Doctor of Philosophy / Department of Physics / Nobel S. Rebello / Research that investigates the dynamics of knowledge construction by students as they model phenomena at the microscopic level has not been extensively conducted in physics and science education in general. This research wherein I investigated the dynamics of knowledge construction of students in the context of microscopic friction is an attempt to do so. The study commenced with an investigation of the variations in the existing models of students about microscopic friction (phase I of the study). Clinical interviews were conducted with introductory physics students in order to elicit their models. A phenomenographic approach of data analysis was employed to establish the variations in students’ models. Results show that students’ mental models of friction at the atomic level are dominated by their macroscopic experiences. Friction at the atomic level according to most students is due to mechanical interactions (interlocking or rubbing of atoms). Can we build on these macroscopic ideas of students in order to help them construct more scientific explanations of friction at the atomic level? The second phase of the research was an investigation of the dynamics of knowledge construction of students as they constructed models of friction at the atomic level while building on their prior ideas. Individual as well as group teaching interviews were conducted with introductory physics students in order to investigate students learning trajectories and the processes they undergo as they created new models of friction at the atomic level. Results show that the span, zone of proximal development and the epistemological orientations of the students greatly influenced the extent to which they utilize scaffolding afforded to them during the model-building process. Moreover, results show that students undergo the process of incorporation and displacement during their model construction and reconstruction. In the third phase, an instructional material geared towards helping students develop more scientific explanations of microscopic friction was developed and pilot-tested. Overall, the results of the study have significant implications for further research, in improving instruction, and curriculum material development.

Mathematical models to predict vehicle fuel consumption and emission metrics are presented in this thesis. Vehicle fuel consumption and emissions are complex functions to be approximated in practice due to numerous variables affecting their outcome. Vehicle energy and emissions are particularly sensitive to changes in vehicle state variables such as speed and acceleration, ambient conditions such as temperature, and driver control inputs such as acceleration pedal position and gear shift speeds, among others. Recent empirical studies have produced large amounts of data concerning vehicle fuel consumption and emissions rates and offer a wealth of information to transportation planners. Unfortunately, unless simple relationships are found between fuel consumption and vehicle emission metrics, their application in microscopic traffic and macroscopic planning models becomes prohibitive computationally. This thesis describes the development of microscopic energy and emission models using nonlinear multiple regression and neural network techniques to approximate vehicle fuel consumption and emissions field data. The energy and emission models described in this thesis utilized data collected by the Oak Ridge National Laboratory. The data include microscopic fuel consumption and emission measurements (CO, HC, and NOx) for eight light duty vehicles as a function of vehicle speed and acceleration. The thesis describes modeling processes and the tradeoffs between model accuracy and computational efficiency. Model verification results are included for two vehicle driving cycles. The models presented estimate vehicle fuel consumption within 2.5% of their actual measured values. Vehicle emissions errors fall in the range of 3-33% with correlation coefficients ranging between 0.94 and 0.99. Future transportation planning studies could also make use of the modeling approaches presented in the thesis. The models developed in this study have been incorporated into a microscopic traffic simulation tool called INTEGRATION to further demonstrate their application and relevance to traffic engineering studies. Two sample Intelligent Transportation Systems (ITS) application results are included. In the case studies, it was found that vehicle fuel consumption and emissions are more sensitive to the level of vehicle acceleration than to the vehicle speed. Also, the study shows signalization techniques can reduce fuel consumption and emissions significantly, while incident management techniques do not affect the energy and emissions rates notably. / Master of Science

