Traumatic brain injuries TBIs) are injuries to the brain associated with the transfer of energy from some external source. There are an estimated 1.4 million TBIs each year, and about half are due to transportation crashes NINDS, 2007). Driver distraction is defined as a process or condition that draws a drivers attention away from driving activities toward a competing activity Sheridan, 2004) and has been identified as an under-examined issue for TBI populations Cyr, et al., 2008). The interaction between the cognitive impairments related to TBIs and the competing demands from driver distraction may be especially problematic. The goal of this dissertation is to investigate the effect of driver distraction on individuals with TBI. This dissertation uses several approaches and data sources: crash data, a TBI registry, a survey of TBI drivers, and an on-road driving study of TBI and non-TBI drivers. Results demonstrate that a subset of TBI drivers are more willing to engage in distracting tasks and they are more likely to have received speeding tickets. TBI drivers involved in crashes were less likely to wear seatbelts and were more likely to be involved in multiple crashes compared to all other drivers in crashes. Additionally, a subset of TBI drivers exhibits more risk-taking while driving that may result from the TBI or a predisposition to take risks. A Bayesian approach was used to analyze the effect of distracting tasks on driving performance of TBI drivers in an on-road study. A simulator study of non-TBI drivers was used to develop prior distributions of parameter estimates. The distracting tasks include a CD selecting task, a coin sorting task, and a radio tuning task. All of the tasks contained visual-manual components and the coin sorting task contained an additional cognitive component associated with counting the currency. This suggests that TBI drivers exhibited worse driving performance during a coin sorting task than the non-TBI drivers in terms of the standard deviation of speed and maximum lateral acceleration of the vehicle. This suggests that the cognitive component of the coin sorting task may be causing the decreased performance for the TBI drivers. Across all tasks, TBI drivers spent a larger percent of the task duration looking at the task with a larger number of glances towards the distraction task than the non-TBI drivers. Driver distractions with cognitive components may be especially problematic for TBI drivers. Future work should investigate if this effect is consistent across more complex cognitive driver distraction tasks e.g., cell phone usage) for this population. Additionally, future work should validate the high proportion of TBI drivers involved in multiple crashes.

This study describes analytical model as one innovative way to simulate Light Rail Transit (LRT) operations and calculate vehicular, transit and person delays at LRT crossings through Microsoft Excel. Analytical model emulates LRT trajectories from field and use these trajectories to clearly define train and car phases through Visual Basic for Applications (VBA) logic, which is part of analytical model. Simulation of train trajectories and calculations of delays were done for different LRT strategies and estimated roadway condition, Testing and validation of analytical model were performed in one case study in Salt Lake City (UT). Results show that analytical model is capable of emulating LRT trajectories and estimating delay at isolated LRT crossing. However, analytical model is not capable of simulating different train strategies at two or more LRT crossings, at the same time. Finally, extracted strategy provides savings from $100.000 to $200.000 in study area, on annual basis for projected year.

This project focuses on the development, validation, and sensitivity analysis of finite element models of high-tension cable barrier systems. The literature review details the available cable barrier systems, performance reviews of high-tension systems, and the previous development of a low-tension cable barrier system. An overview of the finite element models created for this project based on a weaved high-tension cable barrier system and a parallel high-tension cable barrier system is provided. This is followed by the comparison of the created models with five previously conducted crash tests for verification and validation purposes. The effects of anchor spacing, number of cables, and tension on each system are then analyzed. For both systems, the peak deflection increases as the anchor spacing is increased until an upper asymptote for the deflection is reached. Reducing the initial cable tension also results in a greater peak deflection. Increasing the number of pretension cables also results in a greater peak deflection. The performance of the weaved and parallel systems with variation in the aforementioned variables is also compared. In all cases the weaved system provided a lower peak deflection. Finally, a recommendation for future areas of study is put forth.

