Traffic conflicts associated to signalized intersections are one of the major contributing factors to crash occurrences. Driver behavior plays an important role in the safety concerns related to signalized intersections. In this research effort, dynamics of driver behavior in relation to the traffic conflicts occurring at the onset of yellow is investigated. The area ahead of intersections in which drivers encounter a dilemma to pass through or stop when the yellow light commences is called Dilemma Zone (DZ). Several DZ-protection algorithms and advance signal settings have been developed to accommodate the DZ-related safety concerns. The focus of this study is on drivers' decision dynamics, human learning, and choice behavior in DZ, and DZ-related safety measures. First, influential factors to drivers' decision in DZ were determined using a driver behavior survey. This information was applied to design an adaptive experiment in a driving simulator study. Scenarios in the experimental design are aimed at capturing drivers learning process while experiencing safe and unsafe signal settings. The result of the experiment revealed that drivers do learn from some of their experience. However, this learning process led into a higher level of risk aversion behavior. Therefore, DZ-protection algorithms, independent of their approach, should not have any concerns regarding drivers learning effect on their protection procedure. Next, the possibility of predicting drivers' decision in different time frames using different datasets was examined. The results showed a promising prediction model if the data collection period is assumed 3 seconds after yellow. The prediction model serves advance signal protection algorithms to make more intelligent decisions. In the next step, a novel Surrogate Safety Number (SSN) was introduced based on the concept of time to collision. This measure is applicable to evaluate different DZ-protection algorithms regardless of their embedded methodology, and it has the potential to be used in developing new DZ-protection algorithms. Last, an agent-based human learning model was developed integrating machine learning and human learning techniques. An abstracted model of human memory and cognitive structure was used to model agent's behavior and learning. The model was applied to DZ decision making process, and agents were trained using the driver simulator data. The human learning model resulted in lower and faster-merging errors in mimicking drivers' behavior comparing to a pure machine learning technique. / Ph. D.

We study the unique phenomena which occur in certain systems characterized by the crossing or avoided crossing of two electronic eigenvalues. First, an example problem will be investigated for a given Hamiltonian resulting in a codimension 1 crossing by implementing results by Hagedorn from 1994. Then we perturb the Hamiltonian to study the system for the corresponding avoided crossing by implementing results by Hagedorn and Joye from 1998. The results from these demonstrate the behavior which occurs at a codimension 1 crossing and avoided crossing and illustrates the differences. These solutions may also be used in further studies with Herman-Kluk propagation and more. Secondly, we study codimension 2 crossings by considering a more general type of wave packet. We focus on the case of Schrödinger equation but our methods are general enough to be adapted to other systems with the geometric conditions therein. The motivation comes from the construction of surface hopping algorithms giving an approximation of the solution of a system of Schrödinger equations coupled by a potential admitting a conical intersection, in the spirit of Herman-Kluk approximation (in close relation with frozen/thawed approximations). Our main Theorem gives explicit transition formulas for the profiles when passing through a conical crossing point, including precise computation of the transformation of the phase and its proof is based on a normal form approach. / Doctor of Philosophy / We study energies of molecular systems in which special circumstances occur. In particular, when these energies intersect, or come close to intersecting. These phenomena give rise to unique physics which allows special reactions to occur and are thus of interest to study. We study one example of a more specific type of energy level crossing and avoided crossing, and then consider another type of crossing in a more general setting. We find solutions for these systems to draw our results from.

Among the most critical elements at signalized intersections are the design of vehicle detection equipment and the timing of change and clearance intervals. Improperly timed clearance intervals or improperly placed detection equipment can potentially place drivers in a Type I dilemma zone, where approaching motorists can neither proceed through the intersection before opposing traffic is released nor safely stop in advance of the stop bar. Type II dilemma zones are not necessarily tied to failures in design, but are more readily tied to difficulties in driver decision making associated with comprehension and behavior. The Type II dilemma zone issues become even more prevalent at high-speed intersections where there is greater potential for serious crashes and more variability in vehicle operating speeds. This research initiative attempts to further describe the impact of driver behavior and comprehension on dilemma zones. To address this notion several experiments are proposed. First, a large empirical observation of high-speed signalized intersections is undertaken at 10 intersection approaches in Vermont. This resulted in the collection of video and speed data as well as full intersection inventories and signal timings. These observations are reduced and analyzed for the purpose of reexamining the boundaries of a Type II dilemma zone. Second, a comparison of point and space sensors for the purpose of dilemma zone mitigation was conducted. This experiment provides evidence supporting the notion that space sensors have the potential for providing superior dilemma zone protection. Third, a computer based survey is conducted to identify if drivers comprehend the correct meaning of the solid yellow indication and how this relates to their predicted behavior. Lastly, a regression model is developed drawing on the data collected from the field observation as well as the static survey to determine how characteristics such as the speed and position of the vehicle as well as driver age and experience influence driver behavior in the Type II dilemma zone. Cumulatively, these experiments will shed additional light on the influence of driver behavior and comprehension on the Type II dilemma zone.

