61 research outputs found
Random parameter analysis of IIHS vehicle death rate factors and their contributions to fixed object and non-domestic collision severity
No full textWe study the severity of driver crashes involving fixed object and non-domestic collisions on the SR5, SR82, SR90, SR182, SR205, SR405 and SR705 in the state of Washington using a data set of 10871 for the years 2018 until 2019. We use a multinomial logit model to identify statistically relevant factors explaining the severity of the most severe injury type, which is classified into the four classes, which are non-apparent injury, possible injury, suspected minor injury and suspected serious injury plus fatality, respectively. Furthermore, to account for unobserved heterogeneity we use a mixed logit model with heterogeneity in variance.
We study the effect of a number of factors including time period, sobriety type, vehicle count, work-zone information, first collision type, junction relationship, weather conditions, pavement surface conditions, ambient light conditions, first impact location, vehicle movement information, vehicle style, vehicle size, first vehicle action, vehicle defects conditions, vehicle 1 demographics, driver 1 contributing causes, site-type indicators, impairment & fault dummies and count, encroachment indicators, driver age level indicators, wildlife indicator, posted speed limits, presence of traffic control systems, age and gender of the driver and county locations of the crash.
The objective of this study was to determine the contributing factors to vehicle driver crash severity involving fixed objects collisions. The results from this study need to be evaluated with caution due to the lack of data about specified driver behaviors and driver skills at the moment of crash related cases available in the WSDOT crash database. Implications for identifying and improving the reporting of unobserved driver behaviors, driver skills and other related factors are therefore discussed
Improving the seismic performance of a cold-formed and hot-rolled steel wall system equipped with a novel curved steel-composite dampers
No full textThe enhancement of seismic performance in mid-rise structures often involves the utilization of hybrid structural systems. Among these, the combination of cold-formed steel (CFS) and hot-rolled steel (HRS) wall systems has been proposed to achieve the desired seismic performance level by effectively controlling lateral forces. This study focuses on the numerical analysis of a novel bracing system equipped with curved steel-composite dampers within a hybrid wall panel (HWP). The objective is to improve energy dissipation, stiffness, and frame strength during displacement control cyclic loading protocol. The investigation evaluated seismic performance of an HWP equipped with the novel curved steel dampers oriented at six different angles (30°, 45°, 55°, 60°, 65°, and 75°), with two different thicknesses (10mm and 13mm) and three different depths (30mm, 40mm, and 50mm). The aim is to determine the most efficient damper geometry to enhance the seismic performance of the HWP frame. The results show that the energy dissipation, frame strength, and elastic stiffness are improved significantly when using (a) a 75° damper with 10 mm thickness and 40 mm depth or (b) a 55° damper with 13 mm thickness and 40 mm depth at the top of the hot-rolled section. In the second phase of the study, a novel chevron bracing system equipped with proposed curved steel dampers is designed and introduced to improve the seismic performance of the HWP frame by increasing the energy dissipation and frame strength of HWP as well as improving the buckling areas in hot-rolled section compared with using the proposed curved steel dampers solely on the hot-rolled section of the HWP during the cyclic loading protocol. The same damper configuration for angle, thickness, and depth has been used to increase the accuracy of the comparison, as well as the same cyclic loading protocol to achieve the most accurate results. The results indicate significant improvements in energy dissipation, frame strength, and buckling areas of the HWP frame when using the novel bracing system equipped with a 75° damper featuring a thickness of 13mm and a depth of 30mm compared with HWP without dampers and HWP equipped with proposed curved steel dampers. In the third phase of the study, a novel approach was introduced by designing curved composite dampers. This involved the use of three different composite patterns by using Aluminum, Gray Cast Iron, and Copper as composite materials combined with steel and three varying core thicknesses for composite combinations. Improved configurations for curved composite dampers were developed for both HWP equipped with the chevron bracing system and curved steel-composite dampers, as well as HWP equipped solely with curved dampers at the top of the hot-rolled section. The results demonstrated that employing curved steel-composite dampers had direct and positive impacts on improving the seismic performance of the HWP for both bracing systems when compared to the same bracing method with curved steel dampers and novel composite combinations were introduced for each bracing system, and curved dampers.Embargo status: Restricted until 06/2174. To request the author grant access, click on the PDF link to the left
Unobserved heterogeneity in ramp crashes: Insights from nonlinear random parameter and random parameter with heterogeneity in means and variance approaches
