The Effects of Inaccurate and Missing Highway-Rail Grade Crossing Inventory Data on Crash and Severity Model Estimation and Prediction

Highway-Rail Grade Crossings (HRGCs) present a significant safety risk to motorists, pedestrians, and train passengers as they are intersections where roads and railways intersect. Every year, HRGCs in the US experience a high number of crashes leading to injuries and fatalities. Estimations of crash and severity models for HRGCs provide insights into safety and mitigation of the risk posed by such incidents. The accuracy of these models plays a vital role in predicting future crashes at these crossings, enabling necessary safety measures to be taken proactively. In the United States, most of these models rely on the Federal Railroad Administration\u27s (FRA) HRGCs inventory database, which serves as the primary source of information for these models. However, errors or incomplete information in this database can significantly impact the accuracy of the estimated crash model parameters and subsequent crash predictions. This study examined the potential differences in expected number of crashes and severity obtained from the Federal Railroad Administration\u27s (FRA) 2020 Accident Prediction and Severity (APS) model when using two different input datasets for 560 HRGCs in Nebraska. The first dataset was the unaltered, original FRA HRGCs inventory dataset, while the second was a field-validated inventory dataset, specifically for those 560 HRGCs. The results showed statistically significant differences in the expected number of crashes and severity predictions using the two different input datasets. Furthermore, to understand how data inaccuracy impacts model estimation for crash frequency and severity prediction, two new zero-inflated negative binomial models for crash prediction and two ordered probit models for crash severity, were estimated based on the two datasets. The analysis revealed significant differences in estimated parameters’ coefficients values of the base and comparison models, and different crash-risk rankings were obtained based on the two datasets. The results emphasize the importance of obtaining accurate and complete inventory data when developing HRGCs crash and severity models to improve their precision and enhance their ability to predict and prevent crashes. Advisor: Aemal J. Khatta

Road Work Zone Safety: Investigating Injury Severity in Motor Vehicle Crashes Using Random Effects Multinomial Logit Model

Work zones serve the purpose of facilitating maintenance and rehabilitation activities on roadways. However, these areas can also present unforeseen conditions to drivers, including narrowed right-of-way, lane shifts, and traffic disruptions. These conditions frequently contribute to vehicular crashes within work zones, resulting in property damage, injuries, and even loss of life. This paper aims to highlight work zone related crash data insights and presents statistical estimates of significant determinants of injury severity by analyzing ten-year crash data (2008-2018) from Nebraska, USA. The examination of crash data helped in highlighting work zone attributes that are empirically associated with serious injury crashes and fatalities. Crash data analysis evaluated the relationship of injury severity in work zones with key crash variables such as time of crash, road classification, crash location, road surface conditions, weather conditions and road characteristics. The crash data revealed that 2016 had the highest (1326/11.28%) whereas 2011 had the lowest (739/6.3%) recorded work zone crashes. Also, most of work zone crashes were recorded in activity and transition areas. A standard multinomial logit model and random effects multinomial logit model was estimated and compared. The estimated model showed that higher crash injury severity was associated with highways and interstates, curved and steep road conditions, lane closure and intermittent type work zones, activity and termination areas in work zones, presence of workers, time of day and certain crash attributes. Identifying these key factors related to work zone crashes helped suggest several mitigation strategies to reduce the severity of such incidents. This research is exploratory in nature, and the findings are anticipated to contribute to future studies on work zone safety

Exploring Statistical and Machine Learning-Based Missing Data Imputation Methods to Improve Crash Frequency Prediction Models for Highway-Rail Grade Crossings