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<p>The goal of this dissertation is to broaden the classes of ideals for which the generators of residual intersections are known. This is split into two main parts.</p> <p>The first part is Chapter 5, where we prove that, for an ideal I in a local Cohen-Macaulay ring R, under suitable technical assumptions, we are able to express s-residual intersections, for s ≥ μ(I) − 2, in terms of (μ(I) − 2)-residual intersections. This result implies that s- residual intersections can be expressed in terms of links, if μ(I) ≤ ht(I) + 3 and some other hypotheses are satisfied. In Chapter 5, we prove our result using two different methods and two different sets of technical assumptions on the depth conditions satisfied by the ideal I. For Section 5.2 and Section 5.3 we use the properties of Fitting ideals and methods developed in [33] to prove our main result. In these sections, we require I to satisfy the Gs condition and be weakly (s − 2)-residually S2. In Section 5.4, we prove analogous results to those in Section 5.2 and Section 5.3 using disguised residual intersections, a notion developed by Bouca and Hassansadeh in [5].</p> <p>The second part is Chapter 6 where we prove that the n-residual intersections of ideals generated by maximal minors of a 2 × n generic matrix for n ≥ 4 are sums of links. To prove this, we require a series of technical results. We begin by proving the main theorem for this chapter in a special case, using the results of Section 6.1 to compute the generators of the relevant links in a our special case, and then using these generators to compute the Gro ̈bner Basis for the sum of links in Section 6.2. The computation of the Gro ̈bner basis, as well as an application of graph theoretic results about binomial edge ideals [17], allow us to show that our main theorem holds in this special case. Lastly, we conclude our proof in Section 6.3, where we show that n-residual intersections of ideals generated by maximal minors of 2 × n generic matrices commute with specialization maps, and use this to show that the generic n-residual intersections of ideals generated by maximal minors of a 2 × n generic matrix for n ≥ 4 are sums of links. This allows us to prove the main theorem of Chapter 6.</p>

As autonomous vehicles begin to move towards full autonomy, the controllers and software within them are becoming incredibly more complex to deal with any plausible scenario. Automotive manufacturers must balance the need for safety with the customers' desire for performance and features. A robust set of tools is a necessity to develop vehicle control protocols and navigation strategies. Vehicle to everything communication protocols and path planning are two aspects of autonomous vehicles that need a large amount of development effort. The MathWorks has put a great amount of effort in developing a robust simulation tool for autonomous vehicles. However, it currently lacks a method to develop V2X communication and path routing. In this thesis, I developed an extension for the Mathworks Simulink autonomous driving toolbox to incorporate graph-based path planning and vehicle to vehicle communication. The navigation system models each road using standard civil engineering techniques, to calculate the intersection points and bounding areas for regions of interest. Based on these regions, a directed graph is created to aid in calculating the shortest path. The navigation system also provides a redundant method for path planning for poorly marked areas and intersections. The vehicle to vehicle communication system emulates the 802.11p protocol and deals with practical challenges such as latency to provide developers with a realistic environment in which to develop vehicle communication protocols. The final result is a simulation where multiple vehicles drive safely and efficiently throughout a city network, sending messages at regions of interest and follow computed paths to their desired destinations. / Thesis / Master of Applied Science (MASc) / Vehicle to Everything communication protocols and path planning are two aspects of autonomous vehicles that need a robust framework to aid in their development. I developed an extension for the Mathworks Simulink autonomous driving toolbox to incorporate graph-based path planning and vehicle to vehicle communication. The navigation system models each road using standard civil engineering techniques, to calculate the intersection points and bounding areas for regions of interest. Based on these regions, a directed graph is created to aid in calculating the shortest path. The navigation system also provides a redundant method for path planning for poorly marked areas and intersections. The vehicle to vehicle communication system emulates the 802.11p protocol and realistic effects such as latency to provide developers with a realistic environment to develop vehicle communication protocols. The final result is a simulation where multiple vehicles drive throughout a city network, sending messages at regions of interest and follow a computed path to their desired destination.

