DSpace at UT Austin
https://repositories.lib.utexas.edu:443
This repository system captures, stores, indexes, preserves, and distributes digital research material.2017-06-22T02:35:17ZModeling and optimizing network infrastructure for autonomous vehicles
http://hdl.handle.net/2152/47311
Modeling and optimizing network infrastructure for autonomous vehicles
Levin, Michael William
Autonomous vehicle (AV) technology has matured sufficiently to be in testing on public roads. However, traffic models of AVs are still in development. Most previous work has studied AV technologies in micro-simulation. The purpose of this dissertation is to model and optimize AV technologies for large city networks to predict how AVs might affect city traffic patterns and travel behaviors. To accomplish these goals, we construct a dynamic network loading model for AVs, consisting of link and node models of AV technologies, which is used to calculate time-dependent travel times in dynamic traffic assignment. We then study several applications of the dynamic network loading to predict how AVs might affect travel demand and traffic congestion. AVs admit reduced perception-reaction times through technologies such as (cooperative) adaptive cruise control, which can reduce following headways and increase capacity. Previous work has studied these in micro-simulation, but we construct a mesoscopic simulation model for analyses on large networks. To study scenarios with both autonomous and conventional vehicles, we modify the kinematic wave theory to include multiple classes of flow. The flow-density relationship also changes in space and time with the class proportions. We present multiclass cell transmission model and prove that it is a Godunov approximation to the multiclass kinematic wave theory. We also develop a car-following model to predict the fundamental diagram at arbitrary proportions of AVs. Complete market penetration scenarios admit dynamic lane reversal -- changing lane direction at high frequencies to more optimally allocate road capacity. We develop a kinematic wave theory in which the number of lanes changes in space and time, and approximately solve it with a cell transmission model. We study two methods of determining lane direction. First, we present a mixed integer linear program for system optimal dynamic traffic assignment. Since this program is computationally difficult to solve, we also study dynamic lane reversal on a single link with deterministic and stochastic demands. The resulting policy is shown to significantly reduce travel times on a city network. AVs also admit reservation-based intersection control, which can make greater use of intersection capacity than traffic signals. AVs communicate with the intersection manager to reserve space-time paths through the intersection. We create a mesoscopic node model by starting with the conflict point variant of reservations and aggregating conflict points into capacity-constrained conflict regions. This model yields an integer program that can be adapted to arbitrary objective functions. To motivate optimization, we present several examples on theoretical and realistic networks demonstrating that naïve reservation policies can perform worse than traffic signals. These occur due to asymmetric intersections affecting optimal capacity allocation and/or user equilibrium route choice behavior. To improve reservations, we adapt the decentralized backpressure wireless packet routing and P0 traffic signal policies for reservations. Results show significant reductions in travel times on a city network. Having developed link and node models, we explore how AVs might affect travel demand and congestion. First, we study how capacity increases and reservations might affect freeway, arterial, and city networks. Capacity increases consistently reduced congestion on all networks, but reservations were not always beneficial. Then, we use dynamic traffic assignment within a four-step planning model, adding the mode choice of empty repositioning trips to avoid parking costs. Results show that allowing empty repositioning to encourage adoption of AVs could reduce congestion. Also, once all vehicles are AVs, congestion will still be significantly reduced. Finally, we present a framework to use the dynamic network loading model to study shared AVs. Results show that shared AVs could reduce congestion if used in certain ways, such as with dynamic ride-sharing. However, shared AVs also cause significant congestion. To summarize, this dissertation presents a complete mesoscopic simulation model of AVs that could be used for a variety of studies of AVs by planners and practitioners. This mesoscopic model includes new node and link technologies that significantly improve travel times over existing infrastructure. In addition, we motivate and present more optimal policies for these AV technologies. Finally, we study several travel behavior scenarios to provide insights about how AV technologies might affect future traffic congestion. The models in this dissertation will provide a basis for future network analyses of AV technologies.
2017-03-24T00:00:00ZIyer Laboratory: Gel-free Library Preparation Protocol
http://hdl.handle.net/2152/47310
Iyer Laboratory: Gel-free Library Preparation Protocol
Hall, Amelia
Originally developed by Amelia Weber Hall in October 2012, then modified to avoid usage of silica gel columns to improve yield.
Used for preparing libraries of DNA derived from chromatin immunoprecipitation experiments by the Iyer lab, and distributed to several other labs across Central Texas. Documented by Amelia Weber Hall, December 2012, March 2015, and June 2017.
This protocol should work well with any DNA that needs to be prepared into a library for high-throughput sequencing, however it is highly optimized for small (nanogram to pictogram) quantities of DNA.
2017-06-21T00:00:00ZAssessing sample size and mobility limits for a two-level multiple membership random effects model
http://hdl.handle.net/2152/47309
Assessing sample size and mobility limits for a two-level multiple membership random effects model
Wheelis, Meaghann Marie
The purpose of the present study was to examine the minimal sample sizes and mobility rates needed for accurate estimation with the multiple-membership random effects model (MM-REM), and the conditions in which the MM-REM provided improved estimation over a traditional multilevel model (MLM). The mobility rate, level one and level two sample size, and the ICC for the level one predictor were manipulated for both a traditional MLM and an MM-REM. Relative parameter bias, relative standard error bias, and credible interval coverage were evaluated across 36 conditions for both methods. Standard error estimates of the intercept were negatively biased and credible interval coverage was low for both methods, and estimation improved as the level one sample size increased and as the ICC for the level one predictor decreased. Additionally, for MLM estimates, standard error bias decreased and credible interval coverage improved as the mobility rate decreased. Negative relative parameter bias was found for estimates of the level two coefficient, which was found to increase as the mobility rate increased for both methods. The level two variance component was overestimated with the MM-REM and underestimated with the MLM, and credible interval coverage was low for both methods. Estimation improved for MM-REM estimates as the level two sample size increased, and as the mobility rate decreased for MLM estimates. The results from the study suggest that, if applied researchers are interested primarily in estimates of the regression coefficients associated with predictors, both the MLM and the MM-REM provide accurate estimates of the level one coefficient, and accurate credible intervals for estimates of the level two coefficient. When applied researchers are interested in the variance components, however, the MM-REM should be used over the MLM when mobility exceeds 10% and the level two sample size is 40 or greater. The results of the study for each condition, in addition to study limitations and recommendations for applied researchers, are discussed.
2017-02-07T00:00:00ZThe study of renewable content in retail electricity portfolios and the effect on consumers’ choices
http://hdl.handle.net/2152/47308
The study of renewable content in retail electricity portfolios and the effect on consumers’ choices
Li, Tianyi
This report focuses on the renewable plans in retail electricity markets. The integration of renewable energy into power grid enable renewables to play an increasingly important role in the electricity market. Consumers’ preference toward renewable energy plans and non-renewable energy plans is discussed by using both homogeneous consumer model and heterogeneous consumer model. The result shows that consumers prefer non-renewable energy plans than renewable energy plans. By misleading the consumers, retailers can increase the revenue from selling two plans with different proportion of renewable energy. The default effect during the procession of consumers’ decision making can also increase the retailers’ revenue.
2017-05-05T00:00:00Z