Highway-rail grade crossings (HRGCs) are critical spatial locations of transportation safety because crashes at HRGCs are often catastrophic, potentially causing several injuries and fatalities. Every year in the United States, a significant number of crashes occur at these crossings, prompting local and state organizations to engage in safety analysis and estimate crash frequency prediction models for resource allocation. These models provide valuable insights into safety and risk mitigation strategies for HRGCs. Furthermore, the estimation of these models is based on inventory details of HRGCs, and their quality is crucial for reliable crash predictions. However, many of these models exclude crossings with missing inventory details, which can adversely affect the precision of these models. In this study, a random sample of inventory details of 2000 HRGCs was taken from the Federal Railroad Administration’s HRGCs inventory database. Data filters were applied to retain only those crossings in the data that were at-grade, public and operational (N=1096). Missing values were imputed using various statistical and machine learning methods, including Mean, Median and Mode (MMM) imputation, Last Observation Carried Forward (LOCF) imputation, K-Nearest Neighbors (KNN) imputation, Expectation-Maximization (EM) imputation, Support Vector Machine (SVM) imputation, and Random Forest (RF) imputation. The results indicated that the crash frequency models based on machine learning imputation methods yielded better-fitted models (lower AIC and BIC values). The findings underscore the importance of obtaining complete inventory data through machine learning imputation methods when developing crash frequency models for HRGCs. This approach can substantially enhance the precision of these models, improving their predictive capabilities, and ultimately saving valuable human lives

Non-Destructive Testing of Fully Recycled Aggregate Concrete Bricks Prepared by Compression Casting Technique

This research study aims to investigate the quality and strength of fully recycled aggregate concrete (RAC) bricks made by using Compression Casting Technique (CCT). Standard size RAC bricks were manufactured using 70% recycled coarse and 30% recycled fine concrete aggregates with 15% cement content by weight of total aggregates. Three values of casting pressure (i.e., 25, 30 and 35 MPa) were studied. Recycled concrete aggregates (fine and coarse) required for this study were produced by crushing of tested concrete samples having compressive strength range of 3000 to 4000 psi. Both destructive and non-destructive testing were performed on RAC bricks. Among NDT, ultrasonic pulse velocity test was performed to assess the quality of RAC bricks and based on results, a correlation between compressive strength and UPV test values is proposed. In addition, Schmitt hammer test was also performed, and their values were compared with laboratory tested samples. Results have highlighted that Schmitt hammer and UPV test can be consider as convenient and reliable way to assess the strength and quality of RAC bricks in the field

Effect of Casting Pressure on the Properties of 100% Recycled Aggregate Concrete Pavers

The construction and demolition (C&D) waste is required to be recycled for sustainable development and to save natural resources. In this study, the influence of casting pressure on the properties of pavers made using Recycled Aggregate Concrete (RAC) was investigated. RAC mix having 60% fine and 40% coarse recyled aggregates was prepared using 20% cement (by weight of total aggregates). The same mix was used in making concrete pavers of size 200 mm x 100 mm x 60 mm by employing Compression Casting Technique (CCT). For comparison purposes, pavers of same size were also prepared using Natural Aggregate Concete (NAC) under the same casting pressure. Pavers were tested to determine their density, initial rate of water absorption, porosity and efflorescence. The tests were performed after 28 days of casting. The results showed that above mentioned properties of RAC pavers are improved with the increase of casting pressure; by increasing the casting pressure from 5 to 30 MPa, density, initial rate of water absorption and prosity of RAC was improved up to 2.01%, 83.6% and 51.7%, respectively. The results further indicated that compared to NAC pavers, RAC pavers exhibit inferior properties. No efflorescence was observed in RAC and NAC pavers

A NOVEL AUTOMATED MODEL GENERATION ALGORITHM FOR HIGH LEVEL FAULT MODELING OF ANALOG CIRCUITS

gh level modelling techniques have been used by researchers from few decades to increase fault simulation speed of analog circuits. However, due to manual model generation, the techniques are tedious and time consuming and unable to reduce analog testing time. To overcome manual modelling limitation, researchers adopt algorithmic support and start using automated model generation (AMG) methods to generate models for high level modelling of analog circuits. AMG models successfully perform HLFM but unfortunately fail to increase high level fault simulation (HLFS) speed compared to full SPICE-circuit simulations. The failure is mainly occurred due to the consumption of multiple models and computational overhead of model switching required capturing nonlinear effects

Analyzing Major Elements of Crash Injury Severity Involving Priority-I Detriments of Vision-Zero Plan