The transportation sector has a significant impact on the environment both nationally and globally since it is a major vehicle fuel consumption and emissions contributor. These emissions are considered a major environmental threat. Consequently, decision makers desperately need tools that can estimate vehicle emissions accurately to quantify the impact of transportation operational projects on the environment. Microscopic fuel consumption and emission models should be capable of computing vehicle emissions reliably to assist decision makers in developing emission mitigation strategies. However, the majority of current state-of-the-art models suffer from two major shortcomings, namely; they either produce a bang-bang control system because they use a linear fuel consumption versus power model or they cannot be calibrated using publicly available data and thus require expensive laboratory or field data collection. Consequently, this dissertation attempts to fill this gap in state-of-the-art emission modeling through a framework based on the Virginia Tech Comprehensive Power-Based Fuel consumption Model (VT-CPFM), which overcomes the above mentioned drawbacks. Specifically, VT-CPFM does not result in a bang-bang control and can be calibrated using publicly available vehicle and road pavement parameters. The main emphasis of this dissertation is to develop a simple and reliable emission model that is able to compute instantaneous emission rates of carbon monoxide (CO), hydrocarbons (HC) and nitrogen oxides (NOx) for the light-duty vehicles (LDVs) and heavy-duty diesel trucks (HDDTs). The proposed extension is entitled Virginia Tech Comprehensive Power-Based Fuel consumption and Emission Model (VT-CPFEM). The study proposes two square root models where the first model structure is a cubic polynomial function that depends on fuel estimates derived solely from VT-CPFM fuel estimates, which enhances the simplicity of the model. The second modeling framework combines the cubic function of the VT-CPFM fuel estimates with a linear speed term. The additional speed term improves the accuracy of the model and can be used as a reference for the driving condition of the vehicle. Moreover, the model is tested and compared with existing models to demonstrate the robustness of the model. Furthermore, the performance of the model was further investigated by applying the model on driving cycles based on real-world driving conditions. The results demonstrate the efficacy of the model in replicating empirical observations reliably and simply with only two parameters. / Ph. D. / The transportation sector places a huge burden on our environment and is one of the major emitters of pollutants. The resulting emissions have a negative impact on human health and could be a concern for national security. Therefore, policymakers are keen to develop models that accurately estimate the emissions from on-road vehicles. Microscopic emission models are capable of estimating the instantaneous vehicle emissions from operational-level projects, and policymakers can use them to evaluate their emission reduction plans and the environmental impact of transportation projects. However, the majority of the current existing models indicate that to achieve the optimum fuel economy, the driver should accelerate at full throttle and full braking for deceleration to minimize the acceleration and deceleration times. This assumption is obviously incorrect since it requires aggressive driving which will result in increasing the fuel consumption rate. Also, the models cannot use publicly accessible and available data to estimate the emissions which require expensive laboratory or field data collection. Consequently, this dissertation attempts to fill this gap in emission modeling through a framework based on the Virginia Tech Comprehensive Power-Based Fuel consumption Model (VT-CPFM), which overcomes the above mentioned drawbacks. Specifically, VT-CPFM does not follow the mentioned assumption of aggressive driving to minimize the fuel consumption as previously explained and can use publicly available vehicle and road pavement variables to estimate the emissions. Also, it utilizes US Environmental Protection Agency (EPA) city and highway the fuel economy ratings to calibrate its parameters. The main emphasis of this dissertation is to develop a simple and reliable emission model that is able to compute instantaneous emission rates of carbon monoxide (CO), hydrocarbons (HC) and nitrogen oxides (NOx) for the light-duty vehicles (LDVs) and heavy-duty diesel trucks (HDDTs). The proposed extension is entitled Virginia Tech Comprehensive PowerBased Fuel consumption and Emission Model (VT-CPFEM). The study proposes two models where the first model structure that depends on fuel estimates derived solely from VT-CPFM fuel estimates, which enhances the simplicity of the model. The second modeling framework combines the VT-CPFM fuel estimates with the speed parameter. The additional speed term improves the accuracy of the model and can be used as a reference for the driving condition of the vehicle. The model framework is consistent in estimating the three emissions for LDVs and HDDTs. Moreover, the performance of the model was investigated in comparison with existing models to demonstrate the reliability of the model. Furthermore, the performance of the model was further evaluated by applying the model on driving cycles based on real-world driving conditions. The results demonstrate the capability of the model in generating accurate and reliable estimates based on the goodness of fit and error values for the three types of emissions.

El aumento de la flota, debido al desarrollo económico del país en la última década, ha generado problemas y dificultades que presentan los usuarios al momento de conducir en las calles de ciudades de todo el territorio nacional. Este conflicto no es ajeno al distrito de Lince, uno de los principales objetivos de este proyecto es llevar a cabo un análisis del problema existente en una de las intersecciones más concurridas de Lince con la ayuda de modelos de microsimulación y mesosimulación adecuados. Por otro lado, las instituciones y / o las empresas de construcción o consultoras que realizan proyectos de viales deben crear modelos computacionales o analíticos como parte fundamental de la etapa de diseño. Sin embargo, con el objetivo de ahorrar o por desconocimiento, realizan simulaciones mesoscópicas o macroscópicas, sin considerar que sus parámetros y ecuaciones son menos exactos que un modelo microscópico. Consistentemente, los resultados obtenidos no han sido correctos, debido a las características observadas en nuestra intersección, como presencia de vehículos pesados, estilo de manejo de conductores, numerosas paradas de transporte público, etc., para evaluar hasta qué punto es eficiente usar un modelo mesoscópico y evitar los tiempos computacionales de un modelo microscópico, se realizará un análisis comparativo de los resultados del Vissim, bajo la metodología de HCM 2010, en una intersección del distrito de Lince y así, optar por un rediseño semafórico coordinado para el cruce de la intersección que mejore el nivel de servicio como para las demoras, en la intersección estudiada. / The increase in the fleet, due to the economic development of the country in the last decade, has generated problems and difficulties that users present when driving on the streets of cities throughout the national territory. This conflict is not alien to the Lince district, one of the main objectives of this project is to carry out an analysis of the existing problem in one of the busiest intersections of Lince with the help of suitable microsimulation and mesosimulation models. On the other hand, institutions and / or construction or consulting companies that carry out road projects must create computational or analytical models as a fundamental part of the design stage. However, with the aim of saving or due to ignorance, they carry out mesoscopic or macroscopic simulations, without considering that their parameters and equations are less exact than a microscopic model. Consistently, the results obtained have not been correct, due to the characteristics observed in our intersection, such as the presence of heavy vehicles, driving style of drivers, numerous public transport stops, etc., to evaluate to what extent it is efficient to use a model In order to avoid the computational times of a microscopic model, a comparative analysis of the Vissim results will be carried out, under the HCM 2010 methodology, at an intersection of the Lince district and thus, opt for a coordinated traffic light redesign for the intersection of the intersection that improves the level of service as well as for delays, in the studied intersection. / Tesis

Many car-following models predict a stable car-following behavior with a very small fluctuation around an equilibrium value g* of the net headway g with zero speed-difference Δv between the following and the lead vehicle. However, it is well-known and additionally demonstrated by data in this paper, that the fluctuations are much larger than these models predict. Typically, the fluctuation in speed difference is around ±2m/s, while the fluctuation in the net time headway T=g/v can be as big as one or even two seconds, which is as large as the mean time headway itself. By analyzing data from loop detectors as well as data from vehicle trajectories, evidence is provided that this randomness is not due to driver heterogeneity, but can be attributed to an internal stochasticity of the driver itself. A final model-based analysis supports the hypothesis, that the preferred headway of the driver is the parameter that is not kept constant but fluctuates strongly, thus causing the even macroscopically observable randomness in traffic flow.