No full textThe dissertation aims to investigate the heterogenous effects of factors on crash frequency & severity on ramps while considering unobserved heterogeneity through advanced models. The dissertation comprises of two parts. The first section investigated the heterogeneous effects of ramp type, configuration, spatial footprints, traffic, and geometric characteristics on ramp crash frequency, while the second part showed a new method to capture nonlinear relationship of ramp crash severity and contributing factors involved in crash considering unobserved heterogeneity. The first part of the dissertation presents a negative binomial random parameter model with heterogeneity in means and variance to capture the effect of heterogeneous effect of ramp type, alignment, truck volume and interchange geometry and on freeway ramp crash frequency. Two years (2018–2019) of crash data on freeway ramps in Washington State were analyzed. Model estimation results show ramp type (directional, semi-directional and loop), alignment, and traffic characteristics significantly impact ramp crash frequency. The northwest loop ramp indicator has a random parameter. The minimum horizontal curve radius and the total number of vertical curves on the ramp appear to be statistically significant sources of heterogeneity in the mean of this parameter. Heterogeneity in the mean of the random effect is influenced by single truck percentage and the low AADT indicator (<=1,340 vehicles per day). Heterogeneity in the variance of the northwest loop ramp random parameter appears to be associated with the southwest loop ramp indicator indicating unobserved effects due to same-side loop geometries. Directional ramp indicators (on- and off-ramps) and interactions involving speed limit, AADT and horizontal curve radius are statistically significant (as fixed parameters) in their impact on ramp crash frequency. Total centerline mile footprint of all ramps at the interchange is a continuous fixed parameter effect. Ramp-specific lengths (longer than 0.335 miles) also appear to be statistically significant. The findings in this study suggest that ramp and interchange design need to account for a holistic integration of spatial footprint, type of ramp and alignment factors, in addition to traffic flow variables. The second part of the dissertation explores the true nonlinear relationship between contributing factors such as driver behavior and vehicle-related factors and injury severity on different ramp segments while considering unobserved heterogeneity. Nonlinear and linear (standard) random parameter multinomial logit models with heterogeneity in means were estimated using three years of crash data (2018-2020) on freeway ramps in Washington State for the gore-to-gore, merge-diverge, and combined ramp segments. Model estimation results show the nonlinear models are superior to the linear models of all ramp segments based on log-likelihood, AIC, BIC, and model-predicted versus observed crashes by injury severity. Nonlinearity and heterogeneous effects are captured in the random parameter of the total number of vehicles involved in a crash under the possible injury utility. The sources of heterogeneity in the mean of random parameter estimates differ by ramp segment. Heterogeneity in the mean of the random effect is influenced by driver age of 70 or higher, sideswipe collision type, and distracted driver in the gore-to-gore ramp segment, by following too closely in the merge-diverge segment, and by speeding vehicles in the combined ramp segments. In addition, the significant variables with fixed parameter effects vary in the different ramp segments. Indicator variables of not granting a right of way, the interaction of foggy weather and wet road conditions, and the interaction of vertical and horizontal curves were only found statistically significant in the merge-diverge ramp segments due to additional workloads on drivers such as left and right turns, and interactions with traffic in other direction. While indicators of speeding vehicles, and same-direction vehicle movement prior to the crash, which reflects wrong-way driving in ramps, were only found statistically significant in the gore-to-gore ramp segments. These signify the necessity of investigating the traffic safety of ramps by segments. The integrated approach of nonlinearity and unobserved heterogeneity consideration provides detailed insight into the complex interaction of the total number of vehicles. This finding contributes to the knowledge of multiple-vehicle crashes, which are recorded to be substantially higher than single-vehicle crashes in the United States
Spatiotemporal Changes of PM10 and PM2.5 Over Two Decades Across the Greater Phoenix Area
