Defining the Resolution of a Network for Transportation Analyses: a New Method to Improve Transportation Planning Decisions

Travel demand models are important tools used in the analysis of transportation plans, projects, and policies. The modeling results are useful for transportation planners making transportation decisions and for policy makers developing transportation policies. Defining the level of detail (i.e., the number of roads) of the transport network in consistency with the travel demand model’s zone system is crucial to the accuracy of modeling results. However, travel demand modelers have not had tools to determine how much detail is needed in a transport network for a travel demand model. This dissertation seeks to fill this knowledge gap by (1) providing methodology to define an appropriate level of detail for a transport network in a given travel demand model; (2) implementing this methodology in a travel demand model in the Baltimore area; and (3) identifying how this methodology improves the modeling accuracy. All analyses identify the spatial resolution of the transport network has great impacts on the modeling results. For example, when compared to the observed traffic data, a very detailed network underestimates traffic congestion in the Baltimore area, while a network developed by this dissertation provides a more accurate modeling result of the traffic conditions. Through the evaluation of the impacts a new transportation project has on both networks, the differences in their analysis results point out the importance of having an appropriate level of network detail for making improved planning decisions. The results corroborate a suggested guideline concerning the development of a transport network in consistency with the travel demand model’s zone system. To conclude this dissertation, limitations are identified in data sources and methodology, based on which a plan of future studies is laid out.

Controlling Nanostructures for in-situ TEM Characterization

Low dimensional nanostructures, such as nanotubes and 2D sheets, have unique and promising material properties both from a fundamental science and an application standpoint. Theoretical modelling and calculations predict previously unobserved phenomena that experimental scientists often struggle to reproduce because of the difficulty in controlling and characterizing the small structures under real-world constraints. The goal of this dissertation is to controlling these structures so that nanostructures can be characterized in-situ in transmission electron microscopes (TEM) allowing for direct observation of the actual physical responses of the materials to different stimuli. Of most interest to this work are the thermal and electrical properties of carbon nanotubes, boron nitride nanotubes, and graphene. The first topic of the dissertation is using surfactants for aqueous processing to fabricate, store, and deposit the nanostructures. More specifically, thorough characterization of a new surfactant, ammonium laurate (AL), is provided and shows that this new surfactant outperforms the standard surfactant for these materials, sodium dodecyl sulfate (SDS), in almost all tested metrics. New experimental set-ups have been developed by combining specialized in-situ TEM holders with innovative device fabrication. For example, electrical characterization of graphene was performed by using an STM-TEM holder and depositing graphene from aqueous solutions onto lithographically patterned, electron transparent silicon nitride membranes. These experiments produce exciting information about the interaction between graphene and metal probes and the substrate that it rests on. Then, by adding indium to the backside of the membrane and employing the electron thermal microscopy (EThM) technique, the same type of graphene samples could be characterized for thermal transport with high spatial resolution. It is found that reduced graphene oxide sheets deposited onto a silicon nitride membrane and displaying high levels of wrinkling have higher than expected electrical and thermal conduction properties. We are clearly able to visualize the ability of graphene to spread heat away from an electronic hot spot and into the substrate.