Researchers have proposed that distributed energy storage devices could be used to perform ancillary services for the electric grid. This work focuses on vehicle-to-grid and battery-to-grid distributed energy storage devices. In conceptual studies, distributed energy storage devices were shown to be able to accrue revenue for performing these grid stabilization services, and these revenues were used to show that the use of vehicle-to-grid and battery-to-grid can help to offset the initial increased capital cost of electric vehicles. These conceptual studies have assumed a command architecture that allows for a direct and deterministic communication between the grid system operator and the distributed energy storage devices. The first part of this thesis compares this direct, deterministic command architecture to an aggregative command architecture on the basis of the availability, reliability and value of the vehicle-to-grid provided ancillary services. This research incorporates a new level of detail into the modeling of vehicle-to-grid ancillary services by incorporating probabilistic vehicle travel models, time series ancillary services pricing, a consideration of ancillary services reliability. Results show that including an aggregating entity in the command and contracting architecture can improve the scale and reliability of vehicle-to-grid ancillary services, thereby making vehicle-to-grid ancillary services more compatible with the current ancillary services market. However, the aggregative architecture has the deleterious effect of reducing the revenue accrued by plug-in vehicle owners relative to the default architectures. The second part of this work investigates the effects of introducing battery state of charge and time series generation control signals. Results show that in order to integrate a vehicle-to-grid system into the existing markets and power grid the distributed energy storage system will require: 1) an aggregative architecture to meet current industry reliability standards, 2) the construction of low net energy automatic generation control signals, 3) a lower percent call for distributive energy storage systems even if the pool of contracted ancillary service resources gets smaller, 4) a consideration of vehicle performance degradation due to the potential loss of electrically driven miles, and 5) the incorporation of power-to-energy ratios. The third part of this work adapts the vehicle-to-grid model to a battery-to-grid system. Results show that if the automatic generation control signals contain low energy content, battery-to-grid has higher revenue potential than vehicle-to-grid due not having to account for vehicle driving behavior. Additionally, the third portion of this work proposed and performed high level analyses of operational options for battery-to-grid systems receiving automatic generation control signals with high energy content.

Recently, the development of non-pneumatic tires (NPT) such as the Michelin Tweel is receiving increased attention due to potential advantages over pneumatic tires such as low mass, no run flat, good contact pressure distribution, and low rolling resistance (RR). This study focuses on the design of a NPT based on properties of vertical stiffness, contact pressure, and rolling energy loss. Using a finite element (FE) model, a parametric study is conducted to study the effect on vertical stiffness, contact pressure, and rolling resistance (RR) response considering three design variables: (1) thickness of the spokes, (2) the shear band thickness, and (3) shear modulus of the shear band and spokes of the NPT. The first two design variables are geometric parameters of the NPT while the third design variable is a material parameter. Using the three design variables, a design of experiments (DOE) is performed to study the effect on RR, contact pressure, and vertical displacement. Results from the DOE are used to create response surface models (RSM) for the objective function (minimal RR) and constraints on vertical deflection and contact pressure. The analytical RSM function is optimized for minimizing the rolling loss subjected to the given constraints. In addition, a design sensitivity study is performed to evaluate the influence of the design variables on the output response. Results indicate that all the design variables have significant effect on RR, with the shear band thickness and shear modulus having the greater effect.

This dissertation optimizes the problem of designing sector boundaries and assigning air traffic controllers to sectors while considering demand variation over time. For long-term planning purposes, an optimization problem of clean-sheet sectorization is defined to generate a set of sector boundaries that accommodates traffic variation across the planning horizon while minimizing staffing. The resulting boundaries should best accommodate traffic over space and time and be the most efficient in terms of controller shifts. Two integer program formulations are proposed to address the defined problem, and their equivalency is proven. The performance of both formulations is examined with randomly generated numerical examples. Then, a real-world application confirms that the proposed model can save 10%–16% controller-hours, depending on the degree of demand variation over time, in comparison with the sectorization model with a strategy that does not take demand variation into account. Due to the size of realistic sectorization problems, a heuristic based on mathematical programming is developed for a large-scale neighborhood search and implemented in a parallel computing framework in order to obtain quality solutions within time limits. The impact of neighborhood definition and initial solution on heuristic performance has been examined. Numerical results show that the heuristic and the proposed neighborhood selection schemes can find significant improvements beyond the best solutions that are found exclusively from the Mixed Integer Program solvers global search. For operational purposes, under given sector boundaries, an optimization model is proposed to create an operational plan for dynamically combining or splitting sectors and determining controller staffing. In particular, the relation between traffic condition and the staffing decisions is no longer treated as a deterministic, step-wise function but a probabilistic, nonlinear one. Ordinal regression analysis is applied to estimate a set of sector-specific models for predicting sector staffing decisions. The statistical results are then incorporated into the proposed sector combination model. With realistic traffic and staffing data, the proposed model demonstrates the potential saving in controller staffing achievable by optimizing the combination schemes, depending on how freely sectors can combine and split. To address concerns about workload increases resulting from frequent changes of sector combinations, the proposed model is then expanded to a time-dependent one by including a minimum duration of a sector combination scheme. Numerical examples suggest there is a strong tradeoff between combination stability and controller staffing.