No full textUnderstanding the spatial and temporal patterns of airborne particulate matter (PM) in urban areas is critical for characterizing air quality and how it impacts human health. In this study, we examine hourly concentrations of PM10 and PM2.5 (particles with aerodynamic diameter of up to 10 μm and 2.5 μm, respectively) measured over two decades (2000-2021) from 22 EPA AQMS sites throughout the greater Phoenix area in Arizona to analyze spatial and temporal changes including yearly, monthly, weekly, and diurnal trends. High PM10 concentrations were most prevalent during late summer months, however several sites close to industrial regions reported high PM10 concentrations during November. High PM2.5 concentrations were observed during winter months. Similar PM concentrations were observed across each day of the week. All sites observed a bimodal diurnal distribution with peaks in the morning and late evening. A slight decreasing trend was observed by most stations for both PM10 and PM2.5 across the two decade period, mainly from sites located in industrial areas. Most of the days (>90%) examined in this study had either good or moderate air quality levels based on daily PM concentrations. Observations based on World Health Organization (WHO) daily thresholds showed ~56% of days examined were above the PM10 threshold, and ~22% were above the PM2.5 threshold. But only ~2% of daily values were above the Environmental Protection Agency (EPA) daily PM10 threshold, while ~1% were above the EPA daily PM2.5 threshold. However, many of the days below the EPA daily thresholds still had high hourly concentrations with PM10 >100 μg m-3 and PM2.5 >50 μg m-3. Many of these days exceeding either EPA or WHO thresholds were associated with dust events. However, dust events did not account for all days with high PM concentrations, indicating that air pollution in the region is also anthropogenically driven
Replication Data for: Ice Nucleation by Marine Aerosols over the North Atlantic Ocean
No full textIce nucleation data reported in "Ice Nucleation by Marine Aerosols over the North Atlantic Ocean"
by Elise K. Wilbourn, Daniel C. O. Thornton, Catherine Ott, Jason Graff, Patricia K. Quinn, Timothy S. Bates, Raghu Betha, Lynn M. Russell, Michael J. Behrenfeld, and Sarah D. Brook
Preparation and characterization of a multivariate dataset for statistical analysis of fixed object and non-domestic animal collisions in Washington state
No full textResearchers have attempted to identify unobserved causes of fixed Object and non-domestic animal (NDA) collisions that lead to injuries and fatalities. Improper identification of the variables associated with these collisions often leads to biased conclusions on safety interventions, misleading policies and failed road mitigation implementation projects. In many instances, the reason for inaccurate conclusions is the lack of accounting of unobserved heterogeneity. Unobserved heterogeneity can occur due to incomplete data on vehicle involvement in crashes, human factors, environmental factors and infrastructure condition factors. This thesis uses crash data from the Washington State Department of Transportation (WSDOT) to analyze the fixed object-NDA collisions. I will describe the process of fusing WSDOT crash data with other sources of data on vehicle style and size in order to produce a usable dataset for statistical analysis of crash severity. In particular, I will discuss attrition of data and the impact of data attrition on resulting sample sizes and data usability from the standpoint of meaningful variables for the analysis of crash severity. Data attrition can occur when the analysis of unobserved heterogeneity is undertaken for severity prediction. To understand unobserved heterogeneity and its effects on crash severity, the usable crash data dimensions are likely to become high, i.e., rectangular with numerous columns of variables. This is because unobserved heterogeneity can arise from multiple sources, such as human factors, environmental factors, roadway factors, driving environment factors, and vehicle factors. Not all pertinent factors can be captured for comprehensive analysis of crash severity. However, it is advisable to identify as many factors as one can so that omitted variable biases and resulting heterogeneity can be minimized. This can help isolate true unobserved heterogeneity in crash severity analysis. Unobserved heterogeneity results in parameter effects varying across the crash involved driving population, which is of interest to severity modelers, design policy makers, and decision makers who prioritize safety investment decisions.
To begin a proper analysis of unobserved heterogeneity effects, a descriptive analysis of the usable dataset is necessary. A descriptive analysis involves the consideration of sample size, the skewness of the distribution, the range of the variable value and the category of the variable (human factors versus other factors for example). Past research has indicated that indicator variables (variables with value of 1 or 0) tend to be influential in crash severity analysis. This is because in crash severity analysis, the probability of a severity type (no apparent injury, possible injury, evident injury, severe injury and fatality) is the outcome being estimated, given that a crash has occurred. Therefore, measurements are specific to the crash. When variables specific to a crash are being used in statistical analysis, their presence or absence influences the outcome. Therefore, the nature of the majority of crash specific variables is binary. A minority of variables can be non-binary, such as the number of occupants involved in the crash, number of vehicles involved in a crash, vehicle speed (as in posted speed limit in the neighborhood of the crash), driver age, etc.
Collision severity levels relating to the vehicular style, and size are used from data gathered from the Insurance Institute for Highway Safety (IIHS) website. This information was used to calculate the driver overall death rate average relating to the vehicle categories, the death rate mean, maximum, minimum, and standard deviation would be calculated. Similarly, the death rate mean, maximum, minimum, and standard deviation would be calculated for vehicle style.