Road traffic crashes result in significant economic losses for individuals, their families, and entire nations. These losses stem from the expenses associated with injury treatment, as well as the productivity lost due to fatalities or disabilities caused by these injuries. The 2030 Agenda for Sustainable Development set an ambitious target of halving the global number of fatalities and injuries from road traffic crashes by 2020 and achieving zero deaths by 2030, commonly referred to as the \u27Target Zero Plan.\u27 The Target Zero Plan prioritizes traffic safety issues into three distinct levels. The three priority levels are determined based on the percentage of statewide traffic fatalities and serious injuries associated with each factor. This research primarily focuses on analyzing the first priority level that gives emphasis on young drivers, crashes at intersections, driving under the influence of alcohol, and over-speeding behaviors. Iowa Department of Transportation (IDOT) crash data from 2016 to 2020 was analyzed. Initially, we separated variables of interest from the raw crash data that were pertinent to Priority 1 of the Target Zero Plan. Afterwards, descriptive statistics were performed to identify any trends in crashes (2016-2020) involving young drivers, over speeding, DUI, and intersections. A Multinomial logit model was estimated to find the significant factors associated with higher levels of crash severity. The results obtained from model estimation highlighted that “Y” and “L” type of intersections, driving under the influence, over speeding trends, airbag deployment, road surface condition and distracted driving were significantly impacting crash injury severity. Recommendations are presented that may assist stakeholders in meeting the plan “Target Zero”

Microbicides for the Prevention of HPV, HIV-1, and HSV-2: Sexually Transmitted Viral Infections

Sexually transmitted diseases (STDs) can be transmitted through genital-genital, orogenital, or anogenital contacts and remain to be a public health concern worldwide. Approximately one million people around the world are believed to be newly infected with sexually transmitted infections (STIs) each day. Numerous causative agents including bacteria, viruses, protozoa, yeast, and fungi are responsible for STIs; however, viruses exhibit more serious risks, probabilities and outcomes of STDs than other organisms. The most lethal viral STIs are human immunodeficiency virus-1 (HIV), herpes simplex viruses 1 and 2 (HSV-1 and HSV-2), and human papillomavirus (HPV), which are responsible for major sexually transmitted viral infections including AIDS, herpes simplex, and genital warts, respectively. Despite the fact that several prevention strategies such as vaccination, abstinence from sex, limiting sex partners, the use of condoms and a range of therapeutic drugs have drastically reduced the risk of contracting STIs, these three infections continue to spread at an alarming rate. The high incidence and lack of effective vaccine, instigated scientists to look for alternate, cheap, and efficient strategies for controlling these deadly viruses. Microbicide are relatively new approach that may be helpful in preventing STIs transmission when applied inside the genitals before intercourse. Like other interventions, microbicides are used as prophylactic measures against STIs. Therefore, an excellent safety and efficacy profile analysis is mandatory before their approval for human use. Although no safe and efficacious microbicide is yet available, many candidates including nonoxynol-9, Savvy, cellulose sulfate, Carraguard, VivaGel, tenofovir gel, and PRO 2000 have shown promising in vitro activity and many more are under development. However, very few of them have moved to large-scale phase III trials. This chapter aims to provide a brief overview of various microbicides along with their mechanism of actions and recent updates on safety and effectiveness trials

Use of Non Fossil Fuel Resources, Electricity, Economic Growth and Carbon Dioxide Emission in Pakistan

The ARDL (autoregressive distributed lag model) approach is used to study the effects of non fossil fuel resources, economic growth per capita, and use of electricity on carbon dioxide emanations in Pakistan from 1971 to 2014. The outcomes of OLS regression propose that use of non  fossil fuel resources play an essential function in curbing carbon dioxide emanations because sign of coefficients is negative and significant, on  the contrary the economic growth and electricity consumption increase carbon dioxide emanations in the environment.  The findings of ARDL propose that all variables have no effect on the carbon dioxide emanations in the long-run. Nevertheless, the Granger Causality test suggests there is bidirectional causality between carbon dioxide emanations and economic growth per capita.  Similarly, there is unidirectional causality between electricity utilization and carbon dioxide emissions. According to OLS regression findings, this study concluding that uses of non fossil fuel resources mitigate carbon dioxide emissions in Pakistan very effectively and therefore government give preference to use of non fossil fuel resources