Pilots are subject to varying levels of stress, workload, and fatigue during long flights. During different phases of a commercial flight, pilots are engaged in multiple tasks which include going through checklists, checking conditions at their destination, communicating with Air Traffic Control and dealing with other flight related tasks. The amount of work varies from the earlier stages until the end of the flight. It is not well understood how changes in the amount of workload can affect a pilot’s ability to engage with important tasks that relate to safety of flight. The work shown in this thesis focused on the level of engagement displayed by flight crew as a function of level of workload. The principal hypothesis was that very low levels of workload may lead to crew disengagement and sub-optimal levels of performance. The degree to which pilots remain alert and are fatigued during a commercial flight is also not established in a concrete way.

This dissertation focuses on the use of passenger conveyance systems and modeling passenger flow in airport terminals. The successfully designed airport concourse must perform at a level that meets the needs of its users—the passengers. In this research, we propose a database design methodology that allows key conveyance statistics to be analyzed within specific locations across the airport terminal. Using passenger conveyance observations collected at five North American airports, the database enables airport planners, operators and consultants to assess passenger behavior and conveyance device performance. Results from this section of the research were in direct support of the Airport Cooperative Research Program ACRP). In both vertical and horizontal mode choice analysis, two logistic models are developed to serve as predictors to examine the relationship between passenger characteristics and their choice of conveyance system and analyze the probabilities of a passenger choosing different conveyance devices in airport terminals. Our analyses through logistic models show that passengers tend not to use moving walkway with increasing number of rollers. It is important for airport planners to provide an appropriate level of service LOS) for airport passengers. To estimate potential congestion and meet service-level requirements in a concourse, we develop a series of simulation models to estimate the occupancy of any designated area or footprint) within a concourse. Specifically, factors such as the number of gates, flight arrivals, aircraft size and gate configuration are considered in simulation models. We identify significant factors that affect the congestion and establish a service level design standard matrix in the footprint area. We also introduce zones inside the concourse and examine how various diversions concessions, restaurants, etc.) within the concourse and the capacity of departure lounge in each gate affect passenger congestion in each zone. Finally, we combine the database and mode choice models into two comprehensive concourse simulation models: 1) concourse with moving walkway 2) concourse with vertical transition devices escalator, elevator and stairs). We use these models to estimate passenger occupancy and the resulting LOS. This research provides an understanding into how various concourse operation strategies affect when and how passenger congestion forms within the terminal.

