(Georgia Institute of Technology, 2018-08-08)
Liu, Haobing
Motor vehicle emissions and their impacts on local air pollutant concentrations are a primary concern in cities. Properly quantifying energy and emissions is the key step in identifying the major sources of air pollution, evaluating whether transportation activities are consistent with air quality goals, and providing decision makers with reference for implementation of new policies for sustainable development. Mathematical models are commonly used to predict vehicle energy consumption and emissions. Vehicle-specific power (VSP) is widely used in such models to evaluate engine load, and it is represented as a function of vehicle mass, vehicle dynamic parameters (rolling/drag coefficient), driving behavior (speed and acceleration) and road conditions (gravitational acceleration and road gradient). In the U.S. Environmental Protection Agency’s (USEPA’s) MOVES (MOtor Vehicle Emission Simulator) model, speed and VSP levels are tied to vehicle energy consumption and emission rates. Detailed and accurate speed-acceleration joint distributions (SAJDs, also known as Watson plots) can be used to reflect onroad activity required for calculating the distribution of activities in MOVES VSP and speed bins, and thus for estimating vehicle energy consumption and emissions. Road grade is also a critical variable that affects engine operations, as uphill grades require that the engine perform additional work against gravity in the direction of vehicle motion (while downhill grades obtain an energy benefit). Real-world vehicle speed and acceleration can be easily collected using low-cost global positioning system (GPS) data loggers, on-board diagnostics (OBD) system data loggers, and smartphones apps. But, The effect of road grade is usually ignored in emission modeling. On the other hand, very little attention has been paid to the interaction between real-world road grade and onroad activity patterns and the resulting impact on energy use and emissions. However, road grade is expected to impact vehicle operations due to drivers’ response to uphill and downhill driving, or due to vehicle mechanical performance. It is currently unclear that how speed and accelerations vary across different road grade levels, and how the interaction of driver behavior and road grade affect engine power, energy consumption, and emissions modeling. This study is directed at answering two issues: 1): how road grade impacts vehicle speed and acceleration distributions, and how such distributions vary across vehicle types, roadway types, traffic conditions, etc., and 2): how significant the impact of integrating grade interactions is with respect to energy, emissions, and air quality modeling.
(Georgia Institute of Technology, 2015-11-19)
Chilukuri, Bhargava Rama
Ramp meters have been used for congestion management on freeways since the 1960s to maximize freeway capacity by controlling on-ramp flows. Traditionally, the focus has been to develop rule-based algorithms and optimal control case studies. This led to a host of algorithms and methods which cannot be proven to provide an optimal control and the case studies does not provide a systematic understanding of the characteristics of optimal control and its influence on traffic dynamics. Moreover, optimal is not easy to achieve in practice due to the limited storage on the on-ramps.
Towards this end, this dissertation systematically studies the optimality conditions for the case of unlimited storage and spatiotemporal evolution of control and its corresponding traffic dynamics on freeway and ramps under queue constraint, carefully taking the traffic dynamics into account.
A Kinematic Wave model of the freeway-ramps system is optimized for minimal total delay. The optimality conditions for the case of unlimited ramp storage are studied using Moskowitz functions that provide several interesting insights for different scenarios, including the case of limited storage. This dissertation shows that the current problem posed as a nonlinear coupled PDE system with a nonlinear merge model cannot be solved analytically. This study also shows that the discrete-time nonlinear formulation solved with simulation-based optimization does not converge in reasonable time. To overcome this, the problem is reposed as a LP formulation that includes capacity drop. For discrete formulation, this study develops an error-free solution to the KW model with a source term that enhanced the quality of the numerical solution. This study identifies four distinct regions in the state surface with distinct metering patterns. Explicit modeling of ramps enabled correlating the initialization and termination times of the metering patterns with the evolution of traffic dynamics on the freeways and ramps. Using these results, this dissertation presents a hybrid isolated ramp metering algorithm that outperforms existing methods.