Although concepts of the XDL intersection or CFI (Continuous Flow Intersection) have been around for approximately four decades, users do not yet have a simplified procedure to evaluate its traffic performance and compare it with a conventional intersection. Several studies have shown qualitative and quantitative benefits of the XDL intersection without providing accessible tools for traffic engineers and planners to estimate average control delays, and queues. Modeling was conducted on typical geometries over a wide distribution of traffic flow conditions for three different design configurations or cases using VISSIM simulations with pre-timed signal settings. Some comparisons with similar conventional designs show considerable savings in average control delay, and average queue length and increase in intersection capacity. The statistical models provide an accessible tool for a practitioner to assess average delay and average queue length for three types of XDL intersections. Pre-timed signal controller settings are provided for each of the five intersections of the XDL network. In this research, a "real-time" traffic signal control strategy is developed using genetic algorithms and neural networks to provide near-optimal traffic performance for XDL intersections. Knowing the traffic arrival pattern at an intersection in advance, it is possible to come up with the best signal control strategy for the respective scenario. Hypothetical cases of traffic arrival patterns are generated and genetic algorithms are used to come up with near-optimal signal control strategy for the respective cases. The neural network controller is then trained and tested using pairs of hypothetical traffic scenarios and corresponding signal control strategies. The developed neural network controller produces near-optimal traffic signal control strategy in "real-time" for all varieties of traffic arrival patterns. / Master of Science

Crash data suggests that intersections are areas producing conflicts among the various road users because of entering and crossing movements. Traffic signal control systems may not always be sufficient in preventing collisions at intersections between emergency and other vehicles. The Firefighter Fatality Retrospective Study of 2002 illustrates that the second leading cause of fatal injury for firefighters is vehicle collisions. Furthermore, the involvement of an emergency vehicle in a crash can negatively affect the overall efficiency of emergency response services. Thus, there is a need to facilitate the implementation of higher-payoff strategies to improve the safety of emergency vehicle passage through signalized intersections. This research aims to provide a basis for the transportation professionals to identify problem areas and take measures that will potentially enhance intersection safety for emergency vehicles. It includes the presentation and comparison of the EV crash situation in Northern Virginia. The results indicate that 49% of all EV accidents along U.S. Highways in Northern Virginia occurred at signalized intersections. This percentage is 75% along U.S. Highways in Fairfax County, the largest county in Northern Virginia, and it is 79% along U.S. 1 in Fairfax County. The analysis, also, illustrates that the major collision type at signalized intersections was of the angle type, which suggests that an appropriate warning sign may be absent. These findings enhance our understanding of emergency vehicle crash characteristics and thus, may facilitate the identification of possible warrants to be used in determining the appropriateness of installing signal preemption equipment at signalized intersections. / Master of Science

Crash prediction models are used to estimate the number of crashes using a set of explanatory variables. The highway safety community has used modeling techniques to predict vehicle-to-vehicle crashes for decades. Specifically, generalized linear models (GLMs) are commonly used because they can model non-linear count data such as motor vehicle crashes. Regression models such as the Poisson, Zero-inflated Poisson (ZIP), and the Negative Binomial are commonly used to model crashes. Until recently very little research has been conducted on crash prediction modeling for pedestrian-motor vehicle crashes. This thesis considers several candidate crash prediction models using a variety of explanatory variables and regression functions. The goal of this thesis is to develop a pedestrian crash prediction model to contribute to the field of pedestrian safety prediction research. Additionally, the thesis contributes to the work done by the Federal Highway Administration to estimate pedestrian exposure in urban areas. The results of the crash prediction analyses indicate the pedestrian-vehicle crash model is similar to models from previous work. An analysis of two pedestrian volume estimation methods indicates that using a scaling technique will produce volume estimates highly correlated to observed volumes. The ratio of crash and exposure estimates gives a crash rate estimation that is useful for traffic engineers and transportation policy makers to evaluate pedestrian safety at signalized intersections in an urban environment. / Master of Science

Consecutive traffic signals produce vehicle stops and acceleration/deceleration maneuvers on arterial roads, which may increase vehicle fuel consumption levels significantly. Eco-cooperative adaptive cruise control (Eco-CACC) systems can improve vehicle energy efficiency using connected vehicle (CV) technology. In this thesis, an Eco-CACC system is proposed to compute a fuel-optimized vehicle trajectory while traversing multiple signalized intersections. The proposed system utilizes signal phasing and timing (SPaT) information together with real-time vehicle dynamics data to compute the optimal acceleration/deceleration levels and cruise speeds for connected-technology-equipped vehicles while approaching and leaving signalized intersections, while considering vehicle queues upstream of the intersections. The INTEGRATION microscopic traffic simulation software was used to conduct a comprehensive sensitivity analysis of a set of variables, including different levels of CV market penetration rates (MPRs), demand levels, phase splits, offsets, and distances between intersections to assess the benefits of the proposed algorithm. Based on the analysis, fuel consumption saving increase with an increase in MPRs and a decrease in the cycle length. At a 100% equipped-vehicle MPR, the fuel consumption is reduced by as much as 13.8% relative to the base no Eco-CACC control. The results demonstrate an existence of optimal values for demand levels and the distance between intersections to reach the maximum fuel consumption reduction. Moreover, if the offset is near the optimal values for that specific approach, the benefits from the algorithm are reduced. The algorithm is limited to under-saturated conditions, so the algorithm should be enhanced to deal with over-saturated conditions. / Master of Science