The goal of this thesis is to analyze the WSDOT data in relationship with the IIHS to determine the statistically significant variables which could emerge as unobserved heterogeneity variables in crashes and level of severity which can also be analyzed further using applicable statistical models. In this thesis I explore the viability of binary and non-binary variables in terms of their effective sample sizes for usability statistical analysis. In the process of making this assessment, attrition issues are discussed. An original contribution of this thesis is therefore the fused outcome of multiple types of data – IIHS death rate data by vehicle style and size, and crash specific data obtained from WSDOT crash datasets for fixed object and NDA collisions. To the best of the author’s knowledge, the fusion of IIHS data for use in crash severity analysis and the assessment of unobserved heterogeneity has not occurred based on my review of the published literature. The published literature fails to recognize that IIHS vehicle ratings are not comparable across vehicle classes. In contrast, IIHS death rates are comparable because they are observed death rates in real crashes. I demonstrate the utility of IIHS death rates in the analysis of unobserved heterogeneity in severity modeling using a sample latent class model and compare the model predictions with those of the conventional multinomial logit model.Embargo status: Restricted to TTU community only. To view, login with your eRaider (top right). Others may request access exception by clicking on the PDF link to the left
Comprehensive evaluation of the hydrodynamics of a nature-based wastewater treatment solution: the Pond-in-Pond (PIP)
No full textWaste stabilization ponds (WSP) are recommended to treat wastewater in small communities. The benefits include lower operational and maintenance costs, less energy consumption, and the potential for agricultural reuse. Previous research provides evidence that ponds have poor hydrodynamic efficiency, but novel designs help overcome this issue. The proposed design, Pond-in-Pond, combines an anaerobic pit surrounded by berms into an outer pond. To better understand ponds' hydrodynamic behavior, computational fluid dynamics (CFD) simulations are commonly used, as they provide results that help identify flow patterns and calculate hydraulic efficiency. In addition to CFD, two methods were explored: the compartment model (CM), which uses idealized reactors to explain hydrodynamics in rectors; and the dispersed plug flow model (DPFM), an analytical solution to the advection-diffusion equation (ADE) for an instantaneous impulse tracer. The goal with these alternative methods is to provide a simpler approach to CFD simulations while having advantages such as kinetic modeling using the CM and a geometry pre-optimization tool using the DPFM. When analyzing the PIP, previous research suggested that the anaerobic pit plays an important role in solids retention. Therefore, several pit configurations were analyzed using CFD simulations, transport models and solids settling assessments. These simulations provided evidence that a square anaerobic pit would promote the best hydrodynamics for the PIP and settle particles as small as 50 μm, such as Hookworms. To address sustainability with the produced effluent, two post-treatment solutions were analyzed, using a constructed wetland for stream discharge and a land application system to reuse the effluent for irrigating alfalfa crops. Besides the technical aspects, the financial impact was evaluated, and the land application had a better return on investment. Moreover, this solution increases soil fertility with the treated effluent, generates revenue for the municipality, and promotes sustainable water management, especially in arid regions
A statistical analysis of limit equilibrium factors of safety for slope stability analysis
No full textThe purpose of this thesis is to evaluate the relationship between the LEM factors of safety and the factor of safety obtained from the Fellenius method (hereafter referred to as the Ordinary method). To do this evaluation, I generate a synthetic dataset with varying soil properties, using four baseline soil properties such as cohesion, friction angle, slope angle, and density. Using the synthetic dataset, LEM FOS is calculated, in addition to the Ordinary FOS. A total of 200 synthetic soil observations are generated.
By using the synthetically generated LEM FOS and the Ordinary FOS, I establish a statistical relationship between LEM FOS and Ordinary FOS. The relationship is in the form of a regression model where adjustment factors due to cohesion, friction, density, and slope are estimated
Error component models for traffic crash severity analysis
No full textIn the realm of crash severity modeling, the challenge of accommodating unobserved heterogeneity, particularly crucial for uncovering true effects of contributing factors is a major issue. This dissertation aims to investigate the applicability of error components mixed logit model for modeling crash severity while considering the unobserved heterogeneity, looking at present context and exploring how this model may be able to capture the evolving paradigms in traffic safety, with a focus on fixed object crashes. The dissertation comprises of four parts. The first section provides a general overview of the research associated with the use of artificial intelligence and machine learning with respect to transportation research, uncovering the underlying trends and potential research gaps through the usage of topic modeling framework, specifically latent dirichlet allocation. Object detection, materials strength, and traffic forecasting emerge as active research areas. A focused examination of traffic safety related research reveals collision avoidance, computer vision, and incident management as prominent research domains. The second part of the dissertation focuses on the heterogeneity models used to uncover insights into fixed object crash severity. This section introduces an error components mixed logit model with variability in means and variance to address the diverse impacts of contributing factors on fixed object occupant severity in crashes. This model is an extension to the popular mixed logit framework used for crash data analysis, improving upon previous models by also including error components variance which is further able to uncover some of the unobserved heterogeneity present in crash data. The third part of the dissertation focuses on reviewing research efforts related to fixed object safety analysis and explore emerging fixed object safety landscapes in relation to autonomous systems, electrification of fleets, and climate change. The fourth part builds on the third part and explores how mixed logit models can capture the potential effects generated by automation, electrification and climate change on crash modeling, introducing some potentially relevant contributing factors due to these evolving paradigms
PHYSICAL, CHEMICAL AND TOXICOLOGICAL CHARACTERISTICS OF PARTICULATE EMISSIONS FROM STATIONARY ENGINE FUELED WITH ULTRA LOW SULFUR DIESEL AND WASTE COOKING OIL DERIVED BIODIESEL
No full textPh.DDOCTOR OF PHILOSOPH
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