29 thousand gallons of fuel and 258 tons of CO2 emission yearly.

Acculturation and Hispanic identity/ethnicity as a predictor of change on the brief situational confidence questionnaire

The purpose of this study was to investigate whether level of acculturation among Hispanic adolescent males (n = 174) influenced treatment outcome in a substance abuse program, specifically on the Brief Situational Confidence Questionnaire (BSCQ) which measures relapse confidence. It was hypothesized that lower levels of acculturation were likely to be predictive of positive change, whereas higher levels of acculturation were likely to be predictive of no change or negative change. It was found that adolescents changed over time in BSCQ scores regardless of which acculturation variable was measured. Contrary to expectations, for those adolescents placed in family treatment, place of birth was not significantly associated with treatment response. However, both, U.S. and non-U.S. born adolescents demonstrated a change over time when receiving family treatment, suggesting that the family substance abuse treatment utilized in this intervention effected change over time.