In the United States, speeding is considered to be a contributing factor in about 30 percent of fatal crashes (US DOT, 2000). In an attempt to reduce speeds on roadway segments where speed is considered to be a safety concern, various low cost countermeasures have been investigated. Such countermeasures include pavement markings that give a psychological appearance of narrowing and/or increasing speed have been considered as a relatively low-cost treatment. Perceptual cues are one potential method of influencing motorists to slow down, and ultimately, to save lives. These perceptual techniques might be useful at lowering speeds in a variety of driving situations such as work zones, curves, roundabouts, and toll plazas. Evaluations are required in order to determine the effectiveness of these various treatments at reducing speeds. This research project explored several possible perceptual countermeasures to try on the approaches to curves for reducing speeds. It was ultimately decided to evaluate the effects of peripheral transverse lines in reducing speeds. Although there have been some limited evaluations of peripheral transverse markings in previous studies, no significant field evaluation has been performed and a recommended design for the markings has not been discussed. The projected results of the research effort is to determine pavement marking treatments with a high probability of success at reducing speeds, develop and design peripheral transverse markings based on site considerations, determine the effectiveness of the markings in the field, determine optimal pavement marking design using a driving simulator, and use a controlled research environment to finalize the design. This dissertation contributes to the body of knowledge on speed reduction research through the development of low cost speed reduction strategies, the design of peripheral transverse lines for varying geometric conditions, evaluation of these treatments in the field, in the simulator, and on a controlled roadway, and to finally compare the benefits of each of the evaluation approaches. In the field, peripheral transverse lines spaced at a frequency of 4 bars per second were evaluated in New York, Mississippi, and Texas. The markings were applied on approaches to curves in both rural and urban environments on both multi-lane and two-lane roadways. The authors concluded that overall, the pavement markings reduced speeds up to 59% compared to the baseline in the short term and 24% in the long term on overall vehicle speeds. When evaluating design alternatives of peripheral transverse markings, a follow-up study was performed and compared baseline conditions to markings spaced at a constant interval, exponentially closer, at two bars per second, and at four bars per second. The peripheral transverse lines were effective in reducing centerline encroachment; however, the results were inconclusive as to which particular marking spacing pattern was most effective. There was a large amount of variability in driving speeds using the driving simulator which made it ineffective at comparing designs. The third evaluation was performed at the Virginia Tech Smart Road in which reductions in speed were compared to the baseline at two locations. While one curve had large preview distances and no effect due to the treatments, speed reductions on a freeway ramp type of curve resulted in a speed reduction 42% greater than the reduction in the baseline condition. There are several advantages and disadvantages to evaluations in the field, simulator, and at a controlled research setting which are summarized in this dissertation. Overall, all three have potential of looking at different elements, but it was determined that variability when measuring speed in the driving simulator makes it more challenging as a tool for measuring speed reductions. / Ph. D.