Traffic congestion is a national issue in the United States and has gotten worse in regions of all sizes. Now, more and more intersections are operated in oversaturated situations where the traffic demand exceeds the capacity of the system. Although a significant amount of literature has been devoted to how to manage oversaturated traffic signal systems, our understanding of the characteristics of oversaturation remains limited, particularly with regard to identification of oversaturation and the transition process from under-saturated condition to oversaturation. It has become increasingly obvious that successful traffic management requires efficient methods to identify and model oversaturated conditions. This research moves towards a better understanding of oversaturation, by 1) providing coherent methodologies to quantify oversaturation and 2) developing a simplified model to describe oversaturation at signalized intersections based on high-resolution traffic signal data collected by the SMART-SIGNAL Systematic Monitoring of Arterial Road Traffic Signals) system. In particular, the research focuses on the following four areas: 1) Quantification of oversaturation: Traditional definitions of oversaturation are not applicable for existing detection systems. This research circumvents this issue by quantifying the detrimental effects of oversaturation on signal operations, both temporally and spatially. In the temporal dimension, the detrimental effect is characterized by a residual queue at the end of a cycle, which occupies a portion of green time in the next cycle. In the spatial dimension, the detrimental effect is characterized by a downstream spillover, which blocks the traffic and reduces usable green time. From these observations, we derive two types of an oversaturation severity index OSI): one temporally-based T-OSI) and one spatially-based S-OSI). Both T-OSI and S-OSI are designed to yield a ratio between the unusable green time due to detrimental effects and the total available green time in a cycle, using high resolution traffic signal data. T-OSI is quantified by estimating the residual queue length； and S-OSI is quantified by measuring the time period of spillover. Since different types of OSI T-OSI or S-OSI) point to different underlying causes of oversaturation, this research has the potential to provide guidance for the mitigation strategies of signal oversaturation. 2) Real-time queue length estimation for congested intersections: To quantify T-OSI, this research proposes a novel shockwave-based algorithm to estimate time-dependent queue length even when the signal links are congested with long queues, a situation that the traditional input-output approach for queue length estimation cannot handle. Using high-resolution “event-based” traffic signal data, the new algorithm first identifies traffic state changes； and then applies Lighthill-Whitham-Richards LWR) shockwave theory to estimate maximum and minimum i.e. residual) queue length. This algorithm is also applicable for other aspects of arterial performance such as travel time, delay, and level of service. 3) Queue-Over-Detector QOD): To quantify S-OSI, we study a phenomenon we call Queue-Over-Detector QOD). QOD occurs when a vehicle stops and rests on a detector for a period of time creating a large occupancy value. This research demonstrates that a main cause of QOD is spillover from downstream intersections. Thus QOD identification can be used to quantify oversaturation in the spatial dimension, i.e. S-OSI. This research also briefly studies the relationship between QOD and the cycle-based arterial fundamental diagram AFD) by microscopically investigating individual vehicle trajectories derived from event-based data. Results show that proper treatment of QOD results in a stable form of the AFD which clearly identifies three different regimes, under-saturation, saturation, and over-saturation with queue spillovers. Achieving a stable form of the AFD is of great importance for traffic signal control because of its ability to identify traffic states on a signal link. 4). Traffic flow modeling for oversaturated arterials: The culmination of this research project is a simplified traffic flow model for congested arterial networks, which we call the shockwave profile model SPM). Unlike conventional macroscopic models, in which space is often discretized into small cells for numerical solution, SPM treats each homogeneous road segment with constant capacity as a section； then categorizes the traffic within each section simply as free-flow, saturated, or jammed. Traffic dynamics are analytically described by tracing the shockwave fronts which explicitly separate these three traffic states. SPM is particularly suitable for simulating traffic flow on congested signalized arterials, especially with queue spillover problems. In SPM, queue spillover can be treated as either extending a red light or creating new smaller cycles. Since only the essential features of arterial traffic flow, i.e., queue build-up and dissipation, are considered, SPM significantly simplifies arterial network design and improves numerical efficiency. For these reasons, we fully expect this model to be adopted in real-time applications such as arterial performance prediction and signal optimization.

Throughout the years, transportation organizations and agencies have been unable to keep up with increasing demand for roadway facilities. Similarly, traditional public-sector funding such as motor fuel taxes is falling short in meeting the growing demand for new transportation infrastructure. With deficit financing and congestion problems common to many highways throughout the United States, DOTs are turning to tolling the roadway facilities as a means of financing transportation improvements for inter-urban and urban facilities. In turn, in order for managed facilities such as toll roads or managed lanes to be attractive and viable for the potential investors, the facilities must be predicted to generate sufficient revenue to cover the costs and also provide reasonable rates of return for debt servicing. This requires accurate revenue forecasting, which itself largely is based on an accurate traffic demand forecast. Therefore, the performance and reliability of models that forecast traffic demand for toll roads are critical, and the likelihood that forecasted revenue matches the actual revenue is solely based on the performance and reliability of these travel demand models. The purpose of this research is to evaluate the application of a pseudo-probabilistic route assignment method within a travel demand forecast model in order to forecast the diversion rate for a proposed tolled facility. This will result in an estimation of the future traffic of the tolled facility and its share of the total corridor demand. In addition, throughout this study, effort has been made to explore the existing toll road travel demand forecasting methods and address the technical modeling issues that affect the performance of such methods.