Vehicle recognition and trajectory tracking plays an integral role in many aspects of Intelligent Transportation Systems (ITS) applications; from behavioral modeling and car-following analyses to congestion prevention, crash prediction, dynamic signal timing, and active traffic management. This dissertation aims to improve the tasks of multi-object detection and tracking (MOT) as it pertains to urban traffic by utilizing the domain knowledge of traffic flow then utilize this improvement for applications in real-time traffic performance assessment, safety evaluation, and driver behavior modeling. First, the author proposes an ad-hoc framework for real-time turn count and trajectory reconstruction for vehicles passing through urban intersections. This framework introduces the concept of virtual traffic lanes representing the eight standard National Electrical Manufacturers Association (NEMA) movements within an intersection as spatio-temporal clusters utilized for movement classification and vehicle re-identification. The proposed framework runs as an additional layer to any multi-object tracker with minimal additional computation. The results obtained for a case study and on the AI City benchmark dataset indicate the high ability of the proposed framework in obtaining reliable turn count, speed estimates, and efficiently resolving the vehicle identity switches which occur within the intersection due to detection errors and occlusion. The author then proposes the utilization of the high accuracy and granularity trajectories obtained from video inference to develop a real-time safety-based driver behavior model, which managed to effectively capture the observed driving behavior in the site of study. Finally, the developed model was implemented as an external driver model in VISSIM and managed to reproduce the observed behavior and safety conflicts in simulation, providing an effective decision-support tool to identify appropriate safety interventions that would mitigate those conflicts. The work presented in this dissertation provides an efficient end-to-end framework and blueprint for trajectory extraction from road-side traffic video data, driver behavior modeling, and their applications for real-time traffic performance and safety assessment, as well as improved modeling of safety interventions via microscopic simulation. / Doctor of Philosophy / Traffic crashes are one of the leading causes of death in the world, averaging over 3,000 deaths per day according to the World Health Organization. In the United States alone, there are around 40,000 traffic fatalities annually. Approximately, 21.5% of all traffic fatalities occur due to intersection-related crashes. Intelligent Transportation Systems (ITS) is a field of traffic engineering that aims to transform traffic systems to make safer, more coordinated, and 'smarter' use of transport networks. Vehicle recognition and trajectory tracking, the process of identifying a specific vehicle's movement through time and space, plays an integral role in many aspects of ITS applications; from understanding how people drive and modeling that behavior, to congestion prevention, on-board crash avoidance systems, adaptive signal timing, and active traffic management. This dissertation aims to bridge the gaps in the application of ITS, computer vision, and traffic flow theory and create tools that will aid in evaluating and proactively addressing traffic safety concerns at urban intersections. The author presents an efficient, real-time framework for extracting reliable vehicle trajectories from roadside cameras, then proposes a safety-based driving behavior model that succeeds in capturing the observed driving behavior. This work is concluded by implementing this model in simulation software to replicate the existing safety concerns for an area of study, allowing practitioners to accurately model the existing safety conflicts and evaluate the different operation and safety interventions that would best mitigate them to proactively prevent crashes.

The Safety Triad proposed by Geller (1992) suggests that interventions to increase safety in the community and workplace needs to consider three causal aspects of behavior change. 1) The Person factor considers the past history of an individual as well as specific personality characteristics which may influence responsiveness to an intervention. 2) The Environmental factor considers the manipulation of the environmental antecedents 2lnd consequences of the target behavior. It also includes identifying natural contingencies which may support the behavior after the intervention is withdrawn. 3) The Behavior factor considers the response class in which the target behavior is shaped, and the interrelationships between the target behavior and other behaviors. / Ph. D.

The importance of traffic safety has prompted considerable research on predicting driver risk and evaluating the impact of risk factors. Driver risk modeling is challenging due to the rarity of motor vehicle crashes and heterogeneity in individual driver risk. Statistical modeling and analysis of such driver data are often associated with Big Data, considerable noise, and lacking informative predictors. This dissertation aims to develop several systematic techniques for traffic safety modeling, including finite sample bias correction, decision-adjusted modeling, and effective risk factor construction. Poisson and negative binomial regression models are primary statistical analysis tools for traffic safety evaluation. The regression parameter estimation could suffer from the finite sample bias when the event frequency (e.g., the total number of crashes) is low, which is commonly observed in safety research. Through comprehensive simulation and two case studies, it is found that bias adjustment can provide more accurate estimation when evaluating the impacts of crash risk factors. I also propose a decision-adjusted approach to construct an optimal kinematic-based driver risk prediction model. Decision-adjusted modeling fills the gap between conventional modeling methods and the decision-making perspective, i.e., on how the estimated model will be used. The key of the proposed method is to enable a decision-oriented objective function to properly adjust model estimation by selecting the optimal threshold for kinematic signatures and other model parameters. The decision-adjusted driver-risk prediction framework can outperform a general model selection rule such as the area under the curve (AUC), especially when predicting a small percentage of high-risk drivers. For the third part, I develop a Multi-stratum Iterative Central Composite Design (miCCD) approach to effectively search for the optimal solution of any "black box" function in high dimensional space. Here the "black box" means that the specific formulation of the objective function is unknown or is complicated. The miCCD approach has two major parts: a multi-start scheme and local optimization. The multi-start scheme finds multiple adequate points to start with using space-filling designs (e.g. Latin hypercube sampling). For each adequate starting point, iterative CCD converges to the local optimum. The miCCD is able to determine the optimal threshold of the kinematic signature as a function of the driving speed. / Doctor of Philosophy / When riding in a vehicle, it is common to have personal judgement about whether the driver is safe or risky. The drivers’ behavior may affect your opinion, for example, you may think a driver who frequently hard brakes during one trip is a risky driver, or perhaps a driver who almost took a turn too tightly may be deemed unsafe, but you do not know how much riskier these drivers are compared to an experienced driver. The goal of this dissertation is to show that it is possible to quantify driver risk using data and statistical methods. Risk quantification is not an easy task as crashes are rare and random events. The wildest driver may have no crashes involved in his/her driving history. The rareness and randomness of crash occurrence pose great challenges for driver risk modeling. The second chapter of this dissertation deals with the rare-event issue and provides more accurate estimation. Hard braking, rapid starts, and sharp turns are signs of risky driving behavior. How often these signals occur in a driver’s day-to-day driving reflects their driving habits, which is helpful in modeling driver risk. What magnitude of deceleration would be counted as a hard brake? How hard of a corner would be useful in predicting high-risk drivers? The third and fourth chapter of this dissertation attempt to find the optimal threshold and quantify how much these signals contribute to the assessment of the driver risk. In Chapter 3, I propose to choose the threshold based on the specific application scenario. In Chapter 4, I consider the threshold under different speed limit conditions. The modeling and results of this dissertation will be beneficial for driver fleet safety management, insurance services, and driver education programs.

Where can state, local and federal government officials concentrate their resources in order to reduce the highway fatality rate? A highway fatality model was developed to determine which factor has the greatest positive or negative impact on the highway fatality rate. A cross-section of data from states for 1984 and 1985 was collected for the following variables: average speed, speed variance, percentage of drivers wearing seat belts, percentage of licensed male drivers, percentage of drivers under 25 years of age, drinking age for beer, per capita alcohol consumption, percentage of urban population, and percentage of urban roads. The highway fatality equation was estimated via an iterative approach using ordinary least squares. The variables testing significant include: average speed, speed variance, drinking age for beer, percentage of drivers under 25 years of age, and percentage of urban roads. When translating the results into a policy action, it was determined that keeping the speed limit at 55 MPH on rural interstates would prevent the greatest number of traffic fatalities. Other policy actions considered in order of their impact on highway fatalities include: more stringent enforcement of the 55 MPH speed limit, restricting teenage night-time driving, raising the driving age to 17 years of age, and raising the drinking age for beer to 21 in the seven remaining states. / M.A.

No description available.

No description available.

No description available.

This dissertation presents a research effort to evaluate wake vortex mitigation procedures and technologies in order to decrease aircraft separations, which could result in a runway capacity increase. Aircraft separation is a major obstacle to increasing the operational efficiency of the final approach segment and the runway. An aircraft in motion creates an invisible movement of air called wake turbulence, which has been shown to be dangerous to aircraft that encounter it. To avoid this danger, aircraft separations were developed in the 1970s, that allows time for wake to be dissipated and displaced from an aircraft's path. Though wake vortex separations have been revised, they remain overly conservative. This research identified 16 concepts and 3 sub-concepts for wake mitigation from the literature. The dissertation describes each concept along with its associated benefits and drawbacks. All concepts are grouped, based on common dependencies required for implementation, into four categories: airport fleet dependent, parallel runway dependent, single runway dependent, and aircraft or environmental condition dependent. Dynamic wake vortex mitigation was the concept chosen for further development because of its potential to provide capacity benefit in the near term and because it is initiated by air traffic control, not the pilot. Dynamic wake vortex mitigation discretizes current wake vortex aircraft groups by analyzing characteristics for each individual pair of leader and follower aircraft as well as the environment where the aircraft travel. This results in reduced aircraft separations from current static separation standards. Monte Carlo simulations that calculate the dynamic wake vortex separation required for a follower aircraft were performed by using the National Aeronautics and Space Administration (NASA) Aircraft Vortex Spacing System (AVOSS) Prediction Algorithm (APA) model, a semi-empirical wake vortex behavior model that predicts wake vortex decay as a function of atmospheric turbulence and stratification. Maximum circulation capacities were calculated based on the Federal Aviation Administration's (FAA) proposed wake recategorization phase II (RECAT II) 123 x 123 matrix of wake vortex separations. This research identified environmental turbulence and aircraft weight as the parameters with the greatest influence on wake vortex circulation strength. Wind has the greatest influence on wake vortex lateral behavior, and aircraft mass, environmental turbulence, and wind have the greatest influence on wake vortex vertical position. The research simulated RECAT II and RECAT III dynamic wake separations for Chicago O'Hare International (ORD), Denver International Airport (DEN) and LaGuardia Airport (LGA). The simulation accounted for real-world conditions of aircraft operations during arrival and departure: static and dynamic wake vortex separations, aircraft fleet mix, runway occupancy times, aircraft approach speeds, aircraft wake vortex circulation capacity, environmental conditions, and operational error buffers. Airport data considered for this analysis were based on Airport Surface Detection Equipment Model X (ASDE-X) data records at ORD during a 10-month period in the year 2016, a 3-month period at DEN, and a 4-month period at LGA. Results indicate that further reducing wake vortex separation distances from the FAA's proposed RECAT II static matrix, of 2 nm and less, shifts the operational bottleneck from the final approach segment to the runway. Consequently, given current values of aircraft runway occupancy time under some conditions, the airport runway becomes the limiting factor for inter-arrival separations. One of the major constraints of dynamic wake vortex separation at airports is its dependence on real-time or near-real-time data collection and broadcasting technologies. These technologies would need to measure and report temperature, environmental turbulence, wind speed, air humidity, air density, and aircraft weight, altitude, and speed. / PHD / An aircraft in motion creates an invisible movement of air called wake turbulence, which has been shown to be dangerous to aircraft that encounter it. To avoid this danger, aircraft separations were developed in the 1970s, that allows time for wake to be dissipated and displaced from an aircraft’s path. Though wake vortex separations have been revised, they remain overly conservative. The separation of aircraft approaching a runway is a major obstacle to increasing the operational efficiency of airports. This dissertation presents a research effort to decrease aircraft separations as they approach and depart the airport, which could result in a runway capacity increase. This research identified 16 concepts and 3 sub-concepts for wake mitigation from the literature. The dissertation describes each concept along with its associated benefits and drawbacks. Dynamic wake vortex mitigation was the concept chosen for further development because of its potential to provide capacity benefit in the near term and because it is controlled the by air traffic control, not the pilot. Dynamic wake vortex mitigation, analyzes the characteristics for each individual pair of leader and follower aircraft as well as the environment where the aircraft travel. This research identified environmental turbulence and aircraft weight as the parameters with the greatest influence on wake vortex circulation strength. The wind has the greatest influence on wake vortex lateral behavior, and aircraft mass, environmental turbulence, and wind have the greatest influence on wake vortex vertical position. The research simulated aircraft operations for Chicago O’Hare International Airport, Denver International Airport and LaGuardia Airport. The simulation accounted for real-world conditions of aircraft operations during arrival and departure: aircraft fleet mix, aircraft runway occupancy time, aircraft approach speeds, aircraft wake vortex circulation capacity, environmental conditions, and pilot-controller human error. Results indicate that further reducing aircraft separation distances from static aircraft separations, shifts the operational bottleneck from the airspace to the runway. Consequently, given current values of aircraft runway occupancy time, the airport runway becomes the limiting factor to increase capacity. One of the major constraints of dynamic wake vortex separation at airports is its dependence on real-time data collection and broadcasting technologies. These technologies would need to measure and report temperature, environmental turbulence, wind speed, air humidity, air density, and aircraft weight, altitude, and speed.

In the study, I explored how to achieve sustained road-traffic accidents reduction in Zimbabwe. Road traffic accidents are indiscriminant and each year hundreds of people lose their lives in road traffic accidents in Zimbabwe. A mixed methods research approach was used to conduct the study. The study was therefore done in two phases. Phase 1 was a quantitative survey using questionnaires, and phase 2 was a qualitative case study using semi-structured interviews. A sample of 500 road-users drawn at random from internet databases formed the respondents for the quantitative phase of the inquiry. A further 20 snowball selected participants, formed the qualitative inquiry group. The key findings from the study were that effective intervention planning, timely measurement, adequate resourcing, and inclusive organization development interventions were the key drivers of successful road safety programmes. Additionally, critical interventions for sustainable road traffic safety in Zimbabwe included; community consultation and involvement in road traffic safety strategy formulation and implementation, mainstreaming road traffic safety education in the schools’ curricula, behavioral changes, financial, and engineering interventions. / Business Management / M. Com